截断、删节和相关假设下的分位数回归

IF 0.9 4区数学 Q3 MATHEMATICS, APPLIED

Acta Mathematicae Applicatae Sinica, English Series Pub Date : 2025-04-02 DOI:10.1007/s10255-024-1034-6

Chang-sheng Liu, Yun-jiao Lu, Si-li Niu

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引用次数: 0

摘要

本文主要研究左截右截数据的非参数分位数回归问题。基于Nadaraya-Watson （NW）核平滑和局部线性（LL）平滑技术，构造了条件分位数的NW和LL估计量。在强混合假设下，我们建立了估计量的渐近表示和渐近正态性。通过仿真研究了估计器的有限样本行为，并用一个实际数据实例说明了所提方法的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Quantile Regression under Truncated, Censored and Dependent Assumptions

In this paper, we focus on the problem of nonparametric quantile regression with left-truncated and right-censored data. Based on Nadaraya-Watson (NW) Kernel smoother and the technique of local linear (LL) smoother, we construct the NW and LL estimators of the conditional quantile. Under strong mixing assumptions, we establish asymptotic representation and asymptotic normality of the estimators. Finite sample behavior of the estimators is investigated via simulation, and a real data example is used to illustrate the application of the proposed methods.

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来源期刊

Acta Mathematicae Applicatae Sinica, English Series 数学-应用数学

CiteScore

1.30

自引率

0.00%

发文量

审稿时长

3.0 months

期刊介绍： Acta Mathematicae Applicatae Sinica (English Series) is a quarterly journal established by the Chinese Mathematical Society. The journal publishes high quality research papers from all branches of applied mathematics, and particularly welcomes those from partial differential equations, computational mathematics, applied probability, mathematical finance, statistics, dynamical systems, optimization and management science.