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Index

3D Visual Data Mining (3DVDM) System
10.5.2
abbreviation method
12.3.8
acceptance rate
3.3
empirical
11.4.1
acceptance-complement method
2.8.2
access specifier
13.2.5
accumulated proportion
6.2.1.1
adaptive mixtures
5.4
adaptive SPSA
6.3.3
adaptivity
11.5.1 | 11.5.1 | 11.5.2
invalid
11.5.1
add-with-carry
2.3.6
additive models
5.5 | 10.2.3
address spoofing
5.3
adjusted dependent variable
7.3.3
aesthetics
11.8
affine equivariance
9.3.1 | 9.3.5
affine transformation
9.2.1
AICc
1.1 | 1.3 | 1.3 | 1.3
aircraft design
6.3.3
Akaike's information criterion (AIC)
1.1 | 1.1 | 1.3 | 1.3 | 1.3 | 1.3 | 1.3 | 1.3 | 1.4 | 1.4 | 7.3.5 | 8.1.3.1 | 11.2.3 | 15.2.2.1
algebra
11.3
algebraic curve fitting
6.3.2
algebraic surface fitting
6.3.2
alias method
2.8.2
allowable splits
14.3.1
alterning expectation-conditional maximization (AECM) algorithm
5.4.3.1 | 5.5.3.1
Amdahl's law
8.2.2.1
anaglyphs
10.5 | 10.5.1
Analysis of Functional Neuroimages (AFNI)
4.5.1
analysis of variance
9.5
Andrews plot
10.3.9
annealed entropy
15.2.3
anomaly detection
5.1
antithetic variables
2.3.2.2
antithetic variates
3.2
aperiodic
3.2.1
applications
15.7
approximate distance
6.3.2.2
approximations
12.3.3
AR model
11.2.1 | 11.2.3 | 11.4.3
order
11.2.1
ArcView/XGobi
10.5.2
ArcView/XGobi/XploRe
10.6.1
arithmetic mean
9.1.3
artificial intelligence
6.4.1
asset returns
2.1 | 2.3
association rules
13.4.4
asymptotic bias
5.3.1
asymptotic distribution
6.3.3
asymptotic normality
9.3.2
asymptotic relative efficiency
9.1.1 | 12.1.2
asymptotic variance
5.3.2 | 9.1.2
asymptotically random
2.4.2
attack propagation
5.7
autocorrelation plots
3.3.2.1
autocorrelation time
3.2.2
automatic methods
2.8.1 | 2.8.8
auxiliary variables
3.5
averaged shifted histogram (ASH)
10.3.10 | 10.4.3 | 4.3.3
backscatter
5.3 | 5.5 | 5.5
bandwidth
Choosing | 5.2.1 | 5.2.2 | 5.3
bar chart
10.3.11
base class
13.4
base-line intensity
12.4
batch means
3.2.2
Bayes
optimal
15.2.1
theorem
11.2.3
Bayes factor
1.1 | 1.1 | 1.3 | 1.5 | 1.5 | 1.5 | 1.5 | 1.5 | 11.2.2 | 2.4.1
approximation
11.3.3 | 11.3.3
computation
11.2.3
Bayesian
hierarchical structures
11.4.2
software
11.6
Bayesian classifiers
13.4.3
Bayesian framework
5.1.1
Gibbs sampler
5.5.1 | 5.5.2 | 5.5.2 | 5.5.3.2
MAP estimate
5.5.1 | 5.5.2
MCMC
5.5.1 | 5.5.2
Bayesian inference
2.2.1.2.2 | 2.2.1.3 | 2.2.2 | 2.2.3.2.2 | 2.2.3.2.2 | 2.3.2 | 2.3.2 | 2.3.2.2 | 2.3.4 | 2.4.1 | 2.4.2.2
Bayesian information criterion (BIC)
1.1 | 1.1 | 1.3 | 1.3 | 1.3 | 1.3 | 1.3 | 1.3 | 1.4 | 1.5 | 7.3.5
Bayesian statistics
3.1 | 11.1
Beowulf class cluster
8.2.1.2
Bernoulli data
7.1
Bertillon, Alphonse
11.4.2
BFGS algorithm
2.3.3
bias
5.3.1 | 9.1.2 | 9.2.4 | 9.2.4 | 9.2.6 | 9.3.3 | 9.3.4
function
9.3.4
functional
9.3.3
bias estimation
5.4.1.3
bias-variance tradeoff
1.1 | 1.2 | 1.2 | 1.2 | 1.2.1 | 5.3
binary data
7.1
binary representation
6.4.2
binary response data
3.3.2
Binary Response Model
10.4
binary search
2.8.1
binary tree
14.1
binomial distribution
7.2.1 | 11.2.2
bioinformatics
5.5.3.1
bioprocess control
6.3.3
birth-and-death process
11.4.3
birthday spacings test
2.6
bisquare function
5.2.1
bivariate histogram
4.2.1
blind random search
6.2.2 | 6.2.2.1
block bootstrap
2.4.2 | 2.4.2 | 2.4.2 | 2.4.2 | 2.4.3 | 2.4.6 | 2.4.7
block design
4.3.1
blocks
3.1
blood oxygen level dependent (BOLD) contrast
4.2.3.3
Bonferonni correction
4.4.2.2
boosting
15.4.1
bootstrap
5.3.5 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.4 | 2.4 | 2.4 | 2.4 | 2.4 | 2.4.1 | 2.4.2 | 2.4.2 | 2.4.2 | 2.4.2 | 2.4.2 | 2.4.2 | 2.4.2 | 2.4.2 | 2.4.2 | 2.4.2 | 2.4.2 | 2.4.2 | 2.4.2 | 2.4.3 | 2.4.3 | 2.4.3 | 2.4.3 | 2.4.3 | 2.4.3 | 2.4.3 | 2.4.3 | 2.4.3 | 2.4.3 | 2.4.4 | 2.4.4 | 2.4.4 | 2.4.4 | 2.4.4 | 2.4.4 | 2.4.4 | 2.4.4 | 2.4.4 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 3.2 | 5.4.3 | 14.7
nonparametric
5.3.5
smooth
11.5.2
bootstrap for dependent data
2.1 | 2.1 | 2.4 | 2.4.7
Boston Housing data
12.3.5 | 12.3.5 | 12.3.8 | 12.3.8 | 12.3.8 | 12.3.8
boundary bias
5.2.1
bounded algebraic curve
6.3.2.5
boxplot
9.1.1
brain activation
4.3.1
Brain Voyager
4.5.1
breakdown
9.1.2 | 9.2.3 | 9.3.3
breakdown point
9.1.2 | 9.2.4 | 9.2.4 | 9.2.4 | 9.2.6 | 9.3.2 | 9.4.3 | 9.4.4
breakdown point of M-functional
9.3.3
Breslow-Peto method
12.3.1 | 12.3.1 | 12.3.2
bridge estimator
11.3.3
bridge sampling
11.3.3 | 11.3.3 | 11.3.3
brush-tour
10.3.5
brushing
10.3.2 | 12.3.5
brushing with hue and saturation
10.3.10
burn-in
3.3
candidate generating density
3.3
canonical link
7.2.2 | 7.3.4
cascade algorithm
7.4.1
Castillo-Hadi model
12.2
categorization
7.4
Cauchy distribution
9.2.3
CAVE Audio Visual Experience Automatic Virtual Environment (CAVE)
10.5.2
censored data
5.5.3 | 14.5
central limit theorem
9.1.3 | 11.3.1
characteristic polynomial
2.4.1 | 2.4.4 | 2.4.5 | 2.4.6
chi-squared-test
7.3.5
choice
brand
2.2 | 2.2.1.3
discrete
2.1 | 2.2
probabilities
2.2.1.2.1 | 2.2.1.3 | 2.2.3.2.2
transport mode
2.2
Cholesky decomposition
4.1.1
chromosome
6.4.1 | 6.4.2
class
13.2.3
class diagram
13.3
class hierarchy
13.4.1
class interface
13.2.5
classification
14.1 | 15.1
clustered data
7.5.7
clustering
13.4.2 | 2.4.2 | 2.4.2.2
duration
2.3.5
volatility
2.3
coefficient of determination
8.1.3.1
coefficient of variation
12.1.2 | 12.1.2
collision test
2.6 | 2.6
color
12.3.7
color model
10.3.8 | 10.5.1
combined generators
2.3.4 | 2.4.6
command streams
5.6
common cumulative mean function
12.4
common parameter
12.4.3
common random numbers
3.2
common trend parameter
12.4
complete-data
information matrix
5.3.5
likelihood
5.1.2 | 5.2.1
log likelihood
5.2.1 | 5.2.3 | 5.3.1 | 5.3.2 | 5.3.3 | 5.3.3 | 5.4.1.1 | 5.4.2.2
problem
5.1.2 | 5.3.1 | 5.3.3
sufficient statistics
5.4.2.2
completion
11.4.2
complexity
15.2.3
complexity parameter
14.2.2
composition method
2.8.7
COMPSTAT Conferences
1.2.2
computational effort
11.3.1
computational inference
1.1 | 1.3.1 | 1.3.2 | 1.3.2
computational statistics
1.1
conditional likelihood
12.3.3 | 12.4.2 | 12.4.4
conditional tests
2.3 | 2.3 | 2.3 | 2.3
Conditional VaR
1.1
conditioned choropleth maps (CCmaps)
10.6.3
confidence
13.5.1.4
confidence interval
2.1 | 2.1 | 2.1 | 2.1 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 5.4.2.1 | 9.1.2 | 9.2.5 | 9.3.2
confidence level
11.2.2
confidence regions
11.2.2
conjugate gradient method
