bbemkr (version 1.6)

warmup_admkr: Burn-in period

Description

By minimising the cost value, the function estimates the bandwidths of the regressors and kernel-form error density for the burn-in period

Usage

warmup_admkr(x, inicost, mutsizp, errorsizp, warm = 100, prob = 0.234, 
    errorprob = 0.44, data_x, data_y)

Arguments

x

Log of square bandwidths

inicost

Cost value

mutsizp

Step size of random-walk Metropolis algorithm for the regressors

errorsizp

Step size of random-walk Metropolis algorithm for the kernel-form error density

warm

Number of burn-in iterations

prob

Optimal covergence rate of random-walk Metropolis algorithm for the regressors

errorprob

Optimal covergence rate of random-walk Metropolis algorithm for the kernel-form error density

data_x

Regressors

data_y

Response variable

Value

• x
  Log of square bandwidths
• cost
  Cost value
• mutsizp
  Step size of random-walk Metropolis algorithm for the regressors
• errorsizp
  Step size of random-walk Metropolis algorithm for the kernel-form error density

See Also

mcmcrecord_admkr, logdensity_admkr, loglikelihood_admkr, logpriors_admkr