Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library: smoother

canbw does the canonical bandwith transformation of a bandwith value of kernel K1 into an equivalent bandwidth for Kernel K2.
canker does the canonical bandwith transformation of a bandwith value of kernel K1 into an equivalent bandwidth for Kernel K2.
denbwcrit determines the optimal from a range of bandwidths by one of the following bandwidth selection criteria: Least Squares Cross Validation (lscv), Biased Cross Validation (bcv), Smoothed Cross Validation (scv), Jones, Marron and Park Cross Validation (jmp), Park and Marron Plug-in (pm), Sheather and Jones Plug-in (sj), and Silverman's rule of thumb.
denbwsel interactive tool for bandwidth selection in univariate kernel density estimation.
dencb computes uniform confidence bands with prespecified confidence level for univariate density estimation.
denci computes pointwise confidence intervals with prespecified confidence level for univariate density estimation. The computation uses WARPing.
denest estimates a univariate density by kernel density estimation. The computation uses WARPing.
denestp estimates a p-dimensional density by kernel density estimation. The computation uses WARPing.
denrot determines a rule-of-thumb bandwidth for univariate density estimation according to Silverman.
denrotp determines a rule-of-thumb bandwidth for multivariate density estimation according to Scott.
denxcb computes uniform confidence bands with prespecified confidence level for univariate density estimation.
denxci computes pointwise confidence intervals with prespecified confidence level for univariate density estimation.
denxest estimates a univariate density by kernel density estimation.
denxestp estimates a p-dimensional density by kernel density estimation. The computation uses WARPing.
gauder gauder evaluates derivatives of the Gaussian kernel rescaled by a bandwidth h, to be used for density estimation bandwidth selection.
looreg computes the Nadaraya-Watson leave-one-out estimator without binning using the quartic kernel. Prior to estimation, looreg sorts the data. The sorted data, along with the sorted leave-one-out regression estimates, are returned as an output.
lpderest estimates the q-th derivative of a regression function using local polynomial kernel regression. The computation uses WARPing.
lpderrot determines a rule-of-thumb bandwidth for univariate local polynomial derivatives estimation using the Quartic kernel.
lpderxest estimates the q-th derivative of a regression function using local polynomial kernel regression with Quartic kernel.
lpregest estimates a regression function using local polynomial kernel regression. The computation uses WARPing.
lpregrot determines a rule-of-thumb bandwidth for univariate local polynomial kernel regression using the Quartic kernel.
lpregxest estimates a univariate regression function using local polynomial kernel regression with Quartic kernel.
lprotint lprotint computes the integral of the (p+1)st derivative of a polynomial of order (p+3), this function is used to find rule-of-thumb bandwidth for local polynomial regression and derivative estimation
lregestp estimates a multivariate regression function using local polynomial kernel regression. The computation uses WARPing.
lregxestp estimates a multivariate regression function using local polynomial kernel regression with Quartic kernel.
lvtest This quantlet tests for significance of a subset or of the whole set of continuous regresssors in a nonparametric regression.
regbwcrit determines the optimal from a range of bandwidths by one using the resubstitution estimator with one of the following penalty functions: Shibata's penalty function (shi), Generalized Cross Validation (gcv), Akaike's Information Criterion (aic), Finite Prediction Error (fpe), Rice's T function (rice). The computation uses WARPing.
regbwsel interactive tool for bandwidth selection in univariate kernel regression estimation.
regcb computes uniform confidence bands with prespecified confidence level for univariate regression using the Nadaraya-Watson estimator. The computation uses WARPing.
regci computes pointwise confidence intervals with prespecified confidence level for univariate regression using the Nadaraya-Watson estimator. The computation uses WARPing.
regest computes the Nadaraya-Watson estimator for univariate regression. The computation uses WARPing.
regestp Nadaraya-Watson estimator for multivariate regression. The computation uses WARPing.
regxbwcrit determines the optimal from a range of bandwidths by one using the resubstitution estimator with one of the following penalty functions: Shibata's penalty function (shi), Generalized Cross Validation (gcv), Akaike's Information Criterion (aic), Finite Prediction Error (fpe), Rice's T function (rice).
regxbwsel interactive tool for bandwidth selection in univariate kernel regression estimation.
regxcb computes uniform confidence bands with prespecified confidence level for univariate regression using the Nadaraya-Watson estimator.
regxci computes pointwise confidence intervals with prespecified confidence level for univariate regression using the Nadaraya-Watson estimator.
regxest computes the Nadaraya-Watson estimator for univariate regression.
regxestp computes the Nadaraya-Watson estimator for multivariate regression.
smoothermain loads the kernels needed by the smoother lib functions
smoothertest smoothertest tests all the aforementioned macros of the smoother.lib
spfill spfill fills places of sparsity with interpolated observations to avoid the need of oversmoothing.

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

(C) MD*TECH Method and Data Technologies, 28.6.1999