4.3.5.3
consistency
15.2.2 | 15.2.2 | 15.2.2.2
uniform convergence
15.2.2.2
consistent
9.1.3
constraints
6.1.4 | 6.4.4
constructor
13.2.3.1
contingency table
7.5.5
continuum regression
8.1.7
ridge regression
8.1.7
contour shell
10.3.10
contour surface
10.3.10
control variables
2.3.2.2
convergence
5.2.1 | 5.2.3 | 5.2.3 | 5.2.5 | 5.4.2.1 | 5.4.2.1 | 5.4.2.2 | 5.4.3.1
monotonicity property
5.2.3 | 5.4.1 | 5.4.2.1 | 5.4.3.1
speeding up
5.4.3 | 5.4.3.2 | 5.4.3.2 | 5.4.3.2
convergence assessment
11.3.1
convergence theory
6.2.2.2 | 6.3.2
convolution method
2.8.7
coordinates
11.7
copula
Archimedean
1.4
elliptical
1.4
estimation
1.4
simulation
conditional distributions method
1.4
cost-complexity
14.2.2
count data
3.8.2 | 7.1
count model
2.4.2.2
counting process
12.4 | 12.4.2
covariance functional
9.3.4
covariate
9.4.1 | 12.3
covering numbers
15.2.3
Cox model
12.3 | 12.3.2
Cox's discrete model
12.3.3
critical sampling
Resolution
cross
11.3.1.1 | 11.3.3.1 | 11.10.1.1
cross validation
1.1 | 1.1 | 1.3 | 1.4 | 1.4 | 1.4 | 1.4 | 5.4.1.1 | 8.1.3.4 | 14.2.2
crossover
6.4.2 | 6.4.2
CrystalVision
10.4 | 10.4.2 | 10.4.2 | 10.4.2 | 10.5.2
cumulative logit model
7.5.4
cumulative mean function
12.4.2
curse of dimension
Rates | 5.5 | 11.3.2 | 11.4.1
cyclic menu
12.3.3
data augmentation
3.5 | 2.2.1.2.2 | 2.4.1 | 2.4.2.2
data mining (DM)
5.4.3.2 | 10.1
Data Viewer
10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3
data visualization
4.2.1
DataDesk
Web
dataflow
11.1.1
daughter node
14.2.1
DC shifts
4.3.3.1
Decision theory
11.2.1
decision trees
13.4.3 | 13.5.1.1 | 14.1
decomposition algorithm
7.4.1
defensive sampling
11.5.2
degrees of freedom
1.2 | 1.3 | 5.3.3
denial of service attack
5.1 | 5.3 | 5.5 | 5.5 | 5.5
density estimation
5.5.3 | 5.5.3.2
dependent data
2.1 | 2.1 | 2.4 | 2.4 | 2.4 | 2.4 | 2.4.2 | 2.4.3 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7
descriptive modeling
13.4.2
design matrix
5.2.2
design of experiments
3.1
destructor
13.2.3.1
deterministic annealing EM (DAEM) algorithm
5.3.4
deterministic simulation
3.1 | 3.5.1
Deutsche Aktienindex (DAX)
1.2.5 | 1.3.3
deviance
7.3.2
penalized
11.2.3
deviance information criterion (DIC)
11.2.3
Diamond Fast
10.4.1 | 10.4.1
differentiable
9.1.3 | 9.2.5
digamma function
12.1.2
dimension
high
11.2.1 | 11.5.1
matching
11.4.3
unbounded
11.2.1 | 11.2.3
unknown
11.4.3
dimension reduction
5.4.2.2 | 5.4.2.2 | 5.5
Dimension reduction methods of explanatory variables
6.4
Dirichlet distribution
2.4.2.2
discrepancy
2.2.3
discrete logistic model
12.3
discrete optimization
6.1.2 | 6.3.3
dispersion parameter
7.3.4
distributed memory
8.2.1
distribution
$ \alpha $-stable
1.2
$ \alpha $-stable
1.2
$ \alpha $-stable
characteristic function
1.2.1
$ \alpha $-stable
$ S$ parametrization
1.2.1
$ \alpha $-stable
$ S^0$ parametrization
1.2.1
$ \alpha $-stable
density function
1.2.2
$ \alpha $-stable
distribution function
1.2.2
$ \alpha $-stable
direct integration method
1.2.2
$ \alpha $-stable
STABLE program
1.2.2
$ \alpha $-stable
simulation
1.2.3
$ \alpha $-stable
Fama-Roll method
1.2.4.1
$ \alpha $-stable
method of moments
1.2.4.2
$ \alpha $-stable
regression method
1.2.4.2
$ \alpha $-stable
regression method
1.2.4.2
$ \alpha $-stable
maximum likelihood estimation
1.2.4.3
$ \alpha $-stable
STABLE program
1.2.4.3
binomial
7.2 | 11.2.2
Cauchy
1.2.1
folded $ t$
11.3.2
Gaussian
1.2.1
generalized hyperbolic
density function
1.3
maximum likelihood estimation
1.3.2.1 | 1.3.2.1
mean
1.3
simulation
1.3.1
variance
1.3
generalized inverse Gaussian (GIG)
1.3
simulation
1.3.1 | 1.3.1 | 1.3.1
hyperbolic
1.3 | 1.3
density function
1.3 | 1.3
inverse
1.3.3
inverse Gaussian (IG)
simulation
1.3.1
Lévy
1.2.1
Lévy stable
1.2
mixture
11.4.2 | 11.4.3
normal inverse Gaussian (NIG)
density function
1.3
simulation
1.3.1
tail estimates
1.3.2.1
tails
1.3
predictive
11.2.1
proposal
11.3.2
stable Paretian
1.2
$ t$
11.3.2
target
11.4
truncated stable (TLD)
1.2.6
characteristic function
1.2.6
dot plot
4.2.1
doubledecker plot
13.5.2.3.1
Dow Jones Industrial Average (DJIA)
1.1 | 1.1 | 1.1 | 1.1 | 1.1 | 1.2.5 | 1.3.3
downweighting outlying observations
9.2.3
dual lattice
2.3.2 | 2.4.3
dynamic duration model/analysis
2.1 | 2.3 | 2.3.5
E-step (Expectation step)
5.1.2 | 5.2.1 | 6.3.3.1
exponential family
5.2.1
factor analysis model
5.4.2.2
failure-time data
5.3.2
generalized linear mixed models (GLMM)
5.4.1.1
Monte Carlo
5.4.1 | 5.4.1.1
nonapplicability
5.3.3
normal mixtures
5.3.1
early binding
13.5.1
effective number of parameters
5.3.3
efficiency
9.2.3 | 9.2.6
efficiency of the sample mean
12.1.2
eigenvalues
4.4
inverse iterations
4.4.6
Jacobi method
4.4.2
LR method
4.4.5
power method
4.4.1
QR method
4.4.4
eigenvectors
4.4
electroencephalogram (EEG)
4.2.3.2
elitism
6.4.2 | 6.4.2
EM algorithm
11.4.2
extensions
5.1.3 | 5.4
EM algorithm,
extensions
5.4.3.2
EM mapping
5.2.2 | 5.2.4
embarrassingly parallel
8.2.2.3
empirical measure
9.1.3
encapsulation
13.2
encoding
6.4.2
entropy
2.6 | 14.2.1
equidissection
2.4.2
equidistribution
2.4.2 | 2.4.2
equivariance
9.2.1 | 9.3.1
ergodic chain
3.2.1
estimation vs. testing
11.2.3
estimator
harmonic mean
11.3.3
maximum a posteriori (MAP)
11.2.3
Euler's constant
12.1.2
evolution strategies
6.4.1
evolutionary computation
6.4.1
exact distance
6.3.2.3
excess kurtosis
2.3 | 2.3.4
expectation-conditional maximization (ECM) algorithm
5.4.2 | 5.5.2
multicycle ECM
5.4.2.1 | 5.4.2.1
expectation-conditional maximization either (ECME) algorithm
5.4.2.2 | 5.4.3.1 | 5.5.2
expected shortfall (ES)
1.1
expected tail loss (ETL)
1.1
EXPLOR4
10.4.2 | 10.4.2
exploratory data analysis (EDA)
10.1 | 13.1
exploratory spatial data analysis (ESDA)
10.6.1
ExplorN
10.3.6 | 10.4 | 10.4 | 10.4.2 | 10.4.2 | 10.4.2 | 10.4.2 | 10.4.2 | 10.4.2 | 10.4.2 | 10.4.2 | 10.4.2 | 10.5.2
exponential density function
12.1.2
exponential distribution
9.2.7 | 12.1.2 | 12.1.2
exponential family
5.2.1 | 5.2.1 | 5.3.2 | 5.3.2 | 7.1 | 7.2.1 | 7.3.1 | 11.2.1
sufficient statistics
5.2.1 | 5.4.2.2
Extensible Markup Language (XML)
11.2
extreme value distribution
2.2.3.1
factor analysis model
5.4.2.2
failure-time data
censored
5.1.3 | 5.3.2
exponential distribution
5.3.2
false discovery rate (FDR)
4.4.2.2
fat-shattering dimension
15.2.3
fault detection
6.3.3
feedforward network
13.5.1.2
filter
high-pass
Orthogonality.
