ALDqr_QD: Q-function distance for each observation in quantile...

In quokar: Quantile Regression Outlier Diagnostics with K Left Out Analysis

Description

Q-function distance for each observation in quantile regression model

Usage

ALDqr_QD(y, x, tau, error, iter)

Arguments

y	Dependent variable in quantile regression. Note that: we suppose y follows asymmetric laplace distribution.
x	Indepdent variables in quantile regression. Note that: x is the independent variable matrix which including the intercept. That means, if the dimension of independent variables is p and the sample size is n, x is a n times p+1 matrix with the first column is one.
tau	Quantile
error	The EM algorithm accuracy of error used in MLE estimation
iter	The iteration frequancy for EM algorithm used in MLE estimation

Details

Measure of the influence of the ith case is the following Q-distance function, similar to the likelihood distance LD_{i} (Cook and Weisberg, 1982), defined as

QD_{i} = 2{Q(\hat{θ}|\hat{θ})-Q(\hat{θ_{(i)}})}

References

Benites L E, Lachos V H, Vilca F E.(2015)“Case-Deletion Diagnostics for Quantile Regression Using the Asymmetric Laplace Distribution,arXiv preprint arXiv:1509.05099.

See Also

ALDqr_GCD

quokar documentation built on May 2, 2019, 6:39 a.m.