A closed-form alternative estimator for GLM with categorical explanatory variables-Reference-Cited by-同舟云学术

A closed-form alternative estimator for GLM with categorical explanatory variables

Published:2022-05-24 Issue: Volume: Page:1-17
ISSN:0361-0918
Container-title:Communications in Statistics - Simulation and Computation
language:en
Short-container-title:Communications in Statistics - Simulation and Computation

Author:

Brouste Alexandre¹^ORCID,Dutang Christophe²^ORCID,Rohmer Tom³^ORCID

Affiliation:

1. LMM, Le Mans Université, Le Mans, France

2. CEREMADE, CNRS, Université Paris-Dauphine, Université PSL, Paris, France

3. GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France

Funder

ANR project ’Efficient Inference for large and high-frequency data’

the ‘Chair Risques Emergents ou atypiques en Assurance’

Publisher

Informa UK Limited

Subject

Modeling and Simulation,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/03610918.2022.2076870

Reference18 articles.

1. Closed-form maximum likelihood estimator for generalized linear models in the case of categorical explanatory variables: application to insurance loss modeling

2. Effective Statistical Learning Methods for Actuaries II

3. Dutang, C, and A. Charpentier. 2020. CASdatasets: Insurance datasets. Univ. Paris-Dauphine and Univ. du Québec à Montreal. R package version 1.0-11.

4. An explicit split point procedure in model-based trees allowing for a quick fitting of GLM trees and GLM forests

5. Consistency and Asymptotic Normality of the Maximum Likelihood Estimator in Generalized Linear Models

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