quadrature mirror
Orthogonality.
final prediction error (FPE)
1.3
finite mixture
2.4.1 | 2.4.1
model
2.1 | 2.4 | 2.4.1 | 2.4.2.2
of Gaussian densities
2.3.2.3
finite-difference SA (FDSA)
6.3.2
Fisher consistent
9.1.2
Fisher information
12.4.2
generalized linear model (GLM)
7.3.5
Fisher scoring algorithm
7.3.3
fitness function
6.4.1 | 6.4.2
fitness proportionate selection
6.4.2
Fitts forecasting model
12.3.5
floating-point
6.4.2
focusing
10.3.3
font
12.3.7
fork-join
8.2.1.1
Fourier plot
11.9.2
Fourier space
4.3.2
Fourier transform
4.3.2
Fréchet differentiable
9.2.5 | 9.3.2 | 9.3.4
free-induction decay (FID) signal
4.2.1
frequency domain bootstrap
2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7
frequency polygon
4.3.3
Friedman's index
6.2.2.2.1
full conditional distributions
3.4.1
full likelihood
12.3.3
full-screen view
12.3.5
Functional Image Analysis Software - Computational Olio (FIASCO)
4.4.2.1 | 4.5.1
functional model
5.1
functional neuroimaging
4.2.3.2
gain sequences
6.3.2
gamma distribution
7.2.1 | 12.1.2 | 2.3.5
GARCH
2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.1 | 2.3 | 2.3 | 2.4.2 | 2.4.2.2 | 2.4.2.2
Gauss-Jordan elimination
4.2.1
Gauss-Newton method
8.2.1.2
Gauss-Seidel method
4.3.3 | 4.3.5.1
Gaussian quadrature
1.5
Gaussian simulation smoother
2.3.2.2 | 2.3.2.3
Gaussian/normal distribution
2.2.1.3 | 2.2.1.3 | 2.2.1.3 | 2.2.3.1 | 2.2.3.2.2 | 2.2.3.2.2 | 2.2.3.3 | 2.2.3.3 | 2.3 | 2.3.2 | 2.3.2.1 | 2.3.2.1 | 2.3.2.2 | 2.3.2.2 | 2.3.2.3 | 2.3.2.3 | 2.3.2.3 | 2.3.2.3 | 2.3.2.3 | 2.3.2.3 | 2.3.3 | 2.3.4 | 2.3.4 | 2.3.5 | 2.4.2.1
Matrix
2.2.1.3
truncated
2.2.1.3
gene expression data
5.5.3.1
generalized additive model
7.5.8
generalized cross validation
1.1 | 1.1 | 1.3 | 1.3 | 1.4 | 1.4 | 1.4 | 1.6 | 5.4.1.1
generalized degrees of freedom
1.2 | 1.3 | 1.3 | 1.3
generalized EM (GEM) algorithm
5.2.2 | 5.2.3 | 5.4.2 | 5.4.2.1
generalized estimating equations (GEE)
7.5.7
generalized feedback shift register (GFSR)
2.4.1 | 2.4.5
generalized linear mixed models (GLMM)
5.4.1.1
generalized linear model (GLM)
7.1
generalized maximum likelihood method
1.3 | 1.3 | 1.6 | 12.3.1
generalized method of moments
2.2.1.2.1
generalized partial linear model
7.5.8
generalized principal components
6.3.1
generalized principal components analysis (GPCA)
6.3.1
genetic algorithm
6.4.3
genetic algorithms
6.4
geographic brushing
10.6.1
Geometric distribution
7.2.1
geometrically ergodic
3.2.1
getter
13.2
GGobi
10.4 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | Web
Gibbs sampling algorithm
3.1
Gibbs sampling/sampler
5.5.1 | 5.5.2 | 5.5.2 | 5.5.3.2 | 11.4.2 | 11.4.2 | 2.2.1.2.2 | 2.2.1.2.2 | 2.2.1.2.2 | 2.2.1.3 | 2.2.1.3 | 2.2.1.3 | 2.2.3.2.2 | 2.2.3.2.2 | 2.3.2.3 | 2.4.1 | 2.4.1 | 2.4.2.1 | 2.4.2.2
griddy-
2.4.2.2 | 2.4.2.2
mixing of
11.4.2
Givens rotations
4.1.3.2
Glivenko-Cantelli theorem
9.1.3
global optimization
6.3.1 | 6.4.1
global solutions
6.1.4
goodness of fit
2.2.3 | 1.1 | 1.2 | 5.3 | 5.4.2.2
gradient approximation
6.3.2 | 6.3.3
Gram-Schmidt orthogonalization
4.1.3.4
grand tour
10.3.5 | 10.3.7 | 10.3.9
graphics algebra
11.3
Green's algorithm
11.4.3
Greenwood's formula
12.1.1
gross error model
9.3.3
gross error neighbourhood
9.2.4 | 9.4.3
Gumbel distribution
2.2.3.1
Gustafson's law
8.2.2.2
Hall's index
6.2.2.2.3
Halton sequences
2.2.3.2.1
Hampel identifier
9.2.7
hard thresholding
1.1
harmonic mean
11.3.3
hat function
2.8.4
hat matrix
5.3.3
Hawkes process
2.3.5
hazard function
12.1 | 12.4.1 | 2.3.5
hazard rate
12.1
head motion
4.4.2.1 | 4.4.2.1 | 4.4.2.1
Heisenberg's uncertainty principle
7.2.2
Hessian (or Jacobian) matrix
6.3.3
Hessian (second derivative) matrix
6.3.1
heterogeneity
2.2.1.3 | 2.2.1.3 | 2.2.1.3 | 2.2.1.3 | 2.2.1.3 | 2.2.1.3 | 2.2.1.3 | 2.2.3.2.2 | 2.3.5
heterogeneous populations
11.2.3 | 11.4.2
heteroscedasticity
1.6
hexagonal bins
4.2.1
hidden Markov model
5.2.3 | 5.5.3.2 | 5.5.3.2
hierarchical Bayes
2.2.3.2.2
hierarchical command sequence
12.3.8
high breakdown affine equivariant location and scale functionals
9.3.4
high breakdown regression functional
9.4.4
higher-order kernels
Rates
highest possible breakdown point
9.2.4
highest posterior region
11.2.2
Householder reflections
4.1.3.1
HPF (High Performance Fortran)
8.3.6
Huber distribution
9.1.2
hue brushing
10.3.10
human-machine interaction
6.3.3
HyperVision
10.4.2 | 10.4.2 | 10.4.2 | 10.4.2
hypotheses
11.2.1
hypothesis testing
5.4.2.2
i.i.d. resampling
2.1 | 2.2 | 2.2 | 2.4.3 | 2.4.3 | 2.4.7
identifiability
11.2.1
identification
2.2.1.3 | 2.4.1
problem
2.4.1 | 2.4.2.2 | 2.4.2.2
restrictions
2.2.1.1 | 2.4.1 | 2.4.2.1 | 2.4.2.1 | 2.4.2.1 | 2.4.2.1
Image Analysis
5.5.3.2
image grand tour (IGT)
10.3.8
image registration
4.4.2.1
immersive projection technology (IPT)
10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2
importance function
11.3.1 | 2.3.2.1 | 2.3.2.1 | 2.3.2.1 | 2.3.2.2 | 2.3.2.2
choice of
11.3.1
with finite variance
11.3.1
importance sampling
3.3 | 1.5 | 3.2 | 2.3.2.2 | 2.3.2.2
and regular Monte Carlo
11.3.2
degeneracy of
11.3.2 | 11.5.2
efficient (EIS)
2.3.2 | 2.3.2.1 | 2.3.2.1 | 2.3.2.1 | 2.3.2.1 | 2.3.2.1 | 2.3.2.2 | 2.3.2.3 | 2.3.3 | 2.3.3 | 2.3.3 | 2.3.3 | 2.3.3 | 2.3.3 | 2.3.4 | 2.3.4 | 2.3.5 | 2.3.5 | 2.3.5
