Hamid Karamikabir

Hamid Karamikabir
Persian Gulf University | PGU · Department of Statistics

PhD in Statistics

About

22
Publications
2,658
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69
Citations
Citations since 2017
21 Research Items
69 Citations
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20172018201920202021202220230510152025
20172018201920202021202220230510152025
20172018201920202021202220230510152025
Introduction
Hamid Karamikabir is a full-time Assistant Professor at the Department of Statistics, Faculty of Intelligent Systems Engineering and Data Science at the Persian Gulf University. Hamid does research in Statistics.

Publications

Publications (22)
Article
Full-text available
Abstract Parameter estimation in multivariate analysis is important, particularly when parameter space is restricted. Among different methods, the shrinkage estimation is of interest. In this article we consider the problem of estimating the p-dimensional mean vector in spherically symmetric models. A dominant class of Baranchik-type shrinkage esti...
Article
One of the most important subject in multivariate analysis is parameters estimation. Among different methods, the shrinkage estimation is of interest. In this paper we consider the generalized Bayes shrinkage estimator of location parameter for spherical distribution under balance-type loss. We assume that the random vector having a spherical symme...
Article
Full-text available
In this paper, a new shrinkage soft wavelet threshold estima- tor based on Stein's unbiased risk estimators (SURE) is introduced for elliptical and spherical distributions under the balance loss function. Our focus will be on a particular thresholding rules to obtain a new threshold to produce new estimators. Also, we obtain SURE shrinkage based on...
Article
Full-text available
In this paper, the generalized Bayes estimator of mean vector parameter for multivariate normal distribution with Unknown mean vector and covariance matrix is considered. This estimation is performed under the balanced-LINEX error loss function. The generalized Bayes estimator by using wavelet transformation is investigated. We also prove admissibi...
Article
Full-text available
One of the most important issues in matrix-variate normal distribution is the mean matrix parameter estimation problem. In this paper, we introduce a new soft-threshold wavelet shrinkage estimator based on Stein’s unbiased risk estimate (SURE) for the matrix-variate normal distribution.We focus on particular thresholding rules to obtain a new SURE...
Article
Full-text available
n many applied areas there is a clear need for the extended forms of the well-known distributions.The new distributions are more flexible to model real data that present a high degree of skewness and kurtosis, such that each one solves a particular part of the classical distribution problems. In this paper, a new two-parameter Generalized Odd Gamma...
Article
In this paper, we introduce a new four-parameter distribution which is called Extended Exponentiated Chen (EE-C) distribution. Theoretical properties of this model including the hazard function, moments, conditional moments, mean residual life, mean past lifetime, coefficients of skewness and kurtosis, order statistics and asymptotic properties are...
Article
Full-text available
In this paper, a new class of distributions called the odd log-logistic Burr-X family with two extra positive parameters is introduced and studied. The new generator extends the odd log-logistic and Burr X distributions among several other well-known distributions. We provide some mathematical properties of the new family including asymptotics, mom...
Article
Full-text available
A new lifetime model called the odd log-logistic Chen distribution is being introduced in this paper. We provide a comprehensive account of the mathematical properties of the proposed family including the hazard rate function, moments, conditional moments, coefficient of skewness, coefficient of kurtosis, entropy and order statistics. The parameter...
Article
In this article, the Topp-Leone odd log-logistic Gumbel (TLOLL-Gumbel) family of distribution have beenstudied. This family, contains the very flexible skewed density function. We study many aspects of the new model like hazard rate function, asymptotics, useful expansions, moments, generating Function, R´enyi entropy and order statistics. We discu...
Article
Statistical distributions are very useful in describing and predicting real world phenomena. Consequently, the choice of the most suitable statistical distribution for modeling given data is very important. In this paper, we propose a new class of lifetime distributions called the Weibull Topp-Leone Generated (WTLG) family. The proposed family is c...
Article
