Quantile function regression analysis for interval censored data, with application to salary survey data

淡江大學機構典藏 > 理學院 > 應用數學與數據科學學系 > 期刊論文 > Item 987654321/122344

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题名:	Quantile function regression analysis for interval censored data, with application to salary survey data
作者:	CY, Hsu;CC, Wen;YH, Chen
关键词:	Goodness-of-fit test;Interval censoring;Parametric model;Quantile regression;Truncation
日期:	2021-03-22
上传时间:	2022-03-04 12:11:22 (UTC+8)
摘要:	This study aims at regression analysis for quantile functions where the quantile regression coefficients are treated as functions over a continuum of quantile levels. We propose a general inference procedure for quantile regression coefficient functions with interval-censored outcome data. The modeling framework follows a recent proposal using a set of parametric basis functions to approximate the quantile regression coefficient functions. The new proposal can accommodate outcome data subject to general types of interval censoring, including fixed, random, and partly interval censoring. The large sample theory for the proposed estimator is established for inference, and a goodness-of-fit testing procedure is developed to guide the choice of the basis functions. We apply the proposed methodology to a survey dataset on monthly salaries of Taiwan workers, where only parts of the salary data are exact while the others are interval-censored according to the salary intervals prespecified in the survey questionnaire.
關聯:	Japanese Journal of Statistics and Data Science 4, p.999-1018
DOI:	10.1007/s42081-021-00113-3
显示于类别:	[應用數學與數據科學學系] 期刊論文

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