Index

admissible
Bayes
agglomerative techniques
Hierarchical
allocation rules
12.1
Andrews' curves
1.6 | 1.6
angle between two vectors
Angle
ANOVA
3.5
ANOVA - simple analysis of variace
3.5
Bayes discriminant rule
Bayes
Bernoulli distribution
4.5
Bernoulli distributions
4.5
best line
Subspaces
binary structure
Similarity
Biplots
Biplots
bootstrap
4.6 | 4.6
bootstrap sample
4.6 | 4.6
Boston housing
1.8 | 3.7 | 7.3 | 9.8 | 10.4 | 11.4 | 12.3
boxplot
1.1
construction
Construction
canonical correlation
14.
canonical correlation analysis
14.
canonical correlation coefficient
14.1
canonical correlation variable
14.1
canonical correlation vector
14.1
centering matrix
3.3
central limit theorem (CLT)
4.5 | 4.5 | 4.5
centroid
Hierarchical
characteristic functions
4.2 | no title
classic blue pullovers
3.1
cluster algorithms
11.3
cluster analysis
11.
Cochran theorem
5.2
coefficient of determination
3.4 | 3.6
corrected
3.6
column space
no title | 8.1
common factors
10.1
common principal components
9.7
communality
10.1
complete linkage
Hierarchical
computationally intensive techniques
18.
concentration ellipsoid
Geometry
conditional approximations
Conditional
conditional covariance
SIR | SIR
conditional density
4.1
conditional distribution
5.1
conditional expectation
Conditional | 18.3 | SIR
conditional pdf
4.1
confidence interval
4.5
confussion matrix
Estimation
conjoint measurement analysis
16.
contingency table
13.
contrast
Repeated
convex hull
18.1
copula
4.1
correlation
3.2
multiple
Conditional
correspondence analysis
13. | 13.
covariance
3.1
covariance matrix
decomposition
9.1
properties
no title
CPCA
9.7
Cramer-Rao
6.2
Cramer-Rao-lower bound
6.2
Cramer-Wold
Characteristic
cumulant
Cumulant
cumulative distribution function (cdf)
4.1
curse of dimensionality
18.3
data depth
18.1
degrees of freedom
3.5
dendrogram
Hierarchical
density estimates
1.2
density functions
4.1
derivatives
2.4
determinant
Determinant
diagonal matrix
Properties
Dice
Similarity
discriminant analysis
12.
discriminant rule
12.1
discrimination rules in practice
12.2
dissimilarity of cars
15.1
distance
d
Distance
Euclidean
Distance
iso-distance curves
Distance
distance matrix
15.2.1
distance measures
Distance
distribution
4.1
draftman's plot
1.4
duality relations
8.4
duality theorem
Relation
effective dimension reduction directions
18.3 | The
effective dimension reduction space
18.3
efficient portfolio
17.2
eigenvalues
no title
eigenvectors
no title
elliptical distribution
5.4
elliptically symmetric distribution
18.3
estimation
6.
existence of a riskless asset
Existence
expected cost of misclassification
Maximum
exploratory projection pursuit
Exploratory
extremes
1.1
F-spread
1.1
f-test
The
faces
1.5 | 1.5
factor analysis
10.
