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A Dependence Model for Unbalanced Crop Insurance Indemnity Amounts.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	A Dependence Model for Unbalanced Crop Insurance Indemnity Amounts./
Author:	Lin, Yi-Hsuen.
Description:	1 online resource (43 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-02.
Contained By:	Masters Abstracts International85-02.
Subject:	Statistics. -
Online resource:	click for full text (PQDT)
ISBN:	9798380147491

A Dependence Model for Unbalanced Crop Insurance Indemnity Amounts.
Lin, Yi-Hsuen.

A Dependence Model for Unbalanced Crop Insurance Indemnity Amounts. - 1 online resource (43 pages)

Source: Masters Abstracts International, Volume: 85-02.

Thesis (M.S.)--Michigan State University, 2023.

Includes bibliographical references

In this thesis, a copula model is constructed to estimate dependency and calculate the Value at Risk for insurance coverage under the dependence of the covered losses. The dependence model is illustrated in a three-dimensional setting to simplify the complex theoretical functions and provide an accessible introduction to the copula model. The modeling uses the U.S. crop insurance dataset aggregated by each county level and commodity type. The composite likelihood approach helps to simplify the computation in high-dimensional problems by approximating the negative log-likelihood using bivariate components. In this study, the majorization-minimization principle is employed to estimate the parameters of the normal copula by minimizing the composite likelihood iteratively. To avoid overfitting and result in a valid correlation matrix, the L1 penalty is applied to induce sparsity and shrink irrelevant parameters toward zero. The optimal tuning parameter is selected based on the BIC score to generate a positive semi-definite correlation matrix for the result. In the Appendix of the thesis, the dependence model is extended to a high dimension. The Value at Risk computed for the fictional insurance contract in the data analysis results in a higher value when considering dependence between variables.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380147491Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

Composite likelihoodIndex Terms--Genre/Form:

554714
Electronic books.

A Dependence Model for Unbalanced Crop Insurance Indemnity Amounts.
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500 $a Source: Masters Abstracts International, Volume: 85-02.
500 $a Advisor: Lee, Gee.
502 $a Thesis (M.S.)--Michigan State University, 2023.
504 $a Includes bibliographical references
520 $a In this thesis, a copula model is constructed to estimate dependency and calculate the Value at Risk for insurance coverage under the dependence of the covered losses. The dependence model is illustrated in a three-dimensional setting to simplify the complex theoretical functions and provide an accessible introduction to the copula model. The modeling uses the U.S. crop insurance dataset aggregated by each county level and commodity type. The composite likelihood approach helps to simplify the computation in high-dimensional problems by approximating the negative log-likelihood using bivariate components. In this study, the majorization-minimization principle is employed to estimate the parameters of the normal copula by minimizing the composite likelihood iteratively. To avoid overfitting and result in a valid correlation matrix, the L1 penalty is applied to induce sparsity and shrink irrelevant parameters toward zero. The optimal tuning parameter is selected based on the BIC score to generate a positive semi-definite correlation matrix for the result. In the Appendix of the thesis, the dependence model is extended to a high dimension. The Value at Risk computed for the fictional insurance contract in the data analysis results in a higher value when considering dependence between variables.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
653 $a Composite likelihood
653 $a Correlation matrix
653 $a lasso
653 $a Majorization minimization
653 $a Normal copula
653 $a Value at risk
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