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Showing 2 results for Statistical Inference
Ali Aghamohammadi, Mahdi Sojoudi,
Volume 10, Issue 2 (2-2017)
Abstract
Value-at-Risk and Average Value-at-Risk are tow important risk measures based on statistical methoeds that used to measure the market's risk with quantity structure. Recently, linear regression models such as least squares and quantile methods are introduced to estimate these risk measures. In this paper, these two risk measures are estimated by using omposite quantile regression. To evaluate the performance of the proposed model with the other models, a simulation study was conducted and at the end, applications to real data set from Iran's stock market are illustarted.
Hoda Kamranfar, Javad Etminan, Majid Chahkandi,
Volume 14, Issue 2 (2-2021)
Abstract
A repairable system with two types of failures is studied. Type I failure (minor failure) is removed by a minimal repair, whereas type II failure (catastrophic failure) is modified by an unplanned replacement. The first failure of the system follows a Weibull probability distribution and two maintenance policies are considered. In the first policy, the system is replaced at time T or the first type II failure, and in the second policy, the system is replaced at the nth type I failure, the first type II failure or at time T, whichever takes place first. This paper aims to derive a general representation for the likelihood function of the proposed models. The likelihood-ratio test statistic, maximum likelihood estimators and asymptotic confidence intervals for the parameters are also found. Finally, a Monte Carlo simulation is conducted to illustrate the results.
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