Andrei Badescu

Andrei Badescu

University of Toronto

H-index: 20

North America-Canada

About Andrei Badescu

Andrei Badescu, With an exceptional h-index of 20 and a recent h-index of 14 (since 2020), a distinguished researcher at University of Toronto, specializes in the field of Actuarial Science.

His recent articles reflect a diverse array of research interests and contributions to the field:

Improving risk classification and ratemaking using mixture‐of‐experts models with random effects

Claim reserving via inverse probability weighting: A micro-level chain-ladder method

Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behaviour

Fitting censored and truncated regression data using the mixture of experts models

Effective a Posteriori Ratemaking with Large Insurance Portfolios via Surrogate Modeling

A posteriori risk classification and ratemaking with random effects in the mixture-of-experts model

A Credibility Index Approach for Effective a Posteriori Ratemaking with Large Insurance Portfolios

LRMoE. jl: a software package for insurance loss modelling using mixture of experts regression model

Andrei Badescu Information

University

Position

___

Citations(all)

1373

Citations(since 2020)

514

Cited By

1088

hIndex(all)

20

hIndex(since 2020)

14

i10Index(all)

29

i10Index(since 2020)

20

Email

University Profile Page

Google Scholar

Andrei Badescu Skills & Research Interests

Actuarial Science

Top articles of Andrei Badescu

Improving risk classification and ratemaking using mixture‐of‐experts models with random effects

Journal of Risk and Insurance

2023/9

Claim reserving via inverse probability weighting: A micro-level chain-ladder method

arXiv preprint arXiv:2307.10808

2023/7/5

Data Mining of Telematics Data: Unveiling the Hidden Patterns in Driving Behaviour

arXiv preprint arXiv:2304.10591

2023/4/20

Fitting censored and truncated regression data using the mixture of experts models

North American Actuarial Journal

2022/11/15

Effective a Posteriori Ratemaking with Large Insurance Portfolios via Surrogate Modeling

arXiv preprint arXiv:2211.06568

2022/11/12

A posteriori risk classification and ratemaking with random effects in the mixture-of-experts model

arXiv preprint arXiv:2209.15212

2022/9/30

A Credibility Index Approach for Effective a Posteriori Ratemaking with Large Insurance Portfolios

2022

Andrei Badescu
Andrei Badescu

H-Index: 15

LRMoE. jl: a software package for insurance loss modelling using mixture of experts regression model

Annals of Actuarial Science

2021/7

A new class of severity regression models with an application to IBNR prediction

North American Actuarial Journal

2021/4/3

LRMoE: an R package for flexible actuarial loss modelling using mixture of experts regression model

Available at SSRN 3740215

2020/11/30

Andrei Badescu
Andrei Badescu

H-Index: 15

See List of Professors in Andrei Badescu University(University of Toronto)