Efficient estimation of panel count data with dependent observation process

Weiwei Wang; Yijun Wang; Xianyi Wu; Xiaobing Zhao

doi:10.1080/00949655.2020.1818083

Efficient estimation of panel count data with dependent observation process

Weiwei Wang
, Yijun Wang^*
, Xianyi Wu
, Xiaobing Zhao

^*Corresponding author for this work

School of Statistics

Zhejiang Gongshang University
East China Normal University
Zhejiang University of Finance and Economics

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

In follow-up studies, panel count data are frequently encountered in which subjects are only observed under discrete time points rather than continuous time points. The observation process may be correlated with the panel count data. In this paper, a more general panel count data model with dependent observation process is proposed. A penalized composite quantile regression (CQR) is developed for the panel count data. Consistency and oracle properties are established under some mild regularity conditions. Some numerical simulations are carried out to confirm and assess the performance of the proposed model and approach, and an example from the blander cancer study is also provided.

Original language	English
Pages (from-to)	464-476
Number of pages	13
Journal	Journal of Statistical Computation and Simulation
Volume	91
Issue number	3
DOIs	https://doi.org/10.1080/00949655.2020.1818083
State	Published - 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Panel count data
dependent observation process
penalized composite quantile regression

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10.1080/00949655.2020.1818083

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title = "Efficient estimation of panel count data with dependent observation process",

abstract = "In follow-up studies, panel count data are frequently encountered in which subjects are only observed under discrete time points rather than continuous time points. The observation process may be correlated with the panel count data. In this paper, a more general panel count data model with dependent observation process is proposed. A penalized composite quantile regression (CQR) is developed for the panel count data. Consistency and oracle properties are established under some mild regularity conditions. Some numerical simulations are carried out to confirm and assess the performance of the proposed model and approach, and an example from the blander cancer study is also provided.",

keywords = "Panel count data, dependent observation process, penalized composite quantile regression",

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year = "2021",

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TY - JOUR

T1 - Efficient estimation of panel count data with dependent observation process

AU - Wang, Weiwei

AU - Wang, Yijun

AU - Wu, Xianyi

AU - Zhao, Xiaobing

PY - 2021

Y1 - 2021

N2 - In follow-up studies, panel count data are frequently encountered in which subjects are only observed under discrete time points rather than continuous time points. The observation process may be correlated with the panel count data. In this paper, a more general panel count data model with dependent observation process is proposed. A penalized composite quantile regression (CQR) is developed for the panel count data. Consistency and oracle properties are established under some mild regularity conditions. Some numerical simulations are carried out to confirm and assess the performance of the proposed model and approach, and an example from the blander cancer study is also provided.

AB - In follow-up studies, panel count data are frequently encountered in which subjects are only observed under discrete time points rather than continuous time points. The observation process may be correlated with the panel count data. In this paper, a more general panel count data model with dependent observation process is proposed. A penalized composite quantile regression (CQR) is developed for the panel count data. Consistency and oracle properties are established under some mild regularity conditions. Some numerical simulations are carried out to confirm and assess the performance of the proposed model and approach, and an example from the blander cancer study is also provided.

KW - Panel count data

KW - dependent observation process

KW - penalized composite quantile regression

UR - https://www.scopus.com/pages/publications/85090755559

U2 - 10.1080/00949655.2020.1818083

DO - 10.1080/00949655.2020.1818083

M3 - 文章

AN - SCOPUS:85090755559

SN - 0094-9655

VL - 91

SP - 464

EP - 476

JO - Journal of Statistical Computation and Simulation

JF - Journal of Statistical Computation and Simulation

IS - 3

ER -

Efficient estimation of panel count data with dependent observation process

Abstract

UN SDGs

Keywords

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