Turnitin: Premium Estimation In The Fire Insurance Through Semiparametric Bootstrap

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Turnitin: Premium Estimation In The Fire Insurance Through Semiparametric Bootstrap

Abstract
Along with the development of information, science and technology, there is a quite popular developing resampling method, namely bootstrapping. Bootstrap estimates asymptotically against its original value (observation). Thus, the greater bootstrap replication, the resample distribution will be normally distributed. It indicates that the bootstrap estimator gives better results. Based on the goodness-of-fit test by using Kolmogorov-Smirnov test, the severity on fire insurance data follow Weibull 2 parameter distribution. A case study is conducted on reinsurance company's data for shopping centre's fire. It is a big data. Since it is a reinsurance company's data, the data completeness may be inadequate. It causes the severity claim to be processed using semiparametric bootstrap.

Statement of Responsibility

Author(s)

U. S. Pasaribu - Personal Name
H. Husniah - Personal Name
W. F. Adnan - Personal Name

Edition

Call Number

Subject(s)

Classification

600

Series Title

GMD

Text

Language

English

Publisher

Publishing Year

2022

Publishing Place

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APA Citation

U. S. Pasaribu. (2022).Turnitin: Premium Estimation In The Fire Insurance Through Semiparametric Bootstrap.(Electronic Thesis or Dissertation). Retrieved from https://localhost/etd