Description Usage Arguments Value Author(s) References Examples

Return estimating the parameters in a quantile regression

1 |

`y` |
vector of responses |

`x` |
the design matrix |

`tau` |
the quantile to be estimated, this is generally a number strictly between 0 and 1. |

`error` |
the covergence maximum error |

`iter` |
maximum iterations of the EM algorithm. |

`envelope` |
confidence envelopes for a curve based on bootstrap replicates |

Estimated parameter for a quantile regression fit,standard error, log-likelihood.

Luis Benites Sanchez [email protected], Christian E. Galarza [email protected], Victor Hugo Lachos [email protected]

[1] Koenker, R. W. (2005). Quantile Regression, Cambridge U. Press.

[2] Yu, K. & Moyeed, R. (2001). Bayesian quantile regression. Statistics & Probability Letters, 54 (4), 437 to 447.

[3] Kotz, S., Kozubowski, T. & Podgorski, K. (2001). The laplace distribution and generalizations: A revisit with applications to communications, economics, engineering, and finance. Number 183. Birkhauser.

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ALDqr documentation built on May 30, 2017, 8:26 a.m.

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