Empirical Bayes Testing for a Nonexponential Family Distribution

Chen, Lee-Shen

doi:10.1080/03610920601143675

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…is a bivariate exponential distribution which is often used as the lifetime model of two dependent variables [29].…”

Section: Bayes Decision Rules and Empirical Bayes Decision Rulesmentioning

confidence: 99%

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An empirical Bayes test of parameters for a nonexponential distribution family with Negative Quadrant Dependent random samples

Min-na

2013

2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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The empirical Bayes (EB) test problem for parameters of a nonexponential distribution family is investigated with Negative Quadrant Dependent (NQD) random samples. By using the kernel-type density estimation method, the EB test decision rules of parameters are constructed. The asymptotically optimal property and convergence rates for the EB test decision rules are obtained under some suitable conditions. Finally, an example satisfying the conditions of the theorem is given.

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“…is a bivariate exponential distribution which is often used as the lifetime model of two dependent variables [29].…”

Section: Bayes Decision Rules and Empirical Bayes Decision Rulesmentioning

confidence: 99%

“…In recent literature, Huang, Ling and Cheng [28] studied the EB estimation problems for parameters in a nonexponential distribution family; Lee [29] studied the EB test problems for the same family in the case of identically and independently distributed (i.i.d.) random variables samples.…”

Section: Introductionmentioning

confidence: 99%

An empirical Bayes test of parameters for a nonexponential distribution family with Negative Quadrant Dependent random samples

Min-na

2013

2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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“…Huang and Huang [2] study empirical Bayes estimation of the guaranteed lifetime in a two-parameter exponential distribution. Chen [3] investigates empirical Bayes testing for a non-exponential family distribution. Ding and Wei [4] and Liang [5] have, respectively, studied empirical Bayes procedures for double exponential distributions.…”

Section: Introductionmentioning

confidence: 99%

On empirical Bayes two-tail tests for double exponential distributions

Chen

2009

Journal of Nonparametric Statistics

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This paper deals with the problem of testing the hypotheses H 0 : |θ − θ 0 | ≤ c against H 1 : |θ − θ 0 | > c for the location parameter θ of a double exponential distribution with the probability density f (x|θ) = exp(−|x − θ |)/2 by using the empirical Bayes approach. We construct an empirical Bayes test δ * n and study its associated asymptotic optimality. Three classes of prior distributions are considered. For priors in each class, the associated rates of convergence of δ * n are established. These rates are O(n −2m/(2m+3) ), O((ln n) 3/s /n), and O(n −1 ), respectively, where m > 1 and s ≥ 1 are determined according to some conditions.

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Modeling of daily confirmed Saudi COVID-19 cases using inverted exponential regression

Al-Dawsari¹

2021

MBE

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<abstract><p>The coronavirus disease 2019 (COVID-19) pandemic caused by the coronavirus strain has had massive global impact, and has interrupted economic and social activity. The daily confirmed COVID-19 cases in Saudi Arabia are shown to be affected by some explanatory variables that are recorded daily: recovered COVID-19 cases, critical cases, daily active cases, tests per million, curfew hours, maximal temperatures, maximal relative humidity, maximal wind speed, and maximal pressure. Restrictions applied by the Saudi Arabia government due to the COVID-19 outbreak, from the suspension of Umrah and flights, and the lockdown of some cities with a curfew are based on information about COVID-15. The aim of the paper is to propose some predictive regression models similar to generalized linear models (GLMs) for fitting COVID-19 data in Saudi Arabia to analyze, forecast, and extract meaningful information that helps decision makers. In this direction, we propose some regression models on the basis of inverted exponential distribution (IE-Reg), Bayesian (BReg) and empirical Bayesian regression (EBReg) models for use in conjunction with inverted exponential distribution (IE-BReg and IE-EBReg). In all approaches, we use the logarithm (log) link function, gamma prior and two loss functions in the Bayesian approach, namely, the zero-one and LINEX loss functions. To deal with the outliers in the proposed models, we apply Huber and Tukey's bisquare (biweight) functions. In addition, we use the iteratively reweighted least squares (IRLS) algorithm to estimate Bayesian regression coefficients. Further, we compare IE-Reg, IE-BReg, and IE-EBReg using some criteria, such as Akaike's information criterion (AIC), Bayesian information criterion (BIC), deviance (D), and mean squared error (MSE). Finally, we apply the collected data of the daily confirmed from March 23 - June 21, 2020 with the corresponding explanatory variables to the theoretical findings. IE-EBReg shows good model for the COVID-19 cases in Saudi Arabia compared with the other models</p></abstract>

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Empirical Bayes Testing for a Nonexponential Family Distribution

Cited by 3 publications

References 15 publications

An empirical Bayes test of parameters for a nonexponential distribution family with Negative Quadrant Dependent random samples

An empirical Bayes test of parameters for a nonexponential distribution family with Negative Quadrant Dependent random samples

On empirical Bayes two-tail tests for double exponential distributions

Modeling of daily confirmed Saudi COVID-19 cases using inverted exponential regression

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