Quantile regression modeling for Malaysian automobile insurance premium data

2015 ◽  
Author(s):  
Mohd Fadzli Mohd Fuzi ◽  
Noriszura Ismail ◽  
Abd Aziz Jemain
Keyword(s):  
Quantile Regression ◽  
Insurance Premium ◽  
Regression Modeling ◽  
Automobile Insurance

An Evaluation of the New Japanese Bonus–Malus System with No-claim and Claimed Subclasses

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mahito Okura ◽  
Takuya Yoshizawa ◽  
Motohiro Sakaki
Keyword(s):  
Simulation Analysis ◽  
Insurance Premium ◽  
Automobile Insurance ◽  
Insurance Claims ◽  
Present Value ◽  
Time Discount

AbstractThe purpose of this research is to evaluate the new Japanese Bonus–Malus System (BMS 2012) in automobile insurance, which is an unusual system wherein both no-claim and claimed subclasses exist. To evaluate BMS 2012, we conduct a simulation analysis and compare BMS 2012 with the former Japanese BMS (BMS 2009) in terms of the present value of the total insurance premium that is closely related to the frequency of insurance claims. Based on the comparison, our main conclusion is that BMS 2012 offers more effects to lower the frequency of insurance claims than BMS 2009 does when the policyholders’ classes in BMS are high classes that evaluate as safety drivers, time discount and/or renewal rates are relatively low, and the policyholders’ risk averseness is large.

Bayesian semiparametric quantile regression modeling for estimating earthquake fatality risk

2018 ◽  
Vol 58 (5) ◽  
pp. 2085-2103
Author(s):  
Xuejun Jiang ◽  
Yunxian Li ◽  
Aijun Yang ◽  
Ruowei Zhou
Keyword(s):  
Quantile Regression ◽  
Regression Modeling ◽  
Fatality Risk ◽  
Bayesian Semiparametric

A Study to Estimate Head Count Index in Small Areas with M-quantile Regression Model Case Study : Poverty in Bogor District Year 2015

2018 ◽  
pp. 239-247
Author(s):  
Zahra Fadhila ◽  
Kusman Sadik ◽  
Indahwati A
Keyword(s):  
Quantile Regression ◽  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Random Effect ◽  
Regression Modeling ◽  
Poverty Level ◽  
Model Case ◽  
Small Areas ◽  
Semiparametric Method ◽  
Head Count

Poverty should be overcome with data. Problem arises when poverty is identified in sub-distric level, yet the data are in district level. Alternatively, M-quantile regression modeling in small area estimation as an indirect estimation approach can be done to measure poverty level in sub-district region with the use of district-scaled or even province-scaled data. In this paper, a Monte Carlo simulation will be conducted to empirically evaluate M-quantile modeling which contaminated area random effect and individual random effect to estimate head count index. M-quantile modeling is chosen because it is quantile-based semiparametric method which guarantees statistical estimation to be robust. Both direct and indirect estimations are performed and the the results of both estimations will be compared in each scenarios. The goodness of fit will be measured with bias and root mean squared error (RMSE). The result shows that M-quantile modeling is effective when there are outliers in individual random effect. Finally, results of application of M-quantile regression modeling to National Socio-economic Survey in Indonesia are presented.

THE IMPORTANCE OF THE CHOICE OF TEST FOR FINDING EVIDENCE OF ASYMMETRIC INFORMATION

2014 ◽  
Vol 44 (2) ◽  
pp. 173-195 ◽  
Author(s):  
Catherine Donnelly ◽  
Martin Englund ◽  
Jens Perch Nielsen
Keyword(s):  
Asymmetric Information ◽  
Adverse Selection ◽  
Insurance Coverage ◽  
Insurance Premium ◽  
Insurance Market ◽  
Automobile Insurance ◽  
Selection Models ◽  
Positive Correlation ◽  
Using Data

AbstractWe put one of the predictions of adverse-selection models to the test, using data from the Danish automobile insurance market: that there is a positive correlation between claims risk and insurance coverage. We can find a statistically significant insurance coverage--risk correlation when coverage is expressed relative to the insurance premium, but not when it is expressed in monetary terms.

scholarly journals qgam: Bayesian Nonparametric Quantile Regression Modeling in R

2021 ◽  
Vol 100 (9) ◽  
Author(s):  
Matteo Fasiolo ◽  
Simon N. Wood ◽  
Margaux Zaffran ◽  
Raphaël Nedellec ◽  
Yannig Goude
Keyword(s):  
Quantile Regression ◽  
Regression Modeling ◽  
Bayesian Nonparametric
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