factor analysis model
10.1
factor model
10.2
factor score
10.3
factor scores
10.3
factorial axis
Representation | Subspaces
factorial method
9.6
factorial representation
8.5 | 8.5
factorial variable
Representation | 8.5
factors
8.1
Farthest Neighbor
Hierarchical
Fisher information
6.2
Fisher information matrix
6.2 | 6.2
Fisher's linear discrimination function
Fisher's
five-number summary
1.1 | 1.1
flury faces
1.5
fourths
1.1
French food expenditure
Quality
full model
3.5
G-inverse
G-inverse
non-uniqueness
2.2
general multinormal distribution
5.3
gradient
2.4
group-building algorithm
11.1
Hessian
2.4
hierarchical algorithm
Hierarchical
histograms
1.2 | 1.2
Hotelling $T^2$-distribution
5.3
idempotent matrix
Properties
identity matrix
Properties
independence copula
4.1
independent
3.2 | 4.1
inertia
8.5 | 8.5
information matrix
6.2
interpretation of the factors
Interpretation
interpretation of the principal components
9.3
invariance of scale
Invariance
inverse
Inverse
inverse regression
18.3 | SIR
Jaccard
Similarity
Jacobian
4.3
Jordan decomposition
2.2 | 2.2
kernel densities
1.3 | 1.3
kernel estimator
1.3
Kulczynski
Similarity
likelihood function
6.1
likelihood ratio test
7.1
limit theorems
4.5
linear discriminant analysis
Maximum
linear regression
3.4
linear transformation
no title
link function
18.3
loadings
10.1 | 10.1
non-uniqueness
Non-Uniqueness
log-likelihood function
6.1
Mahalanobis distance
Maximum
Mahalanobis transformation
Mahalanobis | 4.4 | 4.4
marginal densities
4.1
marketing strategies
3.5
maximum likelihood discriminant rule
Maximum
maximum likelihood estimator
6.1
MDS direction
15.1
mean-variance
17. | 17.2
median
1.1 | 18.1
metric methods
15.1
moments
4.2 | no title
multidimentional scaling
15.
multinormal
Geometry | 5. | 5.
multinormal distribution
4.4
multivariate distributions
4.
multivariate median
18.1
multivariate t-distribution
5.4
Nearest Neighbor
Hierarchical
non-metric solution
16.3
Nonexistence of a riskless asset
Nonexistence
nonhomogeneous
Linear
nonmetric methods of MDS
15.1
norm of a vector
Norm
normal distribution
6.1
normalized principal components (NPCs)
9.5
null space
no title
order statistics
1.1
orthogonal complement
Projection
orthogonal matrix
Properties
orthonormed
Subspaces
outliers
1.
outside bars
1.1
parallel coordinates plots
1.7 | 1.7
parallel profiles
Parallel
partitioned covariance matrix
5.1
partitioned matrices
2.5
PAV algorithm
15.3.1 | Nonmetric
pool-adjacent violators algorithm
15.3.1 | Nonmetric
portfolio analysis
17.
portfolio choice
17.1
positive definiteness
Definiteness
positive or negative dependence
1.4
positive semidefinite
3.3
principal components transformation
9.1
principal axes
Distance
principal component method
The
principal components
9.1
principal components analysis (PCA)
9. | 18.3 | The
principal components in practice
9.2
principal components technique
9.2
principal components transformation
9.1
principal factors
The
profile analysis
Profile
profile method
16.2
projection matrix
Projection
projection pursuit
18.2
projection pursuit regression
Projection
projection vector
18.3
proximity between objects
11.2
proximity measure
11.1
quadratic discriminant analysis
Classification
quadratic form
2.3
quadratic forms
no title
quality of the representations
Quality
randomized discriminant rule
Bayes
rank
Rank
reduced model
3.5
rotation
Rotation
rotations
no title
row space
8.1
Russel and Rao (RR)
Similarity
sampling distributions
4.5
scatterplot matrix
1.4
scatterplots
1.4
separation line
1.4
similarity of objects
Similarity
Simple Matching
Similarity
single linkage
Hierarchical
single matching
Similarity
singular normal distribution
Singular
singular value decomposition (SVD)
2.2 | 8.4
sliced inverse regression
18.3 | The | The
algorithm
The
sliced inverse regression II
SIR | SIR | SIR | The | The | The
algorithm
The
solution
nonmetric
Nonmetric
specific factors
10.1
specific variance
10.1
spectral decompositions
2.2
spherical distribution
5.4
standardized linear combinations (SLC)
9.1
statistics
4.5
stimulus
16.2
Student's t-distribution
3.4
sum of squares
3.5
summary statistics
3.3
Swiss bank data
1.1
symmetric matrix
Properties
t-test
3.4
Tanimoto
Similarity
testing
7.
The CAPM
17.4
trace
Trace
trade-off analysis
16.2
transformations
4.3
transpose
Transpose
two factor method
16.2
unbiased estimator
6.2
uncorrelated factors
10.1
unit vector
Norm
upper triangular matrix
Properties
variance explained by PCs
Variance
varimax criterion
Rotation
varimax method
Rotation
varimax rotation method
Rotation
Ward clustering
Hierarchical
Wishart distribution
5.2 | 5.2