X. Sheldon Lin

X. Sheldon Lin

University of Toronto

H-index: 33

North America-Canada

About X. Sheldon Lin

X. Sheldon Lin, With an exceptional h-index of 33 and a recent h-index of 20 (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:

IME's Editorial Board

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

Effective a Posteriori Ratemaking with Large Insurance Portfolios via Surrogate Modeling

Editorial to the virtual special issue on emerging risks and insurance technology

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

X. Sheldon Lin Information

University

Position

___

Citations(all)

3996

Citations(since 2020)

1113

Cited By

3338

hIndex(all)

33

hIndex(since 2020)

20

i10Index(all)

56

i10Index(since 2020)

38

Email

University Profile Page

University of Toronto

Google Scholar

View Google Scholar Profile

X. Sheldon Lin Skills & Research Interests

Actuarial Science

Top articles of X. Sheldon Lin

Title

Journal

Author(s)

Publication Date

IME's Editorial Board

Insurance: Mathematics and Economics

Rob Kaas

Roger Laeven

Sheldon Lin

Qihe Tang

Gordon Willmot

...

2023/11/1

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

Journal of Risk and Insurance

Spark C Tseung

Ian Weng Chan

Tsz Chai Fung

Andrei L Badescu

X Sheldon Lin

2023/9

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

arXiv preprint arXiv:2307.10808

Sebastian Calcetero-Vanegas

Andrei L Badescu

X Sheldon Lin

2023/7/5

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

arXiv preprint arXiv:2304.10591

Ian Weng Chan

Spark C Tseung

Andrei L Badescu

X Sheldon Lin

2023/4/20

Effective a Posteriori Ratemaking with Large Insurance Portfolios via Surrogate Modeling

arXiv preprint arXiv:2211.06568

Sebastian Calcetero-Vanegas

Andrei L Badescu

X Sheldon Lin

2022/11/12

Editorial to the virtual special issue on emerging risks and insurance technology

Runhuan Feng

Roger JA Laeven

X Sheldon Lin

2022/11/1

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

arXiv preprint arXiv:2209.15212

Spark C Tseung

Ian Weng Chan

Tsz Chai Fung

Andrei L Badescu

X Sheldon Lin

2022/9/30

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

Sebastian Calcetero Vanegas

Andrei Badescu

X Sheldon Lin

2022

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

North American Actuarial Journal

Tsz Chai Fung

Andrei L Badescu

X Sheldon Lin

2022/11/15

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

North American Actuarial Journal

Tsz Chai Fung

Andrei L Badescu

X Sheldon Lin

2021/4/3

Fitting multivariate Erlang mixtures to data: A roughness penalty approach

Journal of Computational and Applied Mathematics

Wenyong Gui

Rongtan Huang

X Sheldon Lin

2021/4/1

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

Annals of Actuarial Science

Spark C Tseung

Andrei L Badescu

Tsz Chai Fung

X Sheldon Lin

2021/7

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

Available at SSRN 3740215

Spark C Tseung

Andrei Badescu

Tsz Chai Fung

X Sheldon Lin

2020/11/30

Efficient dynamic hedging for large variable annuity portfolios with multiple underlying assets

ASTIN Bulletin: The Journal of the IAA

X Sheldon Lin

Shuai Yang

2020/9

Fast and efficient nested simulation for large variable annuity portfolios: A surrogate modeling approach

Insurance: Mathematics and Economics

X Sheldon Lin

Shuai Yang

2020/3/1

See List of Professors in X. Sheldon Lin University(University of Toronto)

Co-Authors

H-index: 32
Ken Seng Tan

Ken Seng Tan

Nanyang Technological University

H-index: 22
Guojun Gan

Guojun Gan

University of Connecticut

H-index: 18
Yichun Chi

Yichun Chi

Central University of Finance and Economics

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