Stochastic Quasi-Likelihood for Case-Control Point Pattern Data

Ganggang Xu, Rasmus Waagepetersen, Yongtao Guan

Research output: Contribution to journalArticle

Abstract

We propose a novel stochastic quasi-likelihood estimation procedure for case-control point processes. Quasi-likelihood for point processes depends on a certain optimal weight function and for the new method the weight function is stochastic since it depends on the control point pattern. The new procedure also provides a computationally efficient implementation of quasi-likelihood for univariate point processes in which case a synthetic control point process is simulated by the user. Under mild conditions, the proposed approach yields consistent and asymptotically normal parameter estimators. We further show that the estimators are optimal in the sense that the associated Godambe information is maximal within a wide class of estimating functions for case-control point processes. The effectiveness of the proposed method is further illustrated using extensive simulation studies and two data examples.

Original languageEnglish (US)
JournalJournal of the American Statistical Association
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

Quasi-likelihood
Case-control
Control Points
Point Process
Weight Function
Estimator
Estimating Function
Efficient Implementation
Univariate
Point process
Simulation Study

Keywords

  • Case-control data
  • Godambe information
  • Optimal estimating equations
  • Point process

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Stochastic Quasi-Likelihood for Case-Control Point Pattern Data. / Xu, Ganggang; Waagepetersen, Rasmus; Guan, Yongtao.

In: Journal of the American Statistical Association, 01.01.2018.

Research output: Contribution to journalArticle

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