for model choice
11.3.3
incomplete-data
likelihood
5.1.2 | 5.2.1
missing data
5.1.1 | 5.1.2 | 5.2.1 | 5.3.1 | 5.3.3 | 5.3.5 | 5.4.1.1 | 5.5.3.2
problem
5.1.1 | 5.1.2 | 5.3.1 | 5.5.2
incremental EM (IEM) algorithm
5.4.3.2
independence M-H
3.3
independence of irrelevant alternatives (IIA)
2.2.3.1 | 2.2.3.1 | 2.2.3.1
independent increments
12.4.1
indexed search
2.8.1
inefficiency factor
3.2.2
infinite collection of models
11.2.3
influence function
9.1.2
information criterion
Akaike
1.1 | 1.1 | 1.3 | 1.3 | 1.3 | 1.3 | 1.3 | 1.3 | 1.4 | 1.4 | 7.3.5 | 8.1.3.1 | 11.2.3 | 15.2.2.1
Bayesian
1.1 | 1.1 | 1.3 | 1.3 | 1.3 | 1.3 | 1.3 | 1.3 | 1.4 | 1.5 | 7.3.5
Schwarz
8.1.3.1
information matrix
complete-data
5.3.5
expected
5.3.5 | 5.3.5
observed
5.3.5 | 5.3.5
inheritance
13.4 | 13.4.4
injected randomness
6.1.3
instance
13.2.3
integral
2.1 | 2.2.1.1 | 2.2.1.1 | 2.2.1.1 | 2.2.3.2.1 | 2.2.3.2.2 | 2.3.2 | 2.3.2.1 | 2.3.2.1 | 2.3.2.2
approximation
11.3.1
high dimensional
2.2.1.2.2
multiple
2.3.2
ratio
11.2.2
integrated mean square error
No
intensity
function
2.3.5
model
2.3.5 | 2.3.5
intensity functions
12.4.1
inter arrival time
5.4 | 5.4 | 5.4 | 5.4 | 5.4
interaction term
7.4
interface
13.2.1 | 13.5 | 13.5.4
interface for derivation
13.2.5
Interface Symposia
1.2.2
International Association for Statistical Computing (IASC)
1.2.2
internet protocol
5.2
intersection classifier
5.6 | 5.6 | 5.6
intersection graph
5.6 | 5.6
invariant
3.2
Inverse Gaussian distribution
7.2.1
inverse iterations
4.4.6
inverse moments
6.3.3
inversion method
2.1 | 2.8.1
inverted gamma density/prior
2.3.2.3 | 2.3.2.3 | 2.3.4 | 2.4.2.1
inverted Wishart distribution
2.2.1.3 | 2.2.1.3 | 2.2.3.2.2
iterative refinement
4.2.2
iterative simulation algorithms
5.5.2
iteratively reweighted least squares
7.3 | 7.3.3
Jacobi method
4.3.2 | 4.4.2
Jasp
Web
Java threads
8.3.2.2
k-space
4.3.2 | 4.3.2 | 4.5.1
Kalman filter
2.3.2.2 | 2.3.2.2 | 2.3.2.2 | 2.3.2.2 | 2.3.2.3 | 2.3.5
augmented
2.3.2.3
Kaplan-Meier curves
14.5.1
Kaplan-Meier method
12.1.1
Karush-Kuhn-Tucker (KKT) condition
15.5.1.3
kernel
function
15.4
kernel trick
15.4.1
matrix
15.4.2
mercer
15.4.2.2
kernel density
5.4 | 5.4 | 5.4 | 5.4
kernel density estimation
2.8.3 | 10.3.10
kernel estimation
4.3.4 | 2.3.3
kernel smoother
5.2.1
keystroke timings
5.6 | 5.6
knowledge discovery
13.1
Kolmogoroff metric
9.1.2 | 9.1.3 | 9.2.4
kriging
3.1 | 3.4.1 | 3.5
Kuiper metric
9.2.4
Kullback-Leibler discrepancy
1.3
lagged-Fibonacci generator
2.3.2
Lagrange multipliers
6.2.1
Laplace approximation
1.5
largest nonidentifiable outlier
9.2.7
Larmor frequency
4.2.1 | 4.2.1
lasso
8.1.8
computation
8.1.8
late binding
13.5.1
latent variables
3.1.1
Latin hypercube sampling
3.5.2
lattice
2.3.2 | 2.4.3 | 2.6
Law of Large Numbers
11.3.1
learning
15.1 | 15.2.1
least median of squares LMS
9.4.4
least squares
8.1.1
computation
8.1.1.1
explicit form
8.1.1
Gauss-Markov theorem
8.1.1
inference
8.1.1.2
orthogonal transformations
8.1.1.1
least trimmed squares
9.4.4 | 9.4.5
length of the shortest half
9.2.4
Levenberg-Marquardt method
8.2.1.3
leverage effect
2.3.4
leverage point
9.4.5 | 9.4.5
library
12.3.8
likelihood
2.2.1.2.1 | 2.2.1.2.1 | 2.2.1.2.2 | 2.3.2.2 | 2.3.2.2 | 2.4.1 | 2.4.1
function
2.2 | 2.3.2 | 2.3.2.2 | 2.3.2.2 | 2.3.2.2 | 2.3.3 | 2.3.5 | 2.3.5 | 2.4.1
intensity-based
2.3.5
intractable
11.2
marginal
2.4.2.2
maximum
2.2.1.2.1 | 2.2.1.2.1 | 2.3.2 | 2.4.1
simulated
2.2.1.2.1 | 2.2.3.2.1
likelihood ratio test
generalized linear model (GLM)
7.3.5
likelihood smoothing
5.5.2
limited dependent variable
2.1 | 2.2
linear congruential generator (LCG)
2.3.1 | 2.3.6 | 2.6
linear discriminant analysis
14.1
linear feedback shift register (LFSR)
2.4.1 | 2.4.4 | 2.4.6 | 2.6
linear recurrence
2.3.1
linear recurrence modulo 2
2.4.1
linear recurrence with carry
2.3.6
linear reduction
6.2
linear regression
7.1 | 8. | 8.1 | 8.1 | 9.4.1
linear smoother
5.2.1 | 5.3
linear system
direct methods
4.2
Gauss-Jordan elimination
4.2.1
iterative refinement
4.2.2
gradient methods
4.3.5
conjugate gradient method
4.3.5.3
Gauss-Seidel method
4.3.5.1
steepest descent method
4.3.5.2
iterative methods
4.3
Gauss-Seidel method
4.3.3
general principle
4.3.1
Jacobi method
4.3.2
successive overrelaxation (SOR) method
4.3.4
link function
5.5.2.1 | 7.1 | 7.2.2 | 11.3.3
canonical
7.2.2
linked brushing
10.3.2
linked highlighting
13.5.2.3
linked views
10.3.2
linking
12.3.5
local bootstrap
2.4.5 | 2.4.5
local likelihood
5.5.2.1
local likelihood equations
5.5.2.1
local linear estimate
5.2.2 | 5.2.2
local optimization
6.3.1
local polynomial
5.3 | 5.5.2.1
local regression
5.2.2
local reversibility
3.3.3
localized random search
6.2.2.2 | 6.3.2
location functional
9.1.2 | 9.2.1 | 9.2.4 | 9.2.5 | 9.3.1 | 9.3.4
location-scale-free transformation
12.2
log-likelihood
3.3.2.1
generalized linear model (GLM)
7.3.1
log-linear model
7.5.5 | 12.4.2
log-logistic distribution
12.1.2
log-normal distribution
12.1.2 | 2.2.3.1 | 2.3.5
log-rank statistic
14.5.1
logistic distribution
2.2.3.1
logit
7.2.1
mixed
2.2.3.1 | 2.2.3.2.2 | 2.2.3.2.2 | 2.4
