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

smoother

approx
auxiliary quantlet for denreg; does linear interpolation
asympcheckit
auxiliary quantlet for denreg
binlindata
linear binning for univariate data, given the binwidth and optionally the origin of the bin grid. The smallest grid with width d that covers the data is found and the data are binned to this grid using the linear binning rule.
binweights
direct computation of the autocovariances of the bincounts needed for fast computation of the kernel estimates of the integrated squared density derivatives.
canbw
computes canonical bandwidth and squared L2 norm for a univariate kernel function K.
canker
does the canonical bandwidth transformation of a bandwidth value of kernel K1 into an equivalent bandwidth for kernel K2.
dcdenest
estimates a univariate density of a random variable that is convoluted with a Gaussian random variable. The estimation is based on deconvolution by inverting the characteristic function.
denbbwcrit
This is a variant of denxbwcrit and denbwcrit using linear binning for fast computation. All kernel estimates of the integrated squared densities or density derivatives are computed approximately by Rdenbest which uses linear binning.
denbwcrit
determines from a range of bandwidths the optimal one using 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
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.
denreg
Probability density estimation for the noise in a nonparametric regression model using the taut string method.
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.
denxbwcrit
an exact variant of denbwcrit. All kernel estimates of the integrated squared densities or density derivatives are computed exactly using Rdenxest.
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.
dlogspline
logspline density - auxiliary quantlet for logspline density estimation
EBBS
automatic bandwidth selection for univariate and multivariate local polynomial regression. It also fits a smooth curve to (x,y) using the obtained local bandwidths.
EBBSmain
automatic bandwidth selection for univariate and multivariate local polynomial regression. It also fits a smooth curve to (x,y) using the resulting local bandwidths.
FDApca
Carries out a penalized functional principal component analysis (PCA) based on the coefficient matrix for functional data. It is possible to choose a smoothing parameter objectively.
gasmregx
calculates the Gasser-Mueller estimator using quartic kernel
gasmregxb
calculates the Gasser-Mueller estimator using quartic boundary kernel
gauder
gauder evaluates derivatives of the Gaussian kernel rescaled by a bandwidth h, to be used for density estimation bandwidth selection.
locpolex
computes the local polynomial estimator with an arbitrary kernel.
logsplinefit
estimates density function using splines
logsplinesummary
summarizes the results of estimation of quantlet logsplinefit
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.
lplocband
Estimates the derivative of a regression function (including the 0th derivative) by local polynomial fits on a grid. This quantlet can be used for univariate or multivariate regression estimation.
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 linear kernel regression. The computation uses WARPing.
lregxestp
estimates a multivariate regression function using local linear kernel regression with Quartic kernel.
lsdcheckfit
auxiliary quantlet for logspline density estimation - checks if list "fit" is compatible with output of logsplinefit quantlet
lvtest
This quantlet tests for significance of a subset or of the whole set of continuous regresssors in a nonparametric regression.
multiwdwr
auxiliary quantlet for pmreg
plogspline
probabilities from a logspline density - auxiliary quantlet for logspline density estimation
plotlogspline
produces a plot of a logspline fit at n equally spaced points covering the support of the density.
pmreg
Estimation of the regression function in a nonparametric regression model using the taut string method.
qlogspline
quantiles from a logspline density - auxiliary quantlet for logspline density estimation
Rdenbest
evaluates a kernel estimate of an integrated squared density (derivative) using the normal kernel for a (vector of) bandwidth(s) h. This quantlet is a variation of Rdenxest and uses linearly prebinned data for faster computation.
Rdenxest
evaluates a kernel estimate of an integrated squared density (derivative) using the normal kernel for a (vector of) bandwidth(s) h.
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
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 a pre-specified confidence level for univariate regression using the Nadaraya-Watson estimator.
regxci
computes pointwise confidence intervals with a pre-specified 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.
rlogspline
random samples from a logspline density - auxiliary quantlet for logspline density estimation
smoothermain
loads the kernels needed by the smoother lib functions
smoothertest
smoothertest tests all the aforementioned quantlets of the smoother.lib
spfill
spfill fills places of sparsity with interpolated observations to avoid the need of oversmoothing.
supsmo
calculates the super smoother
tautstring
auxiliary quantlet for denreg and pmreg, core of taut string method

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, 05.02.2006Impressum