To minimize the cost value, it estimates the bandwidths of the regressors and variance parameter of the error density for the warmup or burn-in period.
Usage
bbewarmup(x, costpara, warm = 4, sizep = 2)
Arguments
x
Log bandwidths of the regressors
costpara
Initial cost value
warm
Number of iterations in the warmup or burn-in period
sizep
Tuning parameter used in the random-walk Metropolis algorithm
Value
xhLog bandwidths of the regressors, after the warmup period
xhtestLog bandwidths of the regressors, during the warmup period
sigmaVariance of the error density, after the warmup period
sigmatestVariance of the error density, during the warmup period
costCost value, after the warmup period
costtestCost value, during the warmup period
accept_rateAcceptance rate of the random-walk Metropolis algorithm