Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions
Predictive modeling is a critical technique in many real-world applications, including auto insurance rate-making and the decision making of rate filings review for regulation purposes. It is also important in predicting financial and economic risk in business and economics. Unlike testing hypothese...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/10/1630 |
| _version_ | 1797498157144211456 |
|---|---|
| author | Shengkun Xie Rebecca Luo |
| author_facet | Shengkun Xie Rebecca Luo |
| author_sort | Shengkun Xie |
| collection | DOAJ |
| description | Predictive modeling is a critical technique in many real-world applications, including auto insurance rate-making and the decision making of rate filings review for regulation purposes. It is also important in predicting financial and economic risk in business and economics. Unlike testing hypotheses in statistical inference, results obtained from predictive modeling serve as statistical evidence for the decision making of the underlying problem and discovering the functional relationship between the response variable and the predictors. As a result of this, the variable importance measures become an essential aspect of helping to better understand the contributions of predictors to the built model. In this work, we focus on the study of using generalized linear models (GLM) for the size of loss distributions. In addition, we address the problem of measuring the importance of the variables used in the GLM to further evaluate their potential impact on insurance pricing. In this regard, we propose to shift the focus from variable importance measures of factor levels to factors themselves and to develop variable importance measures for factors included in the model. Therefore, this work is exclusively for modeling with categorical variables as predictors. This work contributes to the further development of GLM modeling to make it even more practical due to this added value. This study also aims to provide benchmark estimates to allow for the regulation of insurance rates using GLM from the variable importance aspect. |
| first_indexed | 2024-03-10T03:30:23Z |
| format | Article |
| id | doaj.art-ec846efb3f2a4649a07ab4ab3839e525 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T03:30:23Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-ec846efb3f2a4649a07ab4ab3839e5252023-11-23T12:00:07ZengMDPI AGMathematics2227-73902022-05-011010163010.3390/math10101630Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss DistributionsShengkun Xie0Rebecca Luo1Global Management Studies, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, CanadaGlobal Management Studies, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, CanadaPredictive modeling is a critical technique in many real-world applications, including auto insurance rate-making and the decision making of rate filings review for regulation purposes. It is also important in predicting financial and economic risk in business and economics. Unlike testing hypotheses in statistical inference, results obtained from predictive modeling serve as statistical evidence for the decision making of the underlying problem and discovering the functional relationship between the response variable and the predictors. As a result of this, the variable importance measures become an essential aspect of helping to better understand the contributions of predictors to the built model. In this work, we focus on the study of using generalized linear models (GLM) for the size of loss distributions. In addition, we address the problem of measuring the importance of the variables used in the GLM to further evaluate their potential impact on insurance pricing. In this regard, we propose to shift the focus from variable importance measures of factor levels to factors themselves and to develop variable importance measures for factors included in the model. Therefore, this work is exclusively for modeling with categorical variables as predictors. This work contributes to the further development of GLM modeling to make it even more practical due to this added value. This study also aims to provide benchmark estimates to allow for the regulation of insurance rates using GLM from the variable importance aspect.https://www.mdpi.com/2227-7390/10/10/1630rate filingsauto insurance regulationgeneralized linear modelsrate makingpredictive modelingvariable importance measure |
| spellingShingle | Shengkun Xie Rebecca Luo Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions Mathematics rate filings auto insurance regulation generalized linear models rate making predictive modeling variable importance measure |
| title | Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions |
| title_full | Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions |
| title_fullStr | Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions |
| title_full_unstemmed | Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions |
| title_short | Measuring Variable Importance in Generalized Linear Models for Modeling Size of Loss Distributions |
| title_sort | measuring variable importance in generalized linear models for modeling size of loss distributions |
| topic | rate filings auto insurance regulation generalized linear models rate making predictive modeling variable importance measure |
| url | https://www.mdpi.com/2227-7390/10/10/1630 |
| work_keys_str_mv | AT shengkunxie measuringvariableimportanceingeneralizedlinearmodelsformodelingsizeoflossdistributions AT rebeccaluo measuringvariableimportanceingeneralizedlinearmodelsformodelingsizeoflossdistributions |