mixed multinomial (MMNL)
2.2 | 2.2.3 | 2.2.3.1 | 2.2.3.1 | 2.2.3.2.1 | 2.2.3.3
model
2.2.3.1 | 2.4
multinomial
2.2.3.1
probability
2.2.3.1 | 2.2.3.2.1
logit model
7.1 | 7.2.2 | 7.4
longitudinal
3.8.2
longitudinal data
7.5.7
loss function
6.1.1 | 15.2.1
low pass filter
1.1
LR method
4.4.5
LU decomposition
4.1.2
M-estimation
5.5.3.1
M-functional
9.2.3 | 9.2.3 | 9.2.3 | 9.2.4 | 9.2.5 | 9.2.6 | 9.2.6 | 9.3.2 | 9.3.2 | 9.3.2 | 9.4.2 | 9.4.3
with a redescending $ \psi $-function
9.2.3
M-step (Maximization step)
5.1.2 | 5.2.1
exponential family
5.2.1 | 5.3.2
factor analysis model
5.4.2.2
failure-time data
5.3.2
generalized EM (GEM) algorithm
5.2.2
normal mixtures
5.3.1
magnetic field inhomogeneities
4.3.3.1 | 4.4.2.1
magnetic resonance
4.2.1
magnetic resonance imaging
4.2.2
magnetism
4.2.1
magnetoencephalogram (MEG)
4.2.3.2
Mallow Cp
1.1 | 1.1 | 1.3 | 1.3 | 1.3 | 1.3 | 1.4 | 1.4
MANET
10.4 | 10.4 | 10.4.1 | 10.4.1 | 10.4.1 | 10.4.1 | 10.4.1 | 10.4.1 | 10.4.1 | 10.4.1 | 10.4.1 | 10.4.1 | 10.4.1
margin
15.3.2
marginal distribution function
12.2
marginal likelihood
3.1
market risk
1.1
marketing
2.2 | 2.2.1.3 | 2.2.1.3
Markov bootstrap
2.4 | 2.4.5 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.7
Markov chain
3.1 | 3.2.1 | 6.4.5
Markov chain Monte Carlo (MCMC)
3.1 | 5.5.1 | 5.5.2 | 1.5 | 2.3.2 | 2.3.2.3 | 2.3.2.3 | 2.3.3 | 2.3.3 | 2.3.3 | 2.3.3 | 2.3.4 | 2.3.4 | 2.3.4 | 2.3.4 | 2.3.5 | 2.4.1 | 2.4.1 | 2.4.1 | 2.4.2.1 | 2.4.2.1
Markov chain Monte Carlo (MCMC) algorithm
11.1 | 11.4 | 11.4
automated
11.5.1
Markov random field
5.5.3.2
Markov switching autoregressive model
2.4.2 | 2.4.2.1
masking effect
9.2.7
Mason Hypergraphics
10.5.1
Mathematica
Web
mathematical programming
6.3.1
matrix decompositions
4.1
Cholesky decomposition
4.1.1
Givens rotations
4.1.3.2
Gram-Schmidt orthogonalization
4.1.3.4
Householder reflections
4.1.3.1
LU decomposition
4.1.2
QR decomposition
4.1.3
SVD decomposition
4.1.4
matrix inversion
4.1.5
matrix linear recurrence
2.4.1
maximally equidistributed
2.4.2 | 2.4.6
maximum full likelihood
12.3.3
maximum likelihood
2.2.1.2.1 | 2.3.2 | 2.4.1
Monte Carlo (MCML)
2.3.2 | 2.3.2.2 | 2.3.2.2 | 2.3.3 | 2.3.3 | 2.3.3 | 2.3.3 | 2.3.4 | 2.3.4 | 2.3.5
quasi- (QML)
2.3.5
simulated
2.2.1.2.1
maximum likelihood estimate
9.2.3 | 12.1.2
maximum likelihood estimation
5.1.1
global maximum
5.1.1 | 5.2.3 | 5.2.3
local maxima
5.1.1 | 5.2.3 | 5.2.3 | 5.3.4
maximum partial likelihood
12.3.3
maximum score method
10.4
maximum-likelihood
7.3 | 7.3.2
mean squared error
6.3.3 | 1.2 | No | 5.3
measurement noise
6.3.2
median
9.1.1 | 9.1.2 | 9.2.2 | 9.2.4 | 9.2.7 | 9.3.1 | 9.5.1 | 9.5.2
median absolute deviation MAD
9.1.1 | 9.2.2 | 9.2.4 | 9.2.7 | 9.5.1
median polish
9.5.2
menu hierarchy
12.3.3
Mersenne twister
2.4.1 | 2.4.5 | 2.4.6 | 2.7
message
13.2.2
metaclass
13.2.3.1
metamodel
3.1 | 3.3 | 3.3 | 3.4.1 | 3.5.1
method of composition
3.8.1
method of moments
12.1.2
Metropolis Hastings (MH) algorithm
2.2.3.2.2 | 2.2.3.2.2
Metropolis method
3.1
Metropolis-Hastings agorithm
11.4
Metropolis-Hastings algorithm
5.5.2 | 11.4.1 | 2.2.3.2.2
Metropolis-Hastings method
3.1
micromaps
10.6.2
military conscripts
11.4.2
MIMD (multiple instruction stream-multiple data stream)
8.2
MiniCAVE
10.5.2
minimum covariance determinant (MCD)
9.3.4
minimum volume ellipsoid (MVE)
9.3.4
mirror filter
7.4.1
misclassification cost
14.2.2
missing variables
simulation of
11.4.2
mixed model
7.5.7
mixed multinomial logit (MMNL)
2.2 | 2.2.3 | 2.2.3.1 | 2.2.3.2.1 | 2.2.3.3
mixing
3.1.1
mixing density/distribution
2.2.3.1 | 2.2.3.1 | 2.2.3.2.2 | 2.2.3.2.2 | 2.2.3.2.2 | 2.2.3.3 | 2.2.3.3
mixture
Poisson distributions
11.2.3
mixture models
5.1.1 | 5.3.4 | 5.5.1
mixture of factor analyzers
5.4.2.2 | 5.5.3.1
normal mixtures
5.3.1 | 5.3.5 | 5.4.3.2 | 5.4.3.2 | 5.4.3.2
Mixture Sampler algorithm
2.3.2.3 | 2.3.2.3 | 2.3.2.3
mode
attraction
11.4.1
mode tree
4.3.4
model
AR
11.2.1 | 11.2.3 | 11.4.3
averaging
11.2.3 | 11.4.3
binomial
11.2.2
choice
11.4.3
generalised linear
11.2.1
generalized linear
11.3.3
index
11.4.3
mixture
11.4.2
probit
11.3.2 | 11.4.1
model averaging
11.2.3
model choice
11.2.3
and testing
11.2.3
parameter space
11.2.3
model complexity
1.1
model domain
13.1.1
model selection
1.1 | 2.4.1 | 2.4.2.1
generalized linear model (GLM)
7.3.5
modified Bessel function
1.3
moment generating function
7.1
moment index
6.2.2.2.2
Mondrian
10.4 | 10.4.1 | 10.4.1 | 10.4.1 | 10.4.1 | 10.4.1 | 10.4.1 | 10.4.1
Monte Carlo
6.1.3
confidence interval
11.3.2
Markov chain (MCMC)
2.3.2 | 2.3.2.3 | 2.3.2.3 | 2.3.3 | 2.3.3 | 2.3.3 | 2.3.3 | 2.3.4 | 2.3.4 | 2.3.4 | 2.3.4 | 2.3.5 | 2.4.1 | 2.4.1 | 2.4.1 | 2.4.2.1 | 2.4.2.1
maximum likelihood (MCML)
2.3.2
with importance function
11.3.1
Monte Carlo EM
5.4.1 | 5.4.1.1 | 5.5.2
Monte Carlo maximum likelihood (MCML)
2.3.2 | 2.3.2.2 | 2.3.2.2 | 2.3.3 | 2.3.3 | 2.3.3 | 2.3.3 | 2.3.4 | 2.3.4 | 2.3.5
Monte Carlo method
2.1 | 13.1.1 | 3.1 | 3.1 | 3.2 | 11.3.1
and the curse of dimension
11.3.2
Monte Carlo techniques
11.3
efficiency of
11.3.2
population
11.5.2
sequential
11.5.2
Moore's law
8.1
mosaic map
4.2.1
mosaic plot
10.3.11 | 13.5.2.3
mother wavelet
Orthogonality.
