warmup_gaussian {bbemkr}R Documentation

Burn-in period

Description

By minimizing the cost value, the function estimates the bandwidths of the regressors and normal error variance parameter for the burn-in period

Usage

warmup_gaussian(x, inicost, mutsizp, warm = 100, prob = 0.234, data_x, data_y,
       prior_p = 2, prior_st = 1)

Arguments

x

Log of square bandwidths

inicost

Cost value

mutsizp

Step size of random-walk Metropolis algorithm

warm

Number of burn-in iterations

prob

Optimal covergence rate of random-walk Metropolis algorithm

data_x

Regressors

data_y

Response variable

prior_p

Hyperparameter of the inverse-gamma prior

prior_st

Hyperparameter of the inverse-gamma prior

Value

x

Log of square bandwidths

sigma2

Estimate of normal error variance

cost

Cost value

mutsizplast

Final step size of random-walk Metropolis algorithm

mutsizp

Step size of random-walk Metropolis algorithm

Author(s)

Han Lin Shang

See Also

mcmcrecord_gaussian, logdensity_gaussian, loglikelihood_gaussian, logpriors_gaussian


[Package bbemkr version 2.0 Index]