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bbefkr (version 4.2)

Bayesian bandwidth estimation and semi-metric selection for the functional kernel regression with unknown error density

Description

Estimating optimal bandwidths for the regression mean function approximated by the functional Nadaraya-Watson estimator and the error density approximated by a kernel density of residuals simultaneously in a scalar-on-function regression. As a by-product of Markov chain Monte Carlo, the optimal choice of semi-metric is selected based on largest marginal likelihood.

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Version

Install

install.packages('bbefkr')

Monthly Downloads

65

Version

4.2

License

GPL (>= 2)

Maintainer

Shang H

Last Published

April 29th, 2014

Functions in bbefkr (4.2)

SIF

Simulation inefficiency factor
bayMCMC_semi_local

Bayesian bandwidth estimation for a semi-functional partial linear model
bayMCMC_semi_global

Bayesian bandwidth estimation for a semi-functional partial linear model
loglikelihood_global_admkr

Compute the marginal likelihood using Chib's (1995) method
bayMCMC_np_local

Bayesian bandwidth estimation for a functional nonparametric regression with homoscedastic errors
funopare.kernel

Functional Nadaraya-Watson estimator
error.den

Compute the probability density function and cumulative probability density function of the error, using a global bandwidth of residuals
Xvar

Simulated real-valued predictors in the semi-functional partial linear model
error.denadj

Compute the probability density function and cumulative probability density function of error, using localised bandwidths of residuals
logpriors_admkr

Compute the marginal likelihood using Chib's (1995) method
simcurve_smooth_normerr

Simulated data set
logpriorh2

Prior density of the squared bandwidth parameters
bayMCMC_np_global

Bayesian bandwidth estimation for a functional nonparametric regression with homoscedastic errors
logdensity_admkr

Compute the marginal likelihood using Chib's (1995) method
specurves

Spectroscopy tecator data
simulate_error

Simulate errors
bbefkr-package

Bayesian bandwidth estimation for the functional kernel regression with unknown error density