English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This article proposes a new Multi-Kink Quantile Regression (MKQR) model which assumes different linear quantile regression forms in different regions of the domain of the threshold covariate but are still continuous at kink points. First, we investigate parameter estimation, kink points detection and statistical inference in MKQR models. We propose an iterative segmented quantile regression algorithm for estimating both the regression coefficients and the locations of kink points. The proposed algorithm is much more computationally efficient than the grid search algorithm and not sensitive to the selection of initial values. Second, asymptotic properties, such as selection consistency of the number of kink points and asymptotic normality of the estimators of both regression coefficients and kink effects, are established to justify the proposed method theoretically. Third, a score test based on partial subgradients is developed to verify whether the kink effects exist or not. Test-inversion confidence intervals for kink location parameters are also constructed. Monte Carlo simulations and two real data applications on the secondary industrial structure of China and the triceps skinfold thickness of Gambian females illustrate the excellent finite sample performances of the proposed MKQR model. A new R package  MultiKink  is developed to easily implement the proposed methods.

Estimation and Inference for Multi-Kink Quantile Regression