Description Usage Arguments Details Value Author(s) References See Also Examples

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

1 | ```
cost_gaussian(x, data_x, data_y, prior_p, prior_st)
``` |

`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 |

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 of the cost function

Han Lin Shang

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.

1 2 | ```
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)
``` |

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