cost_admkr: Negative of log posterior associated with the bandwidths

In bbemkr: Bayesian bandwidth estimation for multivariate kernel regression with Gaussian error

Description

Calculates the negative of log posterior, using the leave-one-out cross validated samples.

Usage

cost_admkr(x, data_x, data_y)

Arguments

x	Log of square bandwidths
data_x	Regressors
data_y	Response variable

Details

Bandwidth can be re-parameterized by a constant time optimal convergence rate, that is, h = c*n^{rate}.

Value

Value of the cost function

Author(s)

Han Lin Shang

References

H. L. Shang (2013) Bayesian bandwidth estimation for a nonparametric functional regression model with unknown error density, Computational Statistics and Data Analysis, 67, 185-198.

X. Zhang, M. L. King and H. L. Shang (2013). A sampling algorithm for bandwidth estimation in a nonparametric regression model with a flexible error density. Working paper, http://users.monash.edu.au/~xzhang/zhang.king.shang.2013.pdf

X. Zhang, M. L. King and H. L. Shang (2013). Bayesian bandwidth selection for a nonparametric regression model with mixed types of regressors. Working paper, http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2013/wp13-13.pdf

X. Zhang and M. L. King (2013). Gaussian kernel GARCH models. Working paper, http://users.monash.edu.au/~xzhang/zhang.king.2013.rev.pdf

See Also

gibbs_admkr_nw, gibbs_admkr_erro

Examples

x = log(c(nrr(data_x, FALSE),2)^2)
inicost = cost_admkr(x, data_x = data_x, data_y = data_ynorm)

bbemkr documentation built on May 1, 2019, 10:53 p.m.