Ridge Shrinkage Estimators in Finite Mixture of Generalized Estimating Equations.
| Autor: | Sajad Nezamdoust, Farzad Eskandari |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Mathematics and Modeling in Finance, Vol 2, Iss 2, Pp 91-106 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2783-0578 2783-056X |
| DOI: | 10.22054/jmmf.2023.15188 |
| Popis: | The paper considers the problem of estimation of the parameters in nite mixture models.In this article, a new method is proposed for of estimation of the parameters in nite mixture models. Traditionally, the parameter estimation in nite mixture models is performed from a likelihood point of view by exploiting the expectation maximization (EM) method and the Least Square Principle. Ridge regression is an alternative to the ordinary least squares method when multicollinearity presents among the regressor variables in multiple linear regression analysis. Accordingly, we propose a new shrinkage ridge estimation approach. Based on this principle, we propose an iterative algorithm called RidgeIterative Weighted least Square (RIWLS) to estimate the parameters. Monte-Carlo simulation studies are conducted to appraise the performance of our method. The results show that the Proposed estimator perform better than the IWLS method. |
| Databáze: | Directory of Open Access Journals |
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