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Likelihood-based inference under nonconvex boundary constraints.
- Source :
- Biometrika; Jun2024, Vol. 111 Issue 2, p591-607, 17p
- Publication Year :
- 2024
-
Abstract
- Likelihood-based inference under nonconvex constraints on model parameters has become increasingly common in biomedical research. In this paper, we establish large-sample properties of the maximum likelihood estimator when the true parameter value lies at the boundary of a nonconvex parameter space. We further derive the asymptotic distribution of the likelihood ratio test statistic under nonconvex constraints on model parameters. A general Monte Carlo procedure for generating the limiting distribution is provided. The theoretical results are demonstrated by five examples in Anderson's stereotype logistic regression model, genetic association studies, gene-environment interaction tests, cost-constrained linear regression and fairness-constrained linear regression. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00063444
- Volume :
- 111
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Biometrika
- Publication Type :
- Academic Journal
- Accession number :
- 177205385
- Full Text :
- https://doi.org/10.1093/biomet/asad062