- absolute regularity condition
- 16.1.1
- additive outliers
- 18.1.1.2
- AFPE
- 16.2.2
- agglom algorithm
- 9.2.1
-mixing
- 16.1.1
- ANOVA
- 8.1
- ASE
- 16.1.2
- ASEP
- 16.1.2
- asymptotic final prediction error
- see AFPE
- asymptotic mean squared error
- 16.2.2
- asymptotic MISE
- 16.2.2
- average squared error
- see ASE
- of prediction
- see ASEP
- backfitting
- GAM
- 7.1.3
- GPLM
- 6.1.2.0.3
- bandwidth choice
- 16.2.2
- bandwidth selection
- 8.2.2
| 16.1.2
| 16.1.2
| 16.2.2
| 16.2.2
- cross-validation
- 16.1.2
- Silverman's rule-of-thumb
- 16.2.2
- Bera-Jarque test
- 16.1.3
- Berkson error
- 3.2
-mixing
- 16.1.1
- biplots
- correspondence analysis
- 13.3.6
- breakdown point
- 2.1.2
- CAFPE
- 16.2.2
- CART
- 10.
- density estimation
- 10.5.3
- example
- 10.5.1
- growing the tree
- 10.1
- plotting the result
- 10.4
- pruning the tree
- 10.2
- selecting the final tree
- 10.3
- censoring
- 5.1
- classification and regression trees
- see CART
- cluster analysis
- 9.
- average linkage method
- 9.2.1.3
- centroid method
- 9.2.1.4
- complete linkage method
- 9.2.1.2
- hierarchical
- 9.2
- agglomerative
- 9.2.1
- divisive
- 9.2.2
- median method
- 9.2.1.5
- nonhierarchical
- 9.3
- adaptive K-means
- 9.3.2
- fuzzy C-means
- 9.3.4
- hard C-means
- 9.3.3
- K-means
- 9.3.1
- similarity of objects
- 9.1.2
- single linkage method
- 9.2.1.1
- ward method
- 9.2.1.6
- compare two
- 9.3.4
- computation
- Nadarya-Watson estimates
- 16.1.1
- confidence intervals
- Nadaraya-Watson estimator
- 16.1.4
- constraints
- GPLM
- 6.4.3
- contingency table
- 13.1
- controlled-variable model
- 3.2
- correspondence analysis
- 13.
- biplots
- 13.3.6
- XploRe implementation
- 13.2
| 13.3.2
- Cox regression
- 5.3
- hypothesis testing
- 5.3.3
- credit scoring
- GPLM
- 6.2.2
- cross-validation
- 10.3
| 16.2.2
- curse of dimensionality
- 16.2.1
- data preparation
- multiple time series
- 17.1.1
- density estimation
- CART
- 10.5.3
- derivative estimation
- 16.1.5
- diagnostics
- flexible time series
- 16.1.3
- distance
- 9.1.1
- Euclidean
- 9.1.1
- Mahalanobis
- 9.1.1
- maximum
- 9.1.1
- distance measures
- 9.1.1
- DPLS
- 11.
- computing
- 11.3
- example
- 11.4
- overview
- 11.1
- theory
- 11.2
- dynamic partial least squares
- see DPLS
- EIV
- 3.
- calculation
- 3.3.3
- linear eiv models
- 3.1
- nonlinear eiv models
- 3.2
- partially linear eiv models
- 3.3
- regression calibration
- 3.2.1
- simulation extrapolation
- 3.2.2
- variance of error known
- 3.3.1
- variance of error unknown
- 3.3.2
- vector of explanatory variables
- 3.1.2
- endogenous variable
- 4.1
- error
- asymptotic final prediction
- see AFPE
- asymptotic mean squared
- 16.2.2
- average squared
- see ASE
- of prediction
- see ASEP
- corrected asymptotid final prediction
- see CAFPE
- final prediction
- see FPE
- integrated squared
- see ISE
- mean integrated square
- asymptotic
- 16.2.2
- mean integrated squared
- see MISE
- error model
- 3.2
- errors in variables
- see EIV
- estimate
- leave-one-out cross-validation
- 16.1.2
- estimation
- simultaneous-equations
- 4.2
- estimator
- local linear
- 16.1.1
- local quadratic
- 16.1.5
- exogenous regressor
- 4.1
- ExploRing Persistence
- 15.
-mixing
- 16.1.1
- final prediction error
- see FPE
- financial time series
- 15.
- flexible time series
- 16.
| 16.2
- bandwidth choice
- 16.2.2
- bandwidth selection
- 16.1.2
- confidence intervals
- 16.1.4
- derivative estimation
- 16.1.5
- diagnostics
- 16.1.3
| 16.2.3
- plot
- 16.2.3
- selection of lags
- 16.2.2
- FPE
- 16.2.2
| 16.2.2
- corrected asymptotic
- see CAFPE
- GAM
- 6.1.1
| 7.
| 7.1.1
- backfitting
- 7.1.3
- data preparation
- 7.2
- estimation
- 7.3
| 7.3.4
- interactive
- 7.4
- marginal integration
- 7.1.2
- orthogonal series
- 7.1.4
- testing
- 7.6
- theory
- 7.1
- generalized additive models
- see GAM
- generalized linear model
- 6.
- generalized partial linear models
- see GPLM
- GLM
- 3.2
| 6.
- GPLM
- 6.
| 7.
- backfitting
- 6.1.2.0.3
- estimation
- 6.3
| 6.3.1
- likelihood
- 6.1.2
- models
- 6.3
- output display
- 6.4.7
| 6.5.2
- profile likelihhood
- 6.1.2.0.1
- specification test
- 6.5.3
- Speckman estimator
- 6.1.2.0.2
- grid
- GPLM
- 6.4.2
- growth regression
- 8.
| 8.1
- hazard regression
- 5.
