Description Usage Arguments Value References Examples

View source: R/SemiQRegGALaD.R

The local polynomial technique is used to estimate location and scale functions of the quantile-based asymmetric Laplace distribution as discussed in Gijbels et al. (2019c). Using these estimates, the quantile function of the generalized asymmetric Laplace distribution will be estimated. A detailed study can be found in Gijbels et al. (2019b).

1 2 | ```
SemiQRegGALaD(beta, x, y, p1 = 1, p2 = 1, h, alpha = NULL, g,
lower = -Inf, upper = Inf, m = 101)
``` |

`beta` |
This is a specific probability for estimating |

`x` |
This is a conditioning covariate. |

`y` |
The is a response variable. |

`p1` |
This is the order of the Taylor expansion for the location function (i.e., |

`p2` |
This is the order of the Taylor expansion for the log of scale function (i.e., |

`h` |
This is the bandwidth parameter |

`alpha` |
This is the index parameter |

`g` |
This is the "link" function. The function |

`lower` |
This is the lower limit of the domain (support of the random variable) |

`upper` |
This is the upper limit of the domain (support of the random variable) |

`m` |
This is the number of grid points at which the functions are to be evaluated. The default value is 101. |

The code `SemiQRegGALaD`

provides the realized value of the *β*th conditional quantile estimator by using semiparametric quantile regression technique discussed in Gijbels et al. (2019b) and Gijbels et al. (2019c).

Gijbels, I., Karim, R. and Verhasselt, A. (2019b). Quantile estimation in a generalized asymmetric distributional setting. To appear in *Springer Proceedings in Mathematics & Statistics, Proceedings of ‘SMSA 2019’, the 14th Workshop on Stochastic Models, Statistics and their Application*, Dresden, Germany, in March 6–8, 2019. Editors: Ansgar Steland, Ewaryst Rafajlowicz, Ostap Okhrin.

Gijbels, I., Karim, R. and Verhasselt, A. (2019c). Semiparametric quantile regression using quantile-based asymmetric family of densities. Manuscript.

1 2 3 4 5 6 7 8 9 10 | ```
data(LocomotorPerfor)
x=log(LocomotorPerfor$Body_Mass)
y=LocomotorPerfor$MRRS
# For log-link function
g_log<-function(y){log(y)}
h_ROT = 0.9030372
fit<-SemiQRegGALaD(beta=0.90,x,y,p1=1,p2=1,h=h_ROT,g=g_log,lower=0)
plot(x,y)
lines(fit$x0,fit$qf_g)
``` |

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