In this paper, the problem of estimating the mean vector under non-negative constraints on location vector of the multivariate normal distribution is investigated. The value of the wavelet threshold based on Stein's unbiased risk estimators is calculated for the shrinkage estimator in restricted parameter space. We suppose that covariance matrix is...
Article
در این مقاله، برآوردگرهای موجک تابع رگرسیون ناپارامتری بر اساس آستانه‌های مختلف تحت توزیع پیشین آمیخته و تابع زیان توان دوم خطا در فضای بسوف محاسبه شده است. همچنین با استفاده از شبیه‌سازی، بهینگی برآوردگرهای مختلف آستانه‌ موجک شامل میانگین پسین، میانه‌ پسین، عامل بیز، آستانه عام و آستانه قطعی مورد بررسی قرار گرفته است. نتایج نشان می‌دهد که برآوردگر...
Article
One of the most important subjects in many areas of statistics are estimating. In this paper we present the shrinkage estimators of the location parameter vector for spherically symmetric distributions.We suppose that the mean vector is non-negative constraint and the components of diagonal covariance matrix is known. We compered the present estima...
Conference Paper
Full-text available
In this paper we consider the generalized Bayes shrinkage estimator of location parameter for multivariate normal distribution under balance-type loss. Also we find minimax estimator of location parameter based on generalized Bayes estimator. We assume that the random vector having a multivariate normal distribution with the known scalar variationa...
Article
Statistical distributions are very useful in describing and predicting real world phenomena. In many applied areas there is a clear need for the extended forms of the well-known distributions. Generally, the new distributions are more flexible to model real data that present a high degree of skewness and kurtosis. The choice of the best-suited stat...
Article
Full-text available
The new distributions are very useful in describing real data sets, because these distributions are more flexible to model real data that present a high degree of skewness and kurtosis. The choice of the best-suited statistical distribution for modeling data is very important. In this paper, A new class of distributions called the New odd log-logis...
Conference Paper
Full-text available
We propose a method of estimation of the derivatives of probability density based on wavelets methods for a sequence of random variables with a common one-dimensional probability density function. We suppose that the process is uniformly strong mixing (ϕ mixing) and we show that the rate of convergence essentially depends on the behavior of a speci...
Article
Full-text available
In this paper, A new class of distributions called the Zografos-Balakrishnan odd loglogistic Generalized half-normal (ZOLL-GHN) family with four parameters is introduced and studied. Useful representations and some mathematical properties of the new family include moments, quantile function, moment Generating function are investigated. The maximum...
Article
In this paper, the generalized Bayes estimator of elliptical distribution parameter's under asymmetric Linex error loss function is considered. The new shrinkage generalized Bayes estimator by applying wavelet transformation is investigated. We develop admissibility and minimaxity of shrinkage estimator on multivariate normal distribution. We prese...
Conference Paper
Full-text available
In this approach we consider the problem of estimating the p-dimensional location parameter in multivariate normal distribution under balance loss function when the scalar scale component is assumed to be unknown. The aim is to study the performance of a dominant class of estimators over the unrestricted estimator, when the components of location p...
Article
در این مقاله مسئله برآورد بردار میانگین توزیع نرمال چند متغیره با واریانس نامعلوم تحت دو محدودیت مورد بررسی قرار می گیرد. ابتدا فرض می شود تمام مولفه های بردار میانگین نامنفی باشند و سپس تنها زیر مجموعه ای از مولفه های آن نامنفی در نظر گرفته می شوند. هدف یافتن رده ای از برآوردگرهای انقباضی برتر، در فضای پارامتر محدود شده، تحت تابع زیان توان دوم است...

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Project
ISEDS at Persian Gulf University (PGU) has pioneered many of the tools and ideas behind the research and applications often classified as "intelligent systems" and “data science,” where computer science, electrical engineering, statistics, and mathematics join together. This Faculty sees an even brighter future for data science as it harnesses a wider set of ideas to build a new more subtle and powerful science of data. As well as being interested in prediction and statistical computation, our Faculty puts equal weight on designing experiments, modeling sophisticated dependencies (networks, data streams), and trying to understand and quantify causal mechanisms, not simply averages and associations, with large data sets. These views are reflected in our curriculum targeted to data science specialists, our faculty’s research, and the work of our research students.