- Cox proportional hazards model
- 5.3
- hypothesis testing
- 5.3.3
- data structure
- 5.1
- Kaplan-Meier estimator
- 5.2
- Hurst coefficient
- 15.2.1
- Hurst exponent
- 14.1
- income distribution
- 8.
| 8.
- innovation outliers
- 18.1.1.3
- integrated squared error
- see ISE
- ISE
- 16.1.2
- Kalman filter
- 18.2
- optimality of
- 18.2.2
- robust
- see robust Kalman filter
- Kaplan-Meier estimator
- 5.2
- kernel density estimation
- multivariate
- 8.2.3
- univariate
- 8.2.2
- least median of squares
- 2.1.2
- least trimmed squares
- see LTS
- leave-one-out cross-validation estimate
- 16.1.2
- likelihood ratio test
- GPLM
- 6.5.3
- link function
- 6.1
- local linear estimator
- 16.1.1
| 16.2.1
- rate of convergence
- 16.2.1
- variance of
- 16.1.4
- local quadratic estimator
- 16.1.5
- long-memory analysis
- 14.
- example
- 15.5
- tests
- 14.2
| 15.3
- long-memory process
- 14.1
- spectrum of
- 14.1
- LTS
- 2.
| 2.1.2
- marginal integration
- GAM
- 7.1.2
- mean integrated squared error
- see MISE
- MISE
- 16.1.2
| 16.2.2
- asymptotic
- 16.2.2
- model
- additive partially linear
- 7.1.1
- additive with interaction
- 7.1.1
- aggregate money demand
- 17.
- dynamic panel data
- 12.
- dynamic partial least squares
- see DPLS
- generalized additive
- see GAM
- generalized linear
- see GLM
| 6.
- generalized partial linear
- see GPLM
- Klein's
- 4.1
- nonlinear autoregressive
- see NAR
- nonlinear time series
- see flexible time series
- partial linear
- 6.1.1
- simultaneous-equations
- see simultaneous-equations model
- vector autoregressive
- 17.
- money-demand system
- 4.3
- multiple time series
- 17.
- analysis in XploRe
- 17.1.2
- data preparation
- 17.1.1
- estimation
- 17.3.2
- plot of
- 17.2.1
- structural analysis
- 17.4
- validation
- 17.3.3
- Nadaraya-Watson estimator
- 16.2.1
- rate of convergence
- 16.2.1
- variance of
- 16.1.4
- Nadarya-Watson estimator
- computation
- 16.1.1
- NAR
- higher order
- 16.2
- neasurement error model
- 3.2
- nonlinear autoregressive model
- see NAR
- nonlinear time series analysis
- see flexible time series
- optional parameters
- GPLM
- 6.4
- orthogonal series
- GAM
- 7.1.4
- outliers
- 18.1.1
- additive
- 18.1.1.2
- innovation
- 18.1.1.3
- other types of
- 18.1.1.4
- output
- GPLM
- 6.4.7
| 6.5.2
- panel data
- 12.
- dynamic panel data model
- 12.4
- fixed effects model
- 12.3
- unit root tests
- 12.5
- plot
- CART
- 10.4
- flexible time series
- 16.2.3
- multiple time series
- 17.2.1
- product kernel
- 16.2.1
- profile likelihhood
- GPLM
- 6.1.2.0.1
- quantile function
- 1.1
- conditional
- 1.2.1
- quantile regression
- 1.
- asymptotic normality
- 1.4.1
- confidence intervals
- 1.4.3
- definition
- 1.2.1
- equivariance
- 1.3.1
- monotonic transformations
- 1.3.2
- rank test
- 1.4.3
- rank test inversion
- 1.4.3
- robustness
- 1.3.3
- statistical inference
- 1.4
- Wald test
- 1.4.2
- quantile regression process
- 1.4.1
- rankscore function
- 1.4.3
- regression tree
- see CART
- rIC filter
- 18.4
- rLS filter
- 18.3
- robust Kalman filter
- 18.
- rIC filter
- 18.4
- rLS filter
- 18.3
- rqfit
- 1.5.1
- rrstest
- 1.5.2
- simultaneous-equations
- computation
- 4.2.5
- estimation
- 4.2
- example
- 4.2.5
| 4.3
- identification
- 4.2.1
- Klein's model
- 4.1
- three-stage least squares
- 4.2.4
- two-stage least squares
- 4.2.3
- simultaneous-equations model
- 4.
- singular value decomposition
- 13.1.1
- specification test
- GPLM
- 6.5.3
- Speckman estimator
- GPLM
- 6.1.2.0.2
- start values
- GPLM
- 6.4.2
- state-space model
- 18.
- statistical characteristics
- GPLM
- 6.5.1
- strong mixing
- 16.1.1
- test
- Bera-Jarque
- 16.1.3
- time series
- absolute regularity condition
- 16.1.1
-mixing
- 16.1.1
- antipersistent
- 14.1
-mixing
- 16.1.1
-mixing
- 16.1.1
- financial
- see financial time series
- flexible
- see flexible time series
- fractionally integrated
- 14.1
| 15.2.2
- long-memory
- 14.1
| 15.1
- multiple
- see multiple time series
- nonlinear
- see flexible time series
- nonstationary
- 14.1
- persistence
- 15.1
- strong mixing
- 16.1.1
- uniform mixing
- 16.1.1
- uncovered interest parity
- 12.
- uniform mixing
- 16.1.1
- WARPing
- 16.1.1
| 16.1.5
| 16.2.1
- weights
- GPLM
- 6.4.3