MPI (Message Passing Interface)
8.3.5
multicollinearity
8.1.2 | 8.1.2
exact
8.1.2 | 8.1.2
near
8.1.2 | 8.1.2
multilevel model
7.5.7
multimodality
2.4.2.1
multinomial distribution
2.4.2.2
multinomial responses
7.5.4
multiple binary responses
14.6
multiple counting processes
12.4.2
multiple document interface
12.3.5
multiple failures
12.4 | 12.4.1
multiple recursive generator (MRG)
2.3.1
multiple recursive matrix generator
2.4.5
multiple-block M-H algorithms
3.1
multiply-with-carry generator
2.3.6
multiresolution analysis
7.3.3
multiresolution analysis (MRA)
7.3.3
multiresolution kd-trees
5.4.3.2
multivariate smoothing
5.5
multivariate-t density
3.3.2.2
mutation
6.4.2 | 6.4.2
Nadaraya-Watson estimate
2.2 | 5.2.1
negative binomial distribution
7.2.1
nested models
7.3.5
network sensor
5.3
neural network
6.3.3 | 13.5.1.2 | 15.4.1
New York Stock Exchange (NYSE)
2.3.5 | 2.3.5
Newton's method
6.3.1 | 8.2.1.1
Newton-Raphson algorithm
6.3.1 | 6.3.3 | 7.3.3 | 2.2.3.2.1
Newton-Raphson method
5.5.2.2 | 12.4.2
Neyman-Pearson theory
11.2.2
no free lunch (NFL) theorems
6.1.4
node impurity
14.2.1
noisy measurement
Convergence | 6.3.1
nominal logistic regression
7.5.4
non-nested models
7.3.5
nonhomogeneous Poisson process
12.4.2 | 12.4.4
nonlinear least squares
8.2.1 | 8.2.1
asymptotic normality
8.2.1
inference
8.2.2
nonlinear regression
8. | 8.2
nonparametric autoregressive bootstrap
2.4.4 | 2.4.4 | 2.4.4
nonparametric curve estimation
2.1 | 2.1 | 2.4.7
nonparametric density estimation
4.1
normal approximation
11.3.2
normal distribution
9.1.1 | 9.2.6
normal equations
5.2.2 | 8.1.1
normalization property
7.3.5
normalizing constant
11.3.1 | 11.3.3
ratios of
11.3.3
novelty detection
15.1 | 15.6.2
NOW (network of workstations)
8.2.1.2
nuisance parameter
12.4.2 | 12.4.4
null deviance
7.4
NUMA (non-uniform memory access)
8.2.1.1
numerical standard error
3.2.2
nViZn
10.6.2
Nyquist ghosts
4.3.3.1 | 4.4.2.1
object
13.2
object composition
13.2.4 | 13.4.4
Object oriented programming (OOP)
13.1
object, starting
13.7
Occam's razor
1.2 | 1.5
offset
7.3.4
Old Faithful geyser data
4.2.1 | 4.2.1
one-way analysis of variance
9.5.1
one-way table
9.5.1
OpenMP
8.3.3
optimization/optimizer
2.2.3.2.1 | 2.2.3.2.1 | 2.3.2.1 | 2.3.3 | 2.4.1
order of menu items
12.3.3
ordered probit model
7.5.4
ordinal logistic regression
7.5.4
ordinary least squares (OLS)
2.3.2.1 | 2.3.2.1
orthogonal series
5.2.5
outlier
9.1.1 | 9.2.3 | 9.2.6 | 9.2.7 | 9.3.5 | 9.4.3 | 9.5.2 | 2.3.4
outlier detection
15.1 | 15.6.2
outlier identification
9.3.5 | 9.5.1
outlier region
9.2.7
outwards testing
9.2.7
overdispersion
7.5.2
overfitting
13.5.1.1
oversmoothing
Choosing | 4.3.2 | 4.3.3
panel data
7.5.7 | 2.2 | 2.4.2.2
panning
10.3.3
parallel computing
8.1
parallel coordinate display
10.3.6
parallel coordinate plot
10.3.6 | 11.9.2
parallel coordinates
13.5.2.2 | 5.5 | 5.5 | 5.5
parameter
of interest
11.2.1
parameter space
constrained
11.2
parameter-expanded EM (PX-EM) algorithm
5.4.3.1
Parseval formula
7.2.4
partial autocorrelation
11.2.1
partial least squares
8.1.9
algorithm
8.1.9
extensions
8.1.9
latent variables
8.1.9
modified Wold's R
8.1.9
nonlinear regression
8.2.3
Wold's R
8.1.9
partial likelihood
12.3.1 | 12.3.3
partially linear models
10.2.2
particle systems
11.5.2
password cracking
5.6
pattern recognition
6.3.3
Pearson statistic
7.3.4
penalized least squares
1.1 | 5.2.3
penalized likelihood
5.1.1 | 5.4.3.1 | 5.5.3.2 | 5.5.2
perfect sampling method
3.9
periodogram
7.2.1
permutation tests
2.3 | 2.3
physiological noise
4.3.3.2 | 4.4.2.1
pie chart
10.3.11
piecewise polynomial
5.2.4
pilot estimate
5.4.1.3
pixel grand tour
10.3.8
plug-in
2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1
plug-in bandwidth selection
5.4.1.3
PMC vs. MCMC
11.5.2
point process
2.3.5 | 2.3.5
Poisson data
7.2
Poisson distribution
2.6 | 12.4.1 | 2.4.1
Poisson process
2.8.5
polymorphic class
13.5.1
polymorphism
13.5
polynomial lattice
2.4.3
polynomial LCG
2.4.4
polynomial regression
3.1 | 3.4.3 | 3.6
polynomial terms
7.4
population
6.4.1
population Monte Carlo (PMC) techniques
11.5.2
positron emission tomography (PET)
4.2.3.2
posterior density
3.3.2 | 2.2.1.2.2 | 2.2.1.2.2 | 2.2.1.3 | 2.2.3.2.2 | 2.2.3.2.2 | 2.3.2 | 2.3.3 | 2.3.3 | 2.3.4 | 2.4.2.1
posterior distribution
11.2.1
posterior mean
2.2.1.2.2 | 2.2.1.3 | 2.2.1.3 | 2.2.1.3 | 2.2.1.3 | 2.2.1.3 | 2.3.3 | 2.3.4 | 2.3.4 | 2.3.4 | 2.3.4 | 2.4.2.1 | 2.4.2.1
posterior probability
5.3.1 | 5.3.4 | 5.4.3.2
power expansions
12.3.3
power method
4.4.1
power parameter
12.2
prediction
5.4.1.1 | 5.4.1.1 | 11.2.1
sequential
11.2.1
predictive modeling
13.4.3
predictive squared error
1.2
PRESS
1.4
primitive polynomial
2.3.1 | 2.4.1
principal components analysis (PCA)
6.2.1 | 8.1.4
principal components regression
8.1.4 | 8.1.4
choice of principle components
8.1.4 | 8.1.4
principal curve
6.3.3
prior
proper
11.3.1
prior (density)
2.2.1.2.2 | 2.2.1.3 | 2.2.1.3 | 2.2.1.3 | 2.2.3.2.2 | 2.2.3.2.2 | 2.3.2.3 | 2.3.2.3 | 2.3.4 | 2.4.1 | 2.4.2.2
informative
2.4.2.2
uninformative
2.2.1.3 | 2.3.2.3
prior distribution
11.2
conjugate
11.2.1
selection of a
11.2
prior-posterior summary
3.3.2.1
probability of move
3.1
probit
model
2.2.3.1 | 2.4.1
multinomial
2.2.1.3
multinomial multiperiod
2.2 | 2.2.1.1 | 2.2.1.2
multivariate
2.2 | 2.2.1 | 2.2.2
static multinomial
2.2.1.3
probit model
7.2.2 | 7.4
probit regression
3.5
problem domain
13.1.1
process control
6.3.3
process forking
8.3.1
productivity
12.3.6
program execution profiling
5.6
progress bar
12.3.6
projection
10.3.4
projection index
6.2.2
projection pursuit
10.3.7 | 5.5 | 6.2.2
projection pursuit guided tour
10.3.7
projection pursuit index
10.3.7
projection step
6.3.3.1
proportion
6.2.1.1
proportional hazard
14.5.1
proportional hazards model
10.3 | 12.3.3
proposal
adaptive
11.5.2
multiscale
11.5.2
proposal distribution
3.1
prosection matrix
10.3.3
prosections
10.3.3
proximity
14.3.3
pruning
13.5.1.1
pseudo data
2.1
pseudo-likelihood
7.5.3
pseudorandom number generator
2.1
Pthread library
8.3.2.1
pulse sequence
4.3.1
PVM (Parallel Virtual Machine)
8.3.4
QR decomposition
4.1.3
QR method
4.4.4
quadratic principal components analysis (QPCA)
6.3.1
quality improvement
6.3.3
quantlets
CSAfin06
1.2.5
quasi-likelihood
7.5.3
quasi-maximum likelihood (QML)
2.3.5
queuing systems
6.3.3
R
Web | 11.6
