A spline-based semiparametric sieve likelihood method for over-dispersed panel count data

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

In this article we study a Gamma-Frailty inhomogeneous Poisson process model for analysing over-dispersed panel count data. A cubic B-spline function is used to approximate the logarithm of the baseline mean function in the semiparametric proportional mean model. The regression parameters and spline coefficients are jointly estimated by maximizing a spline-based sieve pseudo-likelihood and by replacing the nuisance over-dispersion parameter with its moment estimate. The asymptotic properties of the proposed maximum pseudo likelihood estimator, including its consistency, convergence rate and the asymptotic normality of the estimated regression parameters, are thoroughly studied using modern empirical process theory. A spline-based least-squares standard error estimator is developed to facilitate robust inference for the regression parameters. Simulation studies are conducted to investigate finite sample performance of the proposed method and robustness of the Gamma-Frailty inhomogeneous Poisson process model. Finally, for illustration, the method is used to analyse data from an observational study of sexually transmitted infection (STI) in young women.

Original languageEnglish
Pages (from-to)217-245
Number of pages29
JournalCanadian Journal of Statistics
Volume42
Issue number2
DOIs
StatePublished - 2014

Fingerprint

Sieve Methods
Count Data
Likelihood Methods
Panel Data
Inhomogeneous Poisson Process
Spline
Frailty
Regression
Poisson Model
Process Model
Pseudo-maximum Likelihood
Moment Estimate
Robust Inference
Cubic B-spline
Dispersion Parameter
B-spline Function
Pseudo-likelihood
Overdispersion
Observational Study
Empirical Process

Keywords

  • Counting process
  • Gamma-Frailty
  • Monotone B-splines
  • Over-dispersion
  • Panel count data
  • Semiparametric model

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

A spline-based semiparametric sieve likelihood method for over-dispersed panel count data. / Hua, Lei; Zhang, Ying; Tu, Wanzhu.

In: Canadian Journal of Statistics, Vol. 42, No. 2, 2014, p. 217-245.

Research output: Contribution to journalArticle

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