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Showing 5 results for Moments
Anita Abdollahi,
Volume 19, Issue 1 (6-2014)
Abstract
Mathematical methods and statistical distributions present exact results in the climate calculations and hydrological processes. Awareness of the rainfall probability distribution provides the appropriate conditions for water resource planning. Many studies have been done to estimate probability of rainfall by various methods due to the importance of rainfall distribution in the economic, social and particularly agriculture studies. In these studies, the various probabilistic models have been used and the results of the most investigations show that the bivariate gamma distribution branches of gamma model are compatible for rainfall data. The bivariate gamma distribution is used in the hydrological processes modeling. In the present paper, supposing that the X and Y follow the crovelli’s bivariate gamma model, at first a brief description was given in the case of the exact distributions of the functions U=X+Y, P=XY and Q=X⁄((X+Y)) as well as their respective moments, then the validity of this model was evaluated for Rasht airport weather station data. The results showed that rainfall data of this region also confirms The suitability of the crovelli’s bivariate gamma model.
Miss Elaheh Kadkhoda, Mr Morteza Mohammadi, Dr Gholam Reza Mohtashami Borzadaran,
Volume 22, Issue 1 (12-2017)
Abstract
Generalized Lambda Distribution is an extension of Tukey's lambda distribution, that is very flexible in modeling information and statistical data. In this paper, We introduced two parameterization of this distribution. Then We estimate parameters by moment matching, percentile, starship and maximum likelihood methods and compare two parameterization and parameter estimation methods with Kolmogorov-Smirnov test.
Anita Abdollahi Nanvapisheh,
Volume 22, Issue 2 (3-2018)
Abstract
In this paper, first, we investigate probability density function and the failure rate function of some families of exponential distributions. Then we present their features such as expectation, variance, moments and maximum likelihood estimation and we identify the most flexible distributions according to the figure of probability density function and the failure rate function and finally we offer practical examples of them.
Anita Abdollahi Nanvapisheh, ,
Volume 23, Issue 2 (3-2019)
Abstract
In this paper, a new distribution is introduced, which is a generalization of a well-known distribution. This distribution is flexible and applies to income data modeling. We first provide some of the mathematical and distributional properties of this new model and then, to demonstrate the flexibility the new distribution, we will present the applications of this distribution with real data. Data fitting results confirm the appropriateness of this new model for the real data set.
Azam Karandish Marvasti, Dr Ehsan Ormoz, Dr Maryam Basirat,
Volume 27, Issue 1 (3-2023)
Abstract
In this paper, the concept of unit generalized Gompertz (UGG) distribution will be introduced as a new transformed model of the unit Gompertz distribution, which contains the unit Gompertz distribution as a special case. We calculate explicit expressions for the moments, moment generating, quantile, and hazard functions, and Tsallis and R'{e}nyi entropy. Some different methods for estimation and inference about model parameters are presented too. To estimate the unknown parameters of the model, the maximum likelihood, maximum product spacings, and bootstrap sampling have been discussed, and also approximate confidence interval is presented. Finally, a simulation study and an application to a real data set are given.
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