Journal article
SEMIPARAMETRIC LONGITUDINAL MODEL WITH IRREGULAR TIME AUTOREGRESSIVE ERROR PROCESS
Statistica Sinica, Vol.25(2), pp.507-527
04/01/2015
DOI: 10.5705/ss.2013.073
Abstract
This paper considers semiparametric inference for longitudinal data collected at irregular and possibly subject-specific times. We propose an irregular time autoregressive model for the error process in a partially linear model and develop a unified semiparametric profiling approach to estimating the regression parameters and autoregressive coefficients. An appealing feature of the proposed method is that it can effectively accommodate irregular and subject-specific observation times. We establish the asymptotic normality of the proposed estimators and derive explicit forms of their asymptotic variances. For the nonparametric component, we construct a two-stage local polynomial estimator. Our method takes into account the autoregressive error structure and does not drop any observations. The asymptotic bias and variance of the estimator are derived. We report on simulation studies conducted to evaluate the finite sample performance of the proposed method. The analysis of a dataset of CD4 cell counts of HIV seroconverters demonstrates its application.
Details
- Title: Subtitle
- SEMIPARAMETRIC LONGITUDINAL MODEL WITH IRREGULAR TIME AUTOREGRESSIVE ERROR PROCESS
- Creators
- Yang Bai - Shanghai University of Finance and EconomicsJian Huang - University of IowaRui Li - Shanghai University of Finance and EconomicsJinhong You - Shanghai University of Finance and Economics
- Resource Type
- Journal article
- Publication Details
- Statistica Sinica, Vol.25(2), pp.507-527
- Publisher
- STATISTICA SINICA
- DOI
- 10.5705/ss.2013.073
- ISSN
- 1017-0405
- eISSN
- 1996-8507
- Number of pages
- 21
- Grant note
- SHUFE Graduate Innovation and Creativity Funds 11071154; 11471203 / National Natural Science Foundation of China (NSFC) IRT13077 / Program for Changjiang Scholars and Innovative Research Team in University
- Language
- English
- Date published
- 04/01/2015
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257633302771
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