The Experts below are selected from a list of 7131 Experts worldwide ranked by ideXlab platform
Svyatoslav Gryaznov - One of the best experts on this subject based on the ideXlab platform.
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Deconvolution of a Discrete Uniform Distribution
Statistics & Probability Letters, 2016Co-Authors: Anatoly Zhigljavsky, Nina Golyandina, Svyatoslav GryaznovAbstract:Let ξ be a Discrete random variable (r.v.) with Uniform Distribution on the support set {0,1,…,N}. We study the problem of construction of non-degenerate independent r.v.’s ξ1 and ξ2 such that ξ=ξ1+ξ2, if these r.v.’s exist. We describe a general form for the solutions to this problem, offer some analytic constructions and develop algorithms for computing the Distributions of ξ1 and ξ2.
Sinan Calik - One of the best experts on this subject based on the ideXlab platform.
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probability functions of order statistics from Discrete Uniform Distribution
Asian Journal of Fuzzy and Applied Mathematics, 2019Co-Authors: Ayse Metin Karakas, Sinan CalikAbstract:In this paper, we firstly give basic definitions and theorems for order statistics. Later, we show that r. probability function of order statistics from Discrete Uniform Distribution can be obtained in another form.
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Moments of sample extremes of order statistics from Discrete Uniform Distribution and numerical results
Thermal Science, 2018Co-Authors: Sinan Calik, Ayse T. BugatekinAbstract:In this study, the mth raw moments of sample extremes of order statistics from Discrete Uniform Distribution are obtained. The results of sample extremes of order statistics of random variable for the independent and identically Discrete Uniform Distribution are given. Numerical values are shown in table form
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Generalized Moments of Sample Extremes of Order Statistics from Discrete Uniform Distribution
Asian Journal of Applied Sciences, 2017Co-Authors: Ayse T. Bugatekin, Sinan CalikAbstract:Â More advance, I t has studied in some papers that finding of first two moments of sample extremes of order statistics from Discrete Uniform Distribution. In this paper, these moments are generalized. Also, for sample extremes of order statistics from Discrete Uniform Distribution, moment generating functions are obtained.Â
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tables of moments of sample extremes of order statistics from Discrete Uniform Distribution
2013Co-Authors: A Turan, Sinan Calik, M GurcanAbstract:In this paper, moments of sample extremes of order statistics from Discrete Uniform Distribution are given. For n up to 15, algebraic expressions for the expected values and variances of sample extremes of order statistics from Discrete Uniform Distribution are obtained. It is shown that with the help of the sum
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on the variances of the sample maximum of order statistics from a Discrete Uniform Distribution
Turkiye Klinikleri Journal of Biostatistics, 2010Co-Authors: Sinan Calik, Nurhan Halisdemir, Mehmet Gurcan, Ayse MetinAbstract:54 et be a random sample of size n from a Discrete Distributions with probability mass function and cumulative Distribution function P(x). Let be the order statistics obtained from above random sample by arranging the observations in increasing order of magnitude. Let denote by For convenience, for variance of will also be used. The first two moments of order statistics from Discrete Distributions were proved by Khatri.1 Several recurrence relations and identities available for single and product moments order statistics in a sample size n from an arbitrary continuous Distribution were extended for the Discrete case by Balakrishnan.2 The review paper by Nagaraja3 lucidly accounts all the developments on Discrete order statistics. The first two moments were also proOn the Variances of the Sample Maximum of Order Statistics from A Discrete Uniform Distribution
Chun-tao Chang - One of the best experts on this subject based on the ideXlab platform.
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Statistical inference based on progressively censored samples with random removals from the Burr type XII Distribution
Journal of Statistical Computation and Simulation, 2007Co-Authors: Yi-ju Chen, Chun-tao ChangAbstract:In this article, we study the estimation problems for the Burr type XII Distribution based on progressive type II censoring with random removals, where the number of units removed at each failure time has a Discrete Uniform Distribution. We use the method of maximum likelihood to derive the point estimators of the parameters. The main purpose of this article is to construct the exact confidence interval and region for the parameters. Finally, a numerical example is presented to illustrate the methods developed here.
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Inference in the Pareto Distribution based on progressive Type II censoring with random removals
Journal of Applied Statistics, 2003Co-Authors: Chun-tao ChangAbstract:This study considers the estimation problem for the Pareto Distribution based on progressive Type II censoring with random removals. The number of units removed at each failure time has a Discrete Uniform Distribution. We are going to use the maximum likelihood method to obtain the estimator of parameter. The expectation and variance of the maximum likelihood estimator will be derived. The expected time required to complete such an experiment will be computed. Some numerical results of expected test times are carried out for this type of progressive censoring and other sampling schemes.
Anatoly Zhigljavsky - One of the best experts on this subject based on the ideXlab platform.
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Deconvolution of a Discrete Uniform Distribution
Statistics & Probability Letters, 2016Co-Authors: Anatoly Zhigljavsky, Nina Golyandina, Svyatoslav GryaznovAbstract:Let ξ be a Discrete random variable (r.v.) with Uniform Distribution on the support set {0,1,…,N}. We study the problem of construction of non-degenerate independent r.v.’s ξ1 and ξ2 such that ξ=ξ1+ξ2, if these r.v.’s exist. We describe a general form for the solutions to this problem, offer some analytic constructions and develop algorithms for computing the Distributions of ξ1 and ξ2.
Yun Wang - One of the best experts on this subject based on the ideXlab platform.
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grain area as a statistical weight for polycrystal constituents
Journal of Glaciology, 2004Co-Authors: Olivier Gagliardini, Gael Durand, Yun WangAbstract:By using recently developed automatic instruments for fabric and texture measurements on ice, both the c-axis orientation and area of the individual crystals can be determined. Each grain can then be associated with its volume fraction, defined as a function of its measured cross-sectional area, to describe the microstructure of a a polycrystal. The relevance of this approach is studied using a three-dimensional microstructure obtained from the Potts model. In particular, the area weighting is compared to the classical implicit equal weighting used by glaciologists, which assumes that all the grains have the same volume fraction (Discrete Uniform Distribution). Then, using the measurements of c-axis orientation and crystal size performed on the North Greenland Icecore Project (NorthGRIP) ice core, we compare area-weighted and equal-weighted fabrics. All these comparisons are made with respect to the orientation tensor. According to the ability of the Potts model to reproduce the ice microstructure, it is shown that using the grain cross-sectional area to infer its volume fraction improves the description of the actual polycrystal fabric.