- additive model
- see AM
| 5.1.3
| 8.
- backfitting
- 8.1
- bandwidth choice
- 8.3.1
- derivative
- 8.2.2
- equivalent kernel weights
- 8.3.3
- finite sample behavior
- 8.3
- hypotheses testing
- 9.4
- interaction terms
- 8.2.3
- marginal effect
- 8.2.1
- marginal integration
- 8.2
- MASE
- 8.3.2
- additive partial linear model
- see APLM
- ADE
- 6.2
| 6.2.3
- AMISE
- histogram
- 2.2.4
- kernel density estimation
- 3.2.4
- kernel regression
- 4.1.2.3
- AMSE
- local polynomial regression
- 4.1.3
- APLM
- 9.1
- ASH
- 2.4
- asymptotic MISE
- see AMISE
- asymptotic MSE
- see AMSE
- asymptotic properties
- histogram
- 2.2
- kernel density estimation
- 3.2
- average derivative estimator
- see ADE
- average shifted histogram
- see ASH
- averaged squared error
- see ASE
- backfitting
- 8.1
- classical
- 8.1.1
- GAM
- 9.2.1
- GAPLM
- 9.3.1
- GPLM
- 7.2.3
- local scoring
- 9.2.1
- modified
- 8.1.2
- smoothed
- 8.1.3
- bandwidth
- canonical
- 3.4.1
- kernel density estimation
- 3.1.2
- rule of thumb
- 3.3.1
- bandwidth choice
- additive model
- 8.3.1
- kernel density estimation
- 3.3
| 3.3.4
- kernel regression
- 4.3
- Silverman's rule of thumb
- 3.3.1
- bias
- histogram
- 2.2.1
- kernel density estimation
- 3.2.1
- kernel regression
- 4.1.2.3
- multivariate density estimation
- 3.6.1
- multivariate regression
- 4.5.1
- bin
- 2.1.1
- binary response
- 1.2.3
| 5.1
- binwidth
- 2.1.1
| 2.1.3
- optimal choice
- 2.2.5
- rule of thumb
- 2.2.5
- canonical bandwidth
- 3.4.1
- canonical kernel
- 3.4.1
- canonical link function
- 5.2.2
- CHARN model
- 4.4.2
- conditional expectation
- 1.2.3
| 4.1.1.1
- conditional heteroscedastic autoregressive nonlinear
- see CHARN
- confidence bands
- kernel density estimation
- 3.5
- kernel regression
- 4.4.2
- confidence intervals
- kernel density estimation
- 3.5
- kernel regression
- 4.4.1
- cross-validation
- see CV
- bandwidth choice
- 4.3
- biased
- 3.6.3
- kernel density estimation
- 3.3.2
- kernel regression
- 4.3.2
- multivariate density estimation
- 3.6.2.2
- pseudo-likelihood
- 3.6.3
- smoothed
- 3.6.3
- curse of dimensionality
- 1.2
| 4.5.2
| 5.
| 6.
| 8.1.1
| 8.3.3
- density estimation
- 1.1
- histogram
- 2.
- kernel estimation
- 3.1.2
- nonparametric
- 1.1
| 3.
- derivative estimation
- additive function
- 8.2.2
- regression
- 4.1.3
- design
- fixed
- 4.1.1.2
- random
- 4.1.1.2
- deviance
- 5.2.3
- dimension reduction
- 5.1
- Engel curve
- 1.2.2
- equivalent kernel
- 3.4.1
- equivalent kernel weights
- 8.3.3
- explanatory variable
- 1.2
- exponential family
- 5.2.1
- finite prediction error
- 4.3.3
- Fisher scoring algorithm
- 5.2.3
- fixed design
- 4.1.1.2
| 4.1.2.2
- Gasser-Müller estimator
- 4.1.2.2
- Fourier coefficients
- 4.2.4
- Fourier series
- 4.2.4
- frequency polygon
- 2.4
- GAM
- 5.1.3
| 9.
| 9.2
- backfitting
- 9.2.1
- hypotheses testing
- 9.4
- marginal integration
- 9.2.2
- GAPLM
- 5.1.3
| 9.3
- backfitting
- 9.3.1
- hypotheses testing
- 9.4
- marginal integration
- 9.3.2
- Gasser-Müller estimator
- 4.1.2.2
- Gauss-Seidel algorithm
- 8.1
- generalized additive model
- see GAM
- generalized additive partial linear model
- see GAPLM
- generalized cross-validation
- 4.3.3
- generalized linear model
- see GLM
- generalized partial linear model
- see GPLM
- approximate LR test
- 7.3.1
- modified LR test
- 7.3.2
- GLM
- 5.2
- estimation
- 5.2.3
- exponential family
- 5.2.1
- Fisher scoring
- 5.2.3
- hypotheses testing
- 5.2.3
- IRLS
- 5.2.3
- link function
- 5.2.2
- Newton-Raphson
- 5.2.3
- GPLM
- 5.1.3
| 7.
