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Showing 4 results for normal Distribution
Masoud Ghasemi Behjani, ,
Volume 21, Issue 2 (3-2017)
Abstract
In this article, the method of determining the optimal sample size is based on Linex asymmetric loss function and has been expressed through Bayesian method for normal, Poisson and exponential distributions. The desirable sample size has been calculated through numerical method. In numerical method, the average posterior risk is calculated and then it is added to the Lindley linear cost function to achieve the average of the total cost. Then, the diagram of sample size is drawn in comparison to the average of total cost and eventually, the optimal sample size that minimizes the cost has been achieved.
Shahram Yaghoobzadeh Shahrastani Shahram Yaghoobzadeh,
Volume 22, Issue 1 (12-2017)
Abstract
In this paper, a new distribution of the three-parameter lifetime model called the Marshall-Olkin Gompertz is proposed on the basis of the Gompertz distribution. It is a generalization of the Gompertz distribution having decreasing failure rate and can also be increasing and bathtub-shaped depending on its parameters. The probability density function, cumulative distribution function, hazard rate function and some mathematical properties of this model such as, central moments, moments of order statistics, Renyi and Shannon entropies and quantile function are derived. In addition, the maximum likelihood of its parameters method is estimated and this new distribution compared with some Gompertz distribution generalizations by means of a set of real data.
, ,
Volume 23, Issue 1 (9-2018)
Abstract
In this paper some properties of Beta - X family are discussed and a member of the family,the beta– normal distribution, is studied in detail.One real data set are used to illustrate the applications of the beta-normal distribution and compare that to gamma - normal and Birnbaum-Saunders distriboutions.
, ,
Volume 23, Issue 2 (3-2019)
Abstract
In this paper, in order to establish a confidence interval (general and shortest) for quantiles of normal distribution in the case of one population, we present a pivotal quantity that has non-central t distribution. In the case of two independent normal populations, we construct a confidence interval for the difference quantiles based on the generalized pivotal quantity and introduce a simple method for extracting its percentiles, by which a shorter confidence interval can be constructed. We will also examine the performance of the proposed methods by using simulations and examples.
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