Masters Theses
Abstract
"Insurance claims caused by natural disasters exhibit spatial dependence with the strength of dependence being based on factors such as physical distance and population density, to name a few. Accounting for spatial dependence is therefore of crucial importance when modeling these types of claims. In this work, we present an approach to assess spatially dependent insurance risks using a combination of linear regression and factor copula models. Specifically, in loss modeling, observed dependence patterns are highly nonlinear, thus copula-based models seem appropriate since they can handle both linear and nonlinear dependence. The factor copula approach for estimating the spatial dependence reduces a complex dependence structure into a relatively easier task of estimating a spatial dependence parameter. Hence, we use a weighted sum of radial basis functions to model a spatial dependence parameter that determines the influence of each location. The methodology is illustrated using a thunderstorm wind loss dataset of Texas. Extensions to Matérn covariance functions and spatiotemporal models are briefly discussed"--Abstract, page iii.
Advisor(s)
Adekpedjou, Akim
Committee Member(s)
Olbricht, Gayla R.
Samaranayake, V. A.
Department(s)
Mathematics and Statistics
Degree Name
M.S. in Applied Mathematics
Publisher
Missouri University of Science and Technology
Publication Date
Spring 2018
Pagination
viii, 69 pages
Note about bibliography
Includes bibliographical references (pages 65-68).
Geographic Coverage
Texas
Rights
© 2018 Tobias Merk, All rights reserved.
Document Type
Thesis - Open Access
File Type
text
Language
English
Thesis Number
T 11299
Electronic OCLC #
1041858592
Recommended Citation
Merk, Tobias, "Models for high dimensional spatially correlated risks and application to thunderstorm loss data in Texas" (2018). Masters Theses. 7770.
https://scholarsmine.mst.edu/masters_theses/7770
