Journal of Statistical Theory and Applications最新文献_第10页

Empirical Estimation for Sparse Double-Heteroscedastic Hierarchical Normal Models 稀疏双异方差层次正态模型的经验估计

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Journal of Statistical Theory and Applications Pub Date : 2020-06-01 DOI: 10.2991/jsta.d.200422.001

Vida Shantia, S. K. Ghoreishi

{"title":"Empirical Estimation for Sparse Double-Heteroscedastic Hierarchical Normal Models","authors":"Vida Shantia, S. K. Ghoreishi","doi":"10.2991/jsta.d.200422.001","DOIUrl":"https://doi.org/10.2991/jsta.d.200422.001","url":null,"abstract":"The available heteroscedastic hierarchical models perform well for a wide range of real-world data, but for the data sets which exhibit heteroscedasticity mainly due to the lack of constant means rather than unequal variances, the existing models tend to overestimate the variance of the second level model which in turn will cause substantial bias in the parameter estimates. Therefore, in this study, we develop heteroscedastic hierarchical models, called double-heteroscedastic hierarchical models, that take into account the heterogeneity in the means for the second level of the models, in addition to considering the heterogeneity of variance for the first level of the models. In these models, we assume that the vector of means in the second level is sparse. We derive Stein’s unbiased risk estimators (SURE) for the parameters in the model based on data decomposition and study their risk properties both in theory and in numerical experiments under the squared loss. The comparison between our SURE estimator and the classical estimators such as empirical Bayes maximum likelihood estimator (EBMLE) and empirical Bayes moment estimator (EBMOM) is illustrated through a simulation study. Finally, we apply our model to a Baseball data set.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"31 1","pages":"148-161"},"PeriodicalIF":1.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89770474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tests for Comparing Several Two-Parameter Exponential Distributions Based on Uncensored/Censored Samples 基于未删节/删节样本的几种双参数指数分布比较检验

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Journal of Statistical Theory and Applications Pub Date : 2020-06-01 DOI: 10.2991/jsta.d.200512.001

K. Krishnamoorthy, T. Nguyen, Yongli Sang

引用次数: 1

Properties of Shannon Entropy for Double Truncated Random Variables and its Applications 双截断随机变量香农熵的性质及其应用

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Journal of Statistical Theory and Applications Pub Date : 2020-06-01 DOI: 10.2991/jsta.d.200512.003

Rajesh Moharana, S. Kayal

引用次数: 1

On the Probabilistic Latent Semantic Analysis Generalization as the Singular Value Decomposition Probabilistic Image 作为奇异值分解概率图像的概率潜在语义分析概化

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Journal of Statistical Theory and Applications Pub Date : 2020-06-01 DOI: 10.2991/jsta.d.200605.001

Pau Figuera Vinué, P. G. Bringas

{"title":"On the Probabilistic Latent Semantic Analysis Generalization as the Singular Value Decomposition Probabilistic Image","authors":"Pau Figuera Vinué, P. G. Bringas","doi":"10.2991/jsta.d.200605.001","DOIUrl":"https://doi.org/10.2991/jsta.d.200605.001","url":null,"abstract":"The Probabilistic Latent Semantic Analysis has been related with the Singular Value Decomposition. Several problems occur when this comparative is done. Data class restrictions and the existence of several local optima mask the relation, being a formal analogy without any real significance. Moreover, the computational difficulty in terms of time and memory limits the technique applicability. In this work, we use the Nonnegative Matrix Factorization with the Kullback–Leibler divergence to prove, when the number of model components is enough and a limit condition is reached, that the Singular Value Decomposition and the Probabilistic Latent Semantic Analysis empirical distributions are arbitrary close. Under such conditions, the Nonnegative Matrix Factorization and the Probabilistic Latent Semantic Analysis equality is obtained. With this result, the Singular Value Decomposition of every nonnegative entries matrix converges to the general case Probabilistic Latent Semantic Analysis results and constitutes the unique probabilistic image. Moreover, a faster algorithm for the Probabilistic Latent Semantic Analysis is provided.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"56 1","pages":"286-296"},"PeriodicalIF":1.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74939979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Measure of Departure from Marginal Homogeneity for the Analysis of Collapsed Square Contingency Tables with Ordered Categories 有序范畴的塌缩平方列联表分析中偏离边际齐性的测度

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Journal of Statistical Theory and Applications Pub Date : 2020-05-01 DOI: 10.2991/jsta.d.200507.001

K. Yamamoto, Itsumi Iwama, S. Tomizawa

引用次数: 0

Generalized Chain Exponential-Type Estimators under Stratified Two-Phase Sampling with Subsampling the Nonrespondents 非应答者分层两相抽样下的广义链指数型估计

IF 1

Journal of Statistical Theory and Applications Pub Date : 2020-05-01 DOI: 10.2991/jsta.d.200507.002

