Reducing the sensitivity to nuisance parameters in pseudo‐likelihood functions. Issue 4 (30th October 2014)

• Record Type: Journal Article
• Authors: Ning, Yang; Liang, Kung‐Yee; Reid, Nancy
• Is Part Of: Canadian journal of statistics. Volume 42:Issue 4(2014)
• Page Start: 544
• Page End: 562
• Publication Date: 2014-10-30
• Subjects: Mathematical statistics -- Periodicals; 519.5
• DOI: 10.1002/cjs.11232
• Languages: English
• ISSNs: 0319-5724

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <sec id="cjs11232-sec-0001" sec-type="section"> <p>In a parametric model, parameters are often partitioned into parameters of interest and nuisance parameters. However, as the data structure becomes more complex, inference based on the full likelihood may be computationally intractable or sensitive to potential model misspecification. Alternative likelihood‐based methods proposed in these settings include pseudo‐likelihood and composite likelihood. We propose a simple adjustment to these likelihood functions to reduce the impact of nuisance parameters. The advantages of the modification are illustrated through examples and reinforced through simulations. The adjustment is still novel even if attention is restricted to the profile likelihood. <italic>The Canadian Journal of Statistics</italic> 42: 544–562; 2014 © 2014 Statistical Society of Canada</p> </sec> </abstract>