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Last data update: 2014.03.03

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Results 221 - 230 of 182600 found.
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dpoly (Package: quantreg.nonpar) :

Returns or evaluates the first derivatives of orthogonal polynomials of degree 1 to degree over the specified set of points x: the polynomials are all orthogonal to the constant polynomial of degree 0. Alternatively, evaluates the first derivatives of raw polynomials.

● Data Source: CranContrib
● Keywords:
● Alias: dpoly
● 0 images

load.sum (Package: quantreg.nonpar) :

Returns the medians of a vector of ordered factor variables, the modes of a vector of unordered factor variables, and the means of a vector of numeric variables.

● Data Source: CranContrib
● Keywords:
● Alias: load.sum
● 0 images

quantreg.nonpar-package (Package: quantreg.nonpar) :

Implements the nonparametric quantile regression methods developed by Belloni, Chernozhukov, and Fernandez-Val (2011) to partially linear quantile models. Provides point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. Provides pointwise and uniform confidence intervals using analytic and resampling methods.

● Data Source: CranContrib
● Keywords: htest, nonparametric, regression
● Alias: quantreg.nonpar, quantreg.nonpar-package
● 0 images

no.process (Package: quantreg.nonpar) :

A method for the generic function npqr. It computes the quantile regression fits without performing inference

● Data Source: CranContrib
● Keywords: nonparametric, regression
● Alias: no.process
● 0 images

formulaDeriv (Package: quantreg.nonpar) :

Takes the symbolic derivative (or multiple derivatives) of the right hand side of a formula and returns a matrix with the derivative evaluated at each observation in a dataset

● Data Source: CranContrib
● Keywords:
● Alias: formulaDeriv
● 0 images

gbootstrap (Package: quantreg.nonpar) :

A method for the generic function npqr. It computes, via a gradient bootstrap method, the t-statistic used to conduct inference in nonparametric series quantile regression models, as well as outputting confidence intervals and hypothesis test p-values at a user-specified level.

● Data Source: CranContrib
● Keywords: nonparametric, regression
● Alias: gbootstrap
● 0 images

npqr (Package: quantreg.nonpar) :

Implements the nonparametric quantile regression methods developed by Belloni, Chernozhukov, and Fernandez-Val (2011) to partially linear quantile models, Y=g(w,u)+v'γ(u), u|v,w~U[0,1]. Provides point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model, g(w,u), approximated by Z(w)'β(u). Provides pointwise and uniform confidence intervals using analytic and resampling methods.

● Data Source: CranContrib
● Keywords: htest, nonparametric, regression
● Alias: npqr
● 0 images

pivotal (Package: quantreg.nonpar) :

A method for the generic function npqr. It computes, via a pivotal method, the t-statistic used to conduct inference in nonparametric series quantile regression models, as well as outputting confidence intervals and hypothesis test p-values at a user-specified level.

● Data Source: CranContrib
● Keywords: nonparametric, regression
● Alias: pivotal
● 0 images

wbootstrap (Package: quantreg.nonpar) :

A method for the generic function npqr. It computes, via a weighted bootstrap method, the t-statistic used to conduct inference in nonparametric series quantile regression models, as well as outputting confidence intervals and hypothesis test p-values at a user-specified level.

● Data Source: CranContrib
● Keywords: nonparametric, regression
● Alias: wbootstrap
● 0 images

ddpoly (Package: quantreg.nonpar) :

Returns or evaluates the second derivatives of orthogonal polynomials of degree 1 to degree over the specified set of points x: the polynomials are all orthogonal to the constant polynomial of degree 0. Alternatively, evaluates the second derivatives of raw polynomials.

● Data Source: CranContrib
● Keywords:
● Alias: ddpoly
● 0 images