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Showing 4 results for Skew Laplace Distribution
Zahra Dastmard, Gholamreza Mohtashami Borzadaran, Bagher Moghaddaszadeh Bazaz,
Volume 5, Issue 2 (2-2012)
Abstract
The class of discrete distributions supported on the setup integers is considered. A discrete version of normal distribution can be characterized via maximum entropy. Also, moments, Shannon entropy and Renyi entropy have obtained for discrete symmetric distribution. It is shown that the special cases of this measures imply the discrete normal and discrete Laplace distributions. Then, an analogue of Fisher information is studied by discrete normal, bilateral power series, symmetric discrete and double logarithmic distributions. Also, the conditions under which the above distributions are unimodal are obtained. Finally, central and non-central moments, entropy and maximum entropy of double logarithmic distribution have achieved.
Mehrdad Naderi, Alireza Arabpour, Ahad Jamalizadeh,
Volume 11, Issue 2 (3-2018)
Abstract
This paper presents a new extension of Birnbaum-Saunders distribution based on skew Laplace distribution. Some properties of the new distribution are studied and the EM-type estimators of the parameters with their standard errors are obtained. Finally, we conduct a simulation study and illustrate our distribution by considering two real data example.
Peyman Amiri Domari, Mehrdad Naderi, Ahad Jamalizadeh,
Volume 12, Issue 2 (3-2019)
Abstract
In order to construct the asymmetric models and analyzing data set with asymmetric properties, an useful approach is the weighted model. In this paper, a new class of skew-Laplace distributions is introduced by considering a two-parameter weight function which is appropriate to asymmetric and multimodal data sets. Also, some properties of the new distribution namely skewness and kurtosis coefficients, moment generating function, etc are studied. Finally, The practical utility of the methodology is illustrated through a real data collection.
Mohammad Mehdi Saber, Mohsen Mohammadzadeh,
Volume 18, Issue 2 (2-2025)
Abstract
In this article, autoregressive spatial regression and second-order moving average will be presented to model the outputs of a heavy-tailed skewed spatial random field resulting from the developed multivariate generalized Skew-Laplace distribution. The model parameters are estimated by the maximum likelihood method using the Kolbeck-Leibler divergence criterion. Also, the best spatial predictor will be provided. Then, a simulation study is conducted to validate and evaluate the performance of the proposed model. The method is applied to analyze a real data.
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