arXiv subjects

Rizky Reza Fauzi

Publications and source records attributed to Rizky Reza Fauzi.

3 recordsLinked to original sources

arXiv2024

Bayesian Chain Ladder For Cumulative Run-Off Triangle Under Half-Normal Distribution Assumption

An insurance company is required to prepare a certain amount of money, called reserve, as a mean to pay its policy holders claims in the future. There are several types of reserve, one of them is IBNR reserve, for which the payments are made not in the same calendar year as the events that trigger the claims. Wuthrich and Merz (2015) developed a Bayesian Chain Ladder method for gamma distribution that applies the Bayesian Theory into the usual Chain Ladder Method . In this article, we modify the previous Bayesian Chain Ladder for half-normal distribution, as such distribution is more well suited for lighter tail claims.

stat.ME↗

arXiv2020

Boundary-free Kernel-smoothed Goodness-of-fit Tests for Data on General Interval

We propose kernel-type smoothed Kolmogorov-Smirnov and Cramér-von Mises tests for data on general interval, using bijective transformations. Though not as severe as in the kernel density estimation, utilizing naive kernel method directly to those particular tests will result in boundary problem as well. This happens mostly because the value of the naive kernel distribution function estimator is still larger than $0$ (or less than $1$) when it is evaluated at the boundary points. This situation can increase the errors of the tests especially the second-type error. In this article, we use bijective transformations to eliminate the boundary problem. Some simulation results illustrating the estimator and the tests' performances will be presented in the last part of this article.

stat.ME↗

arXiv2020

Boundary-free Estimators of the Mean Residual Life Function by Transformation

We propose two new kernel-type estimators of the mean residual life function $m_X(t)$ of bounded or half-bounded interval supported distributions. Though not as severe as the boundary problems in the kernel density estimation, eliminating the boundary bias problems that occur in the naive kernel estimator of the mean residual life function is needed. In this article, we utilize the property of bijective transformation. Furthermore, our proposed methods preserve the mean value property, which cannot be done by the naive kernel estimator. Some simulation results showing the estimators' performances and a real data analysis will be presented in the last part of this article.

stat.ME↗