- backfitting
- 7.2.3
- hypotheses testing
- 7.3
- profile likelihood
- 7.2.1
- Speckman estimator
- 7.2.2
- gradient
- 3.6.1
- Hessian matrix
- 3.6.1
- histogram
- 2.
- ASH
- 2.4
- asymptotic properties
- 2.2
- bias
- 2.2.1
- binwidth choice
- 2.2.5
- construction
- 2.1.1
- dependence on binwidth
- 2.1.3
- dependence on origin
- 2.3
- derivation
- 2.1.2
- MSE
- 2.2.3
- variance
- 2.2.2
- hypotheses testing
- GPLM
- 7.3
- regression
- 4.4
- i.i.d
- 2.1
- identification
- 5.2.3
- AM
- 8.1.1
- SIM
- 6.
- independent and identically distributed
- see i.i.d.
- index
- 1.2.3
| 5.1.3
| 6.
- semiparametric
- 5.1.3
- integrated squared error
- see ISE
- interaction terms
- 8.2.3
- IRLS
- 5.2.3
- iteratively reweighted least squares
- see IRLS
- kernel density estimation
- 3.
- as a sum of bumps
- 3.1.5
- asymptotic properties
- 3.2
- bandwidth choice
- 3.3.4
- bias
- 3.2.1
- confidence bands
- 3.5
- confidence intervals
- 3.5
- dependence on bandwidth
- 3.1.3
- dependence on kernel
- 3.1.4
- derivation
- 3.1.2
- multivariate
- 3.6
- multivariate rule-of-thumb bandwidth
- 3.6.2.1
- optimal bandwidth
- 3.2.4
- rule-of-thumb bandwidth
- 3.3.1
- variance
- 3.2.2
- kernel function
- 3.1.2
- canonical
- 3.4.1
| 3.4.1
- efficiency
- 3.4.3
- equivalent
- 3.4.1
- kernel regression
- 4.1.2
- bandwidth choice
- 4.3
- bias
- 4.1.2.3
- confidence bands
- 4.4.2
- confidence intervals
- 4.4.1
- cross-validation
- 4.3.2
- fixed design
- 4.1.2.2
- Nadaraya-Watson estimator
- 4.1.2.1
- penalizing functions
- 4.3.3
- random design
- 4.1.2.1
- statistical properties
- 4.1.2.3
- univariate
- 4.1.2
- variance
- 4.1.2.3
-NN
- see
-nearest-neighbor
- least squares
- see LS
- likelihood ratio
- see LR
- linear regression
- 1.2
- link function
- 1.2.3
| 5.1
| 5.2.2
- canonical
- 5.2.2
- nonparametric
- 5.1.2
- power function
- 5.2.2
- local constant
- 4.1.3
- local linear
- 4.1.3
- local polynomial
- derivative estimation
- 4.1.3
- regression
- 4.1.3
| 4.1.3
- local scoring
- 9.2.1
- log-likelihood
- GLM
- 5.2.1
- pseudo likelihood
- 6.2.2
- quasi-likelihood
- 5.2.3
- marginal effect
- 8.2.1
- derivative
- 8.2.2
- marginal integration
- 8.2
- GAM
- 9.2.2
- GAPLM
- 9.3.2
- maximum likelihood
- see ML
- maximum likelihood estimator
- see MLE
- mean averaged squared error
- see MASE
- mean integrated squared error
- see MISE
- mean squared error
- see MSE
- median smoothing
- 4.2.2
- MISE
- histogram
- 2.2.4
- kernel density estimation
- 3.2.4
- regression
- 4.3
- ML
- 5.2.1
| 5.2.3
- MLE
- 5.2.1
| 5.2.3
- MSE
- histogram
- 2.2.3
- kernel density estimation
- 3.2.3
- multivariate density estimation
- 3.6
| 3.6
- bias
- 3.6.1
- computation
- 3.6.3
- graphical representation
- 3.6.3
- variance
- 3.6.1
- multivariate regression
- 4.5
- asymptotics
- 4.5.1
- bias
- 4.5.1
- computation
- 4.5.2
- curse of dimensionality
- 4.5.2
- variance
- 4.5.1
- Nadaraya-Watson estimator
- 4.1.2.1
-nearest-neighbor
- 4.2.1
| 4.2.1
| 4.2.1
- Newton-Raphson algorithm
- GLM
- 5.2.3
- nonparametric regression
- 4.
