A Weighted Composite Likelihood Approach to Inference from Clustered Survey Data Under a Two-Level Model

Laura Dumitrescu (), Wei Qian () and J. N. K. Rao ()
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Laura Dumitrescu: Victoria University of Wellington
Wei Qian: Statistics Canada
J. N. K. Rao: Carleton University

Sankhya A: The Indian Journal of Statistics, 2021, vol. 83, issue 2, No 12, 814-843

Abstract: Abstract Two-level models are widely used for analysing clustered survey data with the design structure matching the model hierarchy. Hypothesis testing on model parameters is studied, using a weighted composite likelihood approach that takes account of the survey design features. In particular, the asymptotic normality of the weighted composite likelihood estimators is established. Using this result, the asymptotic distributions of a generalised score test statistic and a likelihood ratio type test statistic, under a null composite hypothesis on the model parameters, is established. Results of a limited simulation study on the finite sample performance of the proposed tests are reported.

Keywords: Asymptotic distributions; clustered survey data; generalised score test; likelihood-ratio type test; weighted pairwise log-likelihood; Primary 62D05; Secondary 62F12 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13171-020-00234-z

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