Computational Statistics
Manijeh Mahmoodi; Mohammad Reza Salehi Rad; Farzad Eskandari
Abstract
AbstractThe novel corona virus (covid-19) spread quickly from person to another and one of the basic aspects of the country management has been to prevent the spread of this disease. So the prediction of its expansion is very important. In such matters, the estimation of new cases and deaths in covid-19 ...
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AbstractThe novel corona virus (covid-19) spread quickly from person to another and one of the basic aspects of the country management has been to prevent the spread of this disease. So the prediction of its expansion is very important. In such matters, the estimation of new cases and deaths in covid-19 has been considered by researchers. we propose an estimation of the statistical model for predicting the new cases and the new deaths by using the vector autoregressive (VAR) model with the multivariate skew normal (MSN) distribution for the asymmetric shocks and predict the samples data. The maximum likelihood (ML) method is applied to estimation of this model for the weekly data of the new cases and the new deaths of covid-19. Data are taken from World Health Organization (WHO) from March 2020 until March 2023 Iran country. The performance of the model is evaluated with the Akaike and the Bayesian information criterions and the mean absolute prediction error (MAPE) is interpreted.
Abstract
In this article, we consider the problem of estimating the stress-strength reliability $Pr (X > Y)$ based on upper record values when $X$ and $Y$ are two independent but not identically distributed random variables from the power hazard rate distribution with common scale parameter $k$. When the parameter ...
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In this article, we consider the problem of estimating the stress-strength reliability $Pr (X > Y)$ based on upper record values when $X$ and $Y$ are two independent but not identically distributed random variables from the power hazard rate distribution with common scale parameter $k$. When the parameter $k$ is known, the maximum likelihood estimator (MLE), the approximate Bayes estimator and the exact confidence intervals of stress-strength reliability are obtained. When the parameter $k$ is unknown, we obtain the MLE and some bootstrap confidence intervals of stress-strength reliability. We also apply the Gibbs sampling technique to study the Bayesian estimation of stress-strength reliability and the corresponding credible interval. An example is presented in order to illustrate the inferences discussed in the previous sections. Finally, to investigate and compare the performance of the different proposed methods in this paper, a Monte Carlo simulation study is conducted.
