cost: 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_gaussian(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

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.

Value

Value of the cost function

Author(s)

Han Lin Shang

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.

See Also

np_gibbs, cost2_gaussian

Examples

x = log(nrr(data_x, FALSE)^2)
inicost = cost_gaussian(x, data_x = data_x, data_y = data_ynorm, prior_p = 2, prior_st = 1)

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