Efficient algorithms for computing the non and semi-parametric maximum likelihood estimates with panel count data

Gang Cheng, Ying Zhang, Liqiang Lu

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Nonparametric and semi-parametric analysis of panel count data have recently been active research topics in statistical literature. The maximum likelihood method based on the non-homogeneous Poisson process has been proved an efficient inference procedure for such analysis. However, computing the non- and semiparametric maximum likelihood estimates (MLEs) can be very intensive numerically and the available methods are not efficient. In this manuscript, we develop an efficient numerical algorithm stemming from the Newton-Raphson method to compute the non- and semi-parametric MLEs for panel count data. Simulation studies are carried out to demonstrate the numerical efficiency of the proposed algorithm compared to the existing methods in the literature.

Original languageEnglish (US)
Pages (from-to)567-579
Number of pages13
JournalJournal of Nonparametric Statistics
Volume23
Issue number2
DOIs
StatePublished - Jun 1 2011
Externally publishedYes

Fingerprint

Count Data
Panel Data
Maximum Likelihood Estimate
Efficient Algorithms
Computing
Non-homogeneous Poisson Process
Parametric Analysis
Newton-Raphson method
Maximum Likelihood Method
Numerical Algorithms
Simulation Study
Demonstrate
Count data
Maximum likelihood

Keywords

  • Isotonic regression
  • Iterative convex minorant algorithm
  • Monte-Carlo
  • Panel count data
  • Quadratic programming

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Efficient algorithms for computing the non and semi-parametric maximum likelihood estimates with panel count data. / Cheng, Gang; Zhang, Ying; Lu, Liqiang.

In: Journal of Nonparametric Statistics, Vol. 23, No. 2, 01.06.2011, p. 567-579.

Research output: Contribution to journalArticle

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