- multivariate
- 4.5
- univariate
- 4.1
- origin
- 2.1.1
- orthogonal series
- Fourier series
- 4.2.4
- orthogonal series regression
- 4.2.4
- orthonormality
- 4.2.4
- partial linear model
- see PLM
- pdf
- 1.1
| 1.1
| 2.1
| 3.1.1
- multivariate
- 3.6
- penalizing functions
- 4.3.3
- Akaike's information criterion
- 4.3.3
- finite prediction error
- 4.3.3
- generalized cross-validation
- 4.3.3
- Rice's
- 4.3.3
- Shibata' s model selector
- 4.3.3
- penalty term
- bandwidth choice
- 4.3
- spline
- 4.2.3
- spline smoothing
- 4.2.3
- PLM
- 5.1.3
| 7.1
- estimation
- 7.2
- plug-in method
- 3.3.3
- refined
- 3.6.3
- Silverman's rule of thumb
- 3.3.1
- PMLE
- 6.2
- probability density function
- see pdf
- profile likelihood
- 7.2.1
- pseudo likelihood
- 6.2.2
- pseudo maximum likelihood estimator
- see PMLE
- quasi-likelihood
- 5.2.3
- random design
- 4.1.1.2
| 4.1.2.1
| 4.1.2.3
- regression
- 1.2
- conditional expectation
- 4.1.1.1
- confidence bands
- 4.4
- confidence intervals
- 4.4
- fixed design
- 4.1.1.2
| 4.1.2.3
- generalized
- 5.
- hypotheses testing
- 4.4
- kernel regression
- 4.1.2
- linear
- 1.2
| 1.2.1
- local polynomial
- 4.1.3
- median smoothing
- 4.2.2
-nearest-neighbor
- 4.2.1
- nonparametric
- 1.2.2
| 4.
- nonparametric univariate
- 4.1
- orthogonal series
- 4.2.4
- parametric
- 1.2.1
- random design
- 4.1.1.2
| 4.1.2.3
- semiparametric
- 1.2.3
| 5.
- spline smoothing
- 4.2.3
- residual sum of squares
- see RSS
- RSS
- 4.2.3
- rule of thumb
- histogram
- 2.2.5
- kernel density estimation
- 3.3.1
- multivariate density estimation
- 3.6.2.1
- semiparametric least squares
- see SLS
- Shibata' s model selector
- 4.3.3
- Silverman's rule of thumb
- 3.3.1
- SIM
- 6.
- estimation
- 6.2
- hypotheses testing
- 6.3
- identification
- 6.1
- PMLE
- 6.2.2
- SLS
- 6.2.1
- WADE
- 6.2.3
- single index model
- see SIM
- SLS
- 6.2
- smoothing spline
- 4.2.3
- Speckman estimator
- 7.1
| 7.2.2
- spline kernel
- 4.2.3
- spline smoothing
- 4.2.3
- subset selection
- 5.1.1
- Taylor expansion
- first order
- 2.2.1
- multivariate
- 3.6.1
- test
- AM, GAM, GAPLM
- 9.4
- approximate LR test
- 7.3.1
- LR test
- 5.2.3
- modified LR test
- 7.3.2
- SIM
- 6.3
- time series
- nonparametric
- 4.4.2
- variable selection
- 5.1.1
- variance
- histogram
- 2.2.2
- kernel density estimation
- 3.2.2
- kernel regression
- 4.1.2.3
- multivariate density estimation
- 3.6.1
- multivariate regression
- 4.5.1
- WADE
- 6.2
| 6.2.3
- wage equation
- 1.2
- WARPing
- 2.4
- wavelets
- 4.2.4
- weighted average derivative estimator
- see WADE
- weighted semiparametric least squares
- see WSLS
- XploRe
- Preface