radial basis networks
15.4.1
random effects
3.8.2 | 5.4.1.1 | 5.4.1.1
random forests
14.3.3
random graph
5.6
random noise
6.1.3
random number generator
2.1 | 2.1 | 13.2.4
approximate factoring
2.3.3
combined generators
2.3.4 | 2.4.6 | 2.5 | 2.7
definition
2.2.2
figure of merit
2.3.2 | 2.3.2 | 2.4.2
floating-point implementation
2.3.3
implementation
2.3.3 | 2.5
jumping ahead
2.2.3 | 2.3.5 | 2.4.1
non-uniform
2.8
nonlinear
2.5
period length
2.2.2 | 2.4.1 | 2.4.5
physical device
2.2.1
power-of-two modulus
2.3.3
powers-of-two-decomposition
2.3.3
purely periodic
2.2.2
quality criteria
2.2.3 | 2.8
seed
2.2.2
state
2.2.2
statistical tests
2.2.4 | 2.6
streams and substreams
2.2.3 | 2.7
random numbers
13.2.4
common
2.2.3.2.1 | 2.3.2.1
pseudo-
2.2.3.2.1
quasi-
2.2.3.2.1
random permutation
2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.3
random permutation sampler
2.4.2.1
random perturbation vector
6.3.3
random search
6.2
random walk M-H
3.3
Rao-Blackwellization
3.6
rate of convergence
5.2.4 | 5.3.5 | 5.4.2.1 | 5.4.2.2 | 5.4.3.2 | 5.4.3.2
rate matrix
5.2.4
ratio
and normalizing constants
11.3.1
importance sampling for
11.3.3
of integrals
11.3.2
of posterior probabilities
11.2.2
ratio-of-uniforms method
2.8.6
real-number coding
6.4.2
recursive partitioning
11.9.3
recursive sample mean
5.4
red-green blindness
12.3.7
reduced conditional ordinates
3.6
reformatting
10.3.3
REGARD
10.4 | 10.4.1 | 10.4.1 | 10.4.1 | 10.4.1 | 10.4.1 | 10.6.1
regression depth
9.4.4
regression equivariant
9.4.2
regression functional
9.4.1
regression splines
5.2.4
regression trees
14.7
regression-type bootstrap
2.4.5 | 2.4.5
regressor-outlier
9.4.5
regularization
15.2.2.1 | 15.2.2.1
rejection method
2.8.4 | 2.8.6
rejection sampling
2.3.2.3
relative projection Pursuit
6.2.2.3
remote sensing data
4.2.1
resampling
2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.1 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.2 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.3 | 2.4 | 2.4 | 2.4 | 2.4 | 2.4.3 | 2.4.3 | 2.4.3 | 2.4.3 | 2.4.3 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 5.4.3
resampling tests
2.2 | 2.3
rescaling
10.3.3
residual
9.5.2
residual sum of squares
7.3.2
residuals
generalized linear model (GLM)
7.3.4
resistant one-step identifier
9.4.5
resolution of identity
Resolution
response-outlier
9.4.5
restricted maximum likelihood
1.5
reverse move,
probability
11.4.3
reversible
3.2.1
reversible jump MCMC
11.4.3 | 11.4.3
ridge regression
11.2 | 8.1.6 | 8.1.6
almost unbiased
8.1.6.2
almost unbiased feasible
8.1.6.2
bias
8.1.6
choice of ridge parameter
8.1.6 | 8.1.6.1 | 8.1.6.1
feasible generalized
8.1.6.1
generalized
8.1.6.1
minimization formulation
8.1.6
nonlinear regression
8.2.3
reduced-rank data
8.1.6.3
risk
15.2.1
empirical
15.2.2 | 15.2.2
expected
15.2.2
regularized
15.2.2.1
structural minimization
15.2.3
risk measure
1.1 | 1.1
Robbin-Monro algorithm
11.5.1
robust
9.4.2
robust functional
9.2.7
robust location functional
9.1.3
robust regression
5.5.3 | 5.5.3.1 | 9.4.1 | 9.4.3
robust scatter functional
9.3.4
robust statistic
9.1.1 | 9.1.2 | 9.1.3
robustness
9.1.2 | 9.2.2 | 9.2.4
root node
14.2.1
root-finding
6.1.1 | 6.3.2
rotation
10.3.4
roulette wheel selection
6.4.2
S-functional
9.2.3 | 9.3.4 | 9.3.5 | 9.4.4 | 9.4.5
S-Plus
Web
sample mean
12.1.2
sampler performance
3.1.1
SAS
Web
Satterwaite approximation
5.4.2.2
saturated model
7.3.2
saturation brushing
10.3.10
scalable EM algorithm
5.4.3.2
scale functional
9.2.1 | 9.2.4
scales
11.4
scaling algorithm
11.4.1
scaling equation
7.3.3
scaling function
7.3.3
scanner data
2.2.1.3
scatter diagram
4.2.1 | 4.2.1
scatterplot
10.3.1 | 12.3.5
scatterplot matrix
10.3.1 | 12.3.5 | 12.3.5
schema theory
6.4.5
search direction
6.1.3
secondary data analysis
13.2
selection
6.4.2 | 6.4.2
selection sequences
10.3.2
self-consistency
5.2.3
semiparametric models
5.5 | 10.2
semiparametric regression
7.5.8
sensitivity analysis
3.1
sensor placement
6.3.3
serial test
2.6
setter
13.2
shape parameter
12.1.2
shared memory
8.2.1
shortcut
12.3.3
shortest half
9.1.3 | 9.2.3 | 9.3.4 | 9.4.4
shrinkage estimation
8.1.5
shrinking neighbourhood
9.1.2
sieve bootstrap
2.4.3 | 2.4.3 | 2.4.3 | 2.4.3 | 2.4.3 | 2.4.7 | 2.4.7 | 2.4.7
SIMD (single instruction stream-multiple data stream)
8.2
simulated maximum likelihood (SML)
2.2.1.2.1 | 2.2.3.2.1 | 2.2.3.2.1 | 2.2.3.3 | 2.2.3.3 | 2.3.2.2 | 2.3.3 | 2.3.4
quasi-random
2.2.3.2.1
simulated moments
2.2.1.2.1 | 2.2.3.2.1
simulated scores
2.2.3.2.1
simulated tempering
3.8.3
simulation
3.1 | 3.1 | 3.2 | 3.4.2 | 3.4.4 | 3.6 | 11.3 | 11.3.1 | 2.1 | 2.2.1.2.1 | 2.2.1.2.1 | 2.2.3.2.2 | 2.3.2 | 2.3.2.2 | 2.3.2.3 | 2.3.5 | 2.4.2.2
simulation-based optimization
6.1.3 | 6.3.3
simultaneous perturbation SA (SPSA)
6.3.3
single index model
7.5.8 | 10.2.1
single trial fMRI
4.3.1
SISD (single instruction stream-single data stream)
8.2
slammer worm
5.7
slash distribution
9.2.6
slice sampling
3.5
sliced inverse regression
6.4.1
slicing
10.3.3
smooth bootstrap
11.5.2
smoothed maximum score
10.4
smoothing
5.1
smoothing parameter
1.1 | 1.3 | 1.5 | 1.6 | Smoothing | 5.3
SMP (symmetric multiprocessor)
8.2.1.1
soft thresholding
1.1
software reliability
12.4
sparse matrices
4.5
sparsity
15.5.1.3
specification search
10.1
spectral density
7.2.1
spectral test
2.3.2
spectrogram
7.2.2
speech recognition
14.1
Spider
10.4.1 | 10.4.1 | 10.4.1
SPIEM algorithm
5.4.3.2
spine plot
10.3.11
spline
1.1 | 1.1 | 1.2.1 | 1.4 | 1.4
spline smoother
5.2.3 | 5.2.4
spreadplots
10.3.11
SPSA Web site
6.3.3
SPSS
Web
SQL
11.2
squeeze function
2.8.4
SRM
see structural risk minimization
stably bounded algebraic curve
6.3.2.5
standard deviation
9.1.1 | 9.2.2 | 9.2.4 | 9.2.7
standard errors
5.3.5 | 5.3.5 | 5.3.5
starting (initial) value
5.2.3 | 5.2.3 | 5.3.4 | 5.3.4 | 5.3.4
state space
2.3.2.2 | 2.3.2.3
state space model
Gaussian linear
2.3.2.2 | 2.3.2.2 | 2.3.5
stationarity
11.2.1 | 2.3.1 | 2.4.2.2
statistical computing
1.1
statistical functional
9.1.2 | 9.1.2
Statistical Parametric Mapping (SPM)
4.5.1
steepest descent
6.3.1
steepest descent method
4.3.5.2
Stein-rule estimator
8.1.5
stereoscopic display
10.5
stereoscopic graphic
10.5.1
stochastic approximation
6.3
stochastic conditional duration (SCD) model
2.3.5 | 2.3.5 | 2.3.5 | 2.3.5
stochastic gradient
6.3.1
Stochastic optimization
6.