A. Sanaullah, M. Hanif

引用次数: 1

A 3-Component Mixture of Exponential Distribution Assuming Doubly Censored Data: Properties and Bayesian Estimation 双截尾数据下指数分布的三分量混合:性质和贝叶斯估计

IF 1

Journal of Statistical Theory and Applications Pub Date : 2020-05-01 DOI: 10.2991/jsta.d.200508.002

Muhammad Tahir, M. Aslam, M. Abid, Sajid Ali, M. Ahsanullah

{"title":"A 3-Component Mixture of Exponential Distribution Assuming Doubly Censored Data: Properties and Bayesian Estimation","authors":"Muhammad Tahir, M. Aslam, M. Abid, Sajid Ali, M. Ahsanullah","doi":"10.2991/jsta.d.200508.002","DOIUrl":"https://doi.org/10.2991/jsta.d.200508.002","url":null,"abstract":"The output of an engineering process is the result of several inputs, which may be homogeneous or heterogeneous and to study them, we need a model which should be flexible enough to summarize efficiently the nature of such processes. As compared to simple models, mixture models of underlying lifetime distributions are intuitively more appropriate and appealing to model the heterogeneous nature of a process in survival analysis and reliability studies. Moreover, due to time and cost constraints, in the most lifetime testing experiments, censoring is an unavoidable feature. This article focuses on studying a mixture of exponential distributions, and we considered this particular distribution for three reasons. The first reason is its application in reliability modeling of electronic components and the second important reason is its skewed behavior. Similarly, the third and themost important reason is that exponential distribution has thememory-less property. In particular, we deal with the problem of estimating the parameters of a 3-component mixture of exponential distributions using type-II doubly censoring sampling scheme. The elegant closed-form expressions for the Bayes estimators and their posterior risks are derived under squared error loss function, precautionary loss function and DeGroot loss function assuming the noninformative (uniform and Jeffreys’) and the informative priors. A detailedMonte Carlo simulation and real data studies are carried out to investigate the performance (in terms of posterior risks) of the Bayes estimators. From results, it is observed that the Bayes estimates assuming the informative prior perform better than the noninformative priors.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"32 1","pages":"197-211"},"PeriodicalIF":1.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87406035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Bayesian Analysis of Misclassified Generalized Power Series Distributions Under Different Loss Functions 不同损失函数下错分类广义幂级数分布的贝叶斯分析

IF 1

Journal of Statistical Theory and Applications Pub Date : 2020-05-01 DOI: 10.2991/jsta.d.200513.001

P. B. Ahmad

{"title":"Bayesian Analysis of Misclassified Generalized Power Series Distributions Under Different Loss Functions","authors":"P. B. Ahmad","doi":"10.2991/jsta.d.200513.001","DOIUrl":"https://doi.org/10.2991/jsta.d.200513.001","url":null,"abstract":"In certain experimental investigations involving discrete distributions external factors may induce measurement error in the form of misclassification. For instance, a situation may arise where certain values are erroneously reported; such a situation termed as modified or misclassified has been studied by many researchers. Cohen (J. Am. Stat. Assoc. 55 (1960), 139–143; Ann. Inst. Stat. Math. 9 (1960), 189–193; Technometrics. 2 (1960), 109–113) studied misclassification in Poisson and the binomial random variables. In this paper, we discuss misclassification in the most general class of discrete distributions, the generalized power series distributions (GPSDs), where some of the observations corresponding to x = c+1; c ≥ 0 are erroneously observed or at least reported as being x = c with probability α. This class includes among others the binomial, negative binomial, logarithmic series and Poisson distributions. We derive the Bayes estimators of functions of parameters of the misclassified GPSD under different loss functions. The results obtained for misclassified GPSD are then applied to its particular cases like negative binomial, logarithmic series and Poisson distributions. Finally, few numerical examples are provided to illustrate the results.","PeriodicalId":45080,"journal":{"name":"Journal of Statistical Theory and Applications","volume":"24 1","pages":"173-184"},"PeriodicalIF":1.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81714659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Marshall–Olkin Power Generalized Weibull Distribution with Applications in Engineering and Medicine Marshall-Olkin幂广义威布尔分布及其在工程和医学上的应用

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Journal of Statistical Theory and Applications Pub Date : 2020-05-01 DOI: 10.2991/jsta.d.200507.004

A. Afify, D. Kumar, I. Elbatal

引用次数: 12

Modeling Vehicle Insurance Loss Data Using a New Member of T-X Family of Distributions 基于T-X分布族新成员的汽车保险损失数据建模

IF 1

Journal of Statistical Theory and Applications Pub Date : 2020-05-01 DOI: 10.2991/jsta.d.200421.001

Zubair Ahmad, E. Mahmoudi, S. Dey, Saima K. Khosa

引用次数: 19