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MultiKink (version 0.2.0)

Estimation and Inference for Multi-Kink Quantile Regression

Description

Estimation and inference for multiple kink quantile regression for longitudinal data and the i.i.d data. A bootstrap restarting iterative segmented quantile algorithm is proposed to estimate the multiple kink quantile regression model conditional on a given number of change points. The number of kinks is also allowed to be unknown. In such case, the backward elimination algorithm and the bootstrap restarting iterative segmented quantile algorithm are combined to select the number of change points based on a quantile BIC. For longitudinal data, we also develop the GEE estimator to incorporate the within-subject correlations. A score-type based test statistic is also developed for testing the existence of kink effect. The package is based on the paper, ``Wei Zhong, Chuang Wan and Wenyang Zhang (2022). Estimation and inference for multikink quantile regression, JBES'' and ``Chuang Wan, Wei Zhong, Wenyang Zhang and Changliang Zou (2022). Multi-kink quantile regression for longitudinal data with application to progesterone data analysis, Biometrics".

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Version

Install

install.packages('MultiKink')

Monthly Downloads

195

Version

0.2.0

License

GPL

Maintainer

Chuang Wan

Last Published

November 5th, 2023

Functions in MultiKink (0.2.0)

triceps

Triceps Skinfold Thickness Dataset
fit.control

Auxiliary parameters to control the model fitting
mkqr.bea

Fit the multi-kink quantile regression in the absence of the number of change points.
mkqr.fit

Fit the multi-kink quantile regression conditional on a given or pre-specified number of change points.
KinkTest

Test the existence of kink effect in the multi-kink quantile regression
MultiKink-package

MultiKink: Estimation and Inference for Multi-Kink Quantile Regression