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- Title
Estimating the Covariance Matrix of the Maximum Likelihood Estimator Under Linear Cluster-Weighted Models.
- Authors
Soffritti, Gabriele
- Abstract
In recent years, the research into cluster-weighted models has been intense. However, estimating the covariance matrix of the maximum likelihood estimator under a cluster-weighted model is still an open issue. Here, an approach is developed in which information-based estimators of such a covariance matrix are obtained from the incomplete data log-likelihood of the multivariate Gaussian linear cluster-weighted model. To this end, analytical expressions for the score vector and Hessian matrix are provided. Three estimators of the asymptotic covariance matrix of the maximum likelihood estimator, based on the score vector and Hessian matrix, are introduced. The performances of these estimators are numerically evaluated using simulated datasets in comparison with a bootstrap-based estimator; their usefulness is illustrated through a study aiming at evaluating the link between tourism flows and attendance at museums and monuments in two Italian regions.
- Subjects
MAXIMUM likelihood statistics; COVARIANCE matrices; HESSIAN matrices; MISSING data (Statistics); MUSEUM attendance
- Publication
Journal of Classification, 2021, Vol 38, Issue 3, p594
- ISSN
0176-4268
- Publication type
Article
- DOI
10.1007/s00357-021-09390-9