stock trading system
2.3.5
stopping rule
11.5.2
streaming data
5.4
structural risk minimization
15.2.3 | 15.2.3 | 15.3.2
structure parameter
12.4.4
Structured Query Language
11.2
subsampling
2.1 | 2.3 | 2.3 | 2.4 | 2.4.1 | 2.4.1 | 2.4.1 | 2.4.1 | 2.4.1 | 2.4.1 | 2.4.2
subtract-with-borrow
2.3.6
successive overrelaxation (SOR) method
4.3.4
supervised learning
13.3
supplemented EM (SEM) algorithm
5.3.5 | 5.4
support
13.5.1.4
support vector machine
15.1
decomposition
15.5.2
linear
15.3
optimization
15.5.1
sequential minimal optimization (SMO)
15.5.2.3
sparsity
15.5.1.3
support vector novelty detection
15.6.2
support vector regression
15.6.1
surrogate data tests
2.3 | 2.3 | 2.3
surrogate splits
14.3.3
survival analysis
5.3.2
survival function
12.1
survival model
5.5.3 | 7.5.6
survival rate
variance
11.5.2
survival trees
14.5
susceptibility artifacts
4.3.3.2
SV model
2.1 | 2.3 | 2.3.5
canonical
2.3.1 | 2.3.2 | 2.3.2.2 | 2.3.2.2 | 2.3.3 | 2.3.4 | 2.3.5
multivariate
2.3.4
SVD decomposition
4.1.4
symmetric command sequence
12.3.8
syn cookie
5.3
SYSTAT
Web
systems of linear equations
direct methods
4.2
Gauss-Jordan elimination
4.2.1
iterative refinement
4.2.2
gradient methods
4.3.5
conjugate gradient method
4.3.5.3
Gauss-Seidel method
4.3.5.1
steepest descent method
4.3.5.2
iterative methods
4.3
Gauss-Seidel method
4.3.3
general principle
4.3.1
Jacobi method
4.3.2
SOR method
4.3.4
Table Production Language (TPL)
11.3.4.1 | 11.3.4.1
tailored M-H
3.3
tailoring
3.3.3
TAQ database
2.3.5
target tracking
6.1.3
Tausworthe generator
2.4.1 | 2.4.4
Taylor series
5.3.1
Taylor series expansions
12.4.2
TCP three-way handshake
5.3
$ t$-distribution, folded
11.3.2
tempering
2.4.5
terminal nodes
14.2.2
termination criterion
6.4.4
Tesla
4.2.1
thinning
2.8.5
threading
8.3.2
threshold parameters
12.1.2
thresholding
7.3.1
time series
2.1 | 2.1 | 2.2 | 2.2 | 2.2 | 2.3 | 2.3 | 2.3 | 2.4 | 2.4 | 2.4.1 | 2.4.2 | 2.4.2 | 2.4.3 | 2.4.4 | 2.4.5 | 2.4.6 | 2.4.6 | 2.4.6 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.4.7 | 2.1 | 2.3 | 2.4.2.2 | 2.4.2.2
tissue contrast
4.2.2
tournament selection
6.4.2
traffic management
6.3.3
training data
13.3
transform
continuous wavelet
7.3.2
discrete Fourier
7.2.1
discrete wavelet
7.4
empirical Fourier-Stieltjes
7.1
fast Fourier
7.4
Fourier-Stieltjes
7.1
Hilbert
7.2.3
integral Fourier
7.2.2
Laplace
7.1
short time Fourier
7.2.2
Wigner-Ville
7.2.4
windowed Fourier
7.2.2
transformation
Box-Cox
7.1
Fisher $ z$
7.1
transformation models
10.2.4
transformed density rejection
2.8.4
transition kernel
3.2.1
transition matrix
6.4.5
translation equivariant functional
9.3.3
transmission control protocol
5.2
trapping state
11.4.2
tree growing
14.2.1
tree pruning
14.2.2
tree repairing
14.7
Trellis display
12.3.5 | 12.3.5
triangular distribution
2.2.3.1
trigonometric regression
1.1 | 1.1 | 1.2 | 1.4 | 1.4
trojan program
5.4 | 5.4
Tukey's biweight function
9.2.3 | 9.3.5
twisted generalized feedback shift register (GFSR)
2.4.1 | 2.4.5 | 2.4.6
two-way analysis of variance
9.5.2
UMA (uniform memory access)
8.2.1.1
unbiased risk
1.3 | 1.3 | 1.3 | 1.6
unbiased risk estimation
5.4.1.2
under-fitting
15.2.2
Unified Modelling Language (UML)
13.1.2 | 13.3
uniform distribution
2.1 | 2.2.3.1 | 2.4.2.2
uniformity measure
2.2.3 | 2.4.2
unit measurement
11.4.2
unobserved (or latent) variables
2.1
unobserved heterogeneity
10.3
unpredictability
2.2.5
unsupervised learning
13.3
user profiling
5.6
utility/utilities
2.2.1.1 | 2.2.1.1 | 2.2.1.1 | 2.2.1.1 | 2.2.1.2.2 | 2.2.1.2.2 | 2.2.1.3 | 2.2.1.3 | 2.2.3.1 | 2.2.3.1 | 2.2.3.3
validation data
13.3
Value at Risk (VaR)
1.1 | 1.1
copulas
1.4
vanGogh
Web
vanishing moments
7.3.5
Vapnik-Cervonenkis class
9.3.1
variable
auxiliary
11.4.2
variable selection
1.1 | 8.1.3
all-subsets regression
8.1.3.3 | 8.1.3.3
branch and bound
8.1.3.3
genetic algorithms
8.1.3.3
backward elimination
8.1.3.1
cross validation
8.1.3.4
forward selection
8.1.3.2
least angle regression
8.1.3.2
stepwise regression
8.1.3.1
variance estimation
5.3.3.1
variance reduction
2.8 | 2.8.1
variance reduction technique
3.2
varset
11.2 | 11.2.2
VC-bound
15.2.3
VC-dimension
15.2.3
VC-theory
15.2.3
vector error-correction model
2.2.1.3
Virtual Data Visualizer
10.5.2
virtual desktop
12.3.5
virtual reality (VR)
10.1 | 10.5 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.7.1
Virtual Reality Modeling Language (VRML)
10.5.2
visual data mining (VDM)
10.1
volatility of asset returns
2.1 | 2.3
voting
2.2 | 2.2.2
VoxBo
4.5.1
VRGobi
10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2
$ W$-transformation
12.2
Wasserstein metrics
14.5.1
waterfall plot
5.5
wavelet domain
7.4
wavelet regularization
15.2.2.1
wavelets
7.3
Daubechies
7.3.5
Haar
7.3.4
Mexican hat
7.3.2
periodized
7.4.1
sombrero
7.3.2
Weibull density function
12.1.2
Weibull distribution
12.1.2 | 12.1.2 | 2.3.5 | 2.3.5 | 2.3.5 | 2.3.5
Weibull process model
12.4.3
weight function
5.2.1
weights
generalized linear model (GLM)
7.3.4 | 7.5.1
wild bootstrap
2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.5 | 2.4.7
winBUGS
11.1 | 11.6
window titles
5.6
Wishart distribution
2.2.1.3
working correlation
7.5.7
XGobi
10.3.6 | 10.4 | 10.4 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.4.3 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.5.2 | 10.6.1 | 10.6.1
XML
11.2
XploRe
Web | 1.1 | 1.2.2 | 1.3
zooming
10.3.3