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bbemkr (version 1.4)

warmup: 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(x, inicost, mutsizp, warm = 100, prob = 0.234, data_x, data_y)

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

Value

  • xLog of square bandwidths
  • sigma2Estimate of normal error variance
  • costCost value
  • mutsizplastFinal step size of random-walk Metropolis algorithm
  • mutsizpStep size of random-walk Metropolis algroithm

See Also

mcmcrecord, logdensity, loglikelihood, logpriors