cost: Negative of log posterior associated with the bandwidths
Description
Calculates the negative of log posterior, using the leave-one-out cross validated samples.
Usage
cost(x, data_x, data_y, prior_p, prior_st)
Arguments
x
Log of square bandwidths
data_x
Regressors
data_y
Response variable
prior_p
A tuning parameter of the prior of error variance, following inverse gamma distribution
prior_st
Another tuning parameter of the prior of error variance, following inverse gamma distribution
Value
Value of the cost function
Details
Bandwidth can be re-parameterized by a constant times optimal convergence rate, that is, $h=c*n^{rate}$. The prior of $c^2$ is
assumed to follow an inverse-gamma prior with hyperparameters prior_p = 2 and prior_st = 1.
References
X. Zhang and R.D. Brooks and M.L. King (2009), A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation, Journal of Econometrics, 153, 21-32.