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Estimation in the presence of many nuisance parameters: composite likelihood and plug-in likelihood

Wu, Billy, Yao, Qiwei and Zhu, Shiwu (2013) Estimation in the presence of many nuisance parameters: composite likelihood and plug-in likelihood. Stochastic Processes and Their Applications, 123 (7). pp. 2877-2896. ISSN 0304-4149

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Identification Number: 10.1016/


We consider the incidental parameters problem in this paper, i.e. the estimation for a small number of parameters of interest in the presence of a large number of nuisance parameters. By assuming that the observations are taken from a multiple strictly stationary process, the two estimation methods, namely the maximum composite quasi-likelihood estimation (MCQLE) and the maximum plug-in quasi-likelihood estimation (MPQLE) are considered. For the MCQLE, we profile out nuisance parameters based on lower-dimensional marginal likelihoods, while the MPQLE is based on some initial estimators for nuisance parameters. The asymptotic normality for both the MCQLE and the MPQLE is established under the assumption that the number of nuisance parameters and the number of observations go to infinity together, and both the estimators for the parameters of interest enjoy the standard root-nn convergence rate. Simulation with a spatial–temporal model illustrates the finite sample properties of the two estimation methods.

Item Type: Article
Official URL:
Additional Information: © 2013 Elsevier
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 01 Aug 2013 08:26
Last Modified: 20 Aug 2021 00:54
Funders: Engineering and Physical Sciences Research Council

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