Parametric bootstrap for computing G-best and d-best PCS. This function is called by the wrapper PCS.boot.par.

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`theta` |
Vector of statistics (or parameters) from which it is desired to select the top t of them |

`T` |
Vector of the number of statistics (or parameters) desired to be selected |

`D` |
Vector of d-best selection parameters |

`G` |
Vector of G-best selection parameters |

`B` |
Bootstrap sample size |

`SDE` |
Standard error of the statistics theta (row-wise) |

`dist` |
Distributional assumption used for estimating PCS |

`df` |
Common degrees of freedom for one of the t-statistics in theta; the parameter is only used if dist="t" |

`trunc` |
Number of standard errors below the minimum selected population to disregard in the estimation of PCS; it is a truncation parameter to decrease run time |

`est.names` |
Kind of shrinkage estimator employed. Default estimator is "O" for the Olkin estimator. Other estimators will be considered for future releases. |

An array, the non-empty part of which is a matrix whose rows are the entries of G or D and whose columns are the entries of T. If both G and D are entered, then a list is returned, where the $G element is the G-best matrix, the $d element is the d-best matrix.

Jason Wilson, <jason.wilson@biola.edu>

Cui, X. and Wilson, J. 2007. On How to Calculate the Probability of Correct Selection for Large k Populations. University of California, Riverside Statistics Department Technical Report 297. http://www.bubbs.biola.edu/~jason.wilson/Article2_tech_techreport.pdf

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