• Title/Summary/Keyword: maximum posterior estimator

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A Closed-Form Bayesian Inferences for Multinomial Randomized Response Model

  • Heo, Tae-Young;Kim, Jong-Min
    • Communications for Statistical Applications and Methods
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    • v.14 no.1
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    • pp.121-131
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    • 2007
  • In this paper, we examine the problem of estimating the sensitive characteristics and behaviors in a multinomial randomized response model using Bayesian approach. We derived a posterior distribution for parameter of interest for multinomial randomized response model. Based on the posterior distribution, we also calculated a credible intervals and mean squared error (MSE). We finally compare the maximum likelihood estimator and the Bayes estimator in terms of MSE.

Classical and Bayesian methods of estimation for power Lindley distribution with application to waiting time data

  • Sharma, Vikas Kumar;Singh, Sanjay Kumar;Singh, Umesh
    • Communications for Statistical Applications and Methods
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    • v.24 no.3
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    • pp.193-209
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    • 2017
  • The power Lindley distribution with some of its properties is considered in this article. Maximum likelihood, least squares, maximum product spacings, and Bayes estimators are proposed to estimate all the unknown parameters of the power Lindley distribution. Lindley's approximation and Markov chain Monte Carlo techniques are utilized for Bayesian calculations since posterior distribution cannot be reduced to standard distribution. The performances of the proposed estimators are compared based on simulated samples. The waiting times of research articles to be accepted in statistical journals are fitted to the power Lindley distribution with other competing distributions. Chi-square statistic, Kolmogorov-Smirnov statistic, Akaike information criterion and Bayesian information criterion are used to access goodness-of-fit. It was found that the power Lindley distribution gives a better fit for the data than other distributions.

Reliability of the Mixture Model with Gamma Family Using Gibbs Sampler (깁스추출법을 이용한 감마족 신뢰확률 혼합모형에 대한 연구)

  • 김평구
    • Journal of Korean Society for Quality Management
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    • v.27 no.1
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    • pp.80-90
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    • 1999
  • In this paper, reliability estimation using Gibbs sampler is considered for the mixture model with Gamma family, Gibbs sampler is derived to compute the features for the posterior distribution. By simulation study, the maximum likelihood estimator and the Gibbs estimator are obtained. A numerical study with a simulated data is provided.

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Estimation of the exponentiated half-logistic distribution based on multiply Type-I hybrid censoring

  • Jeon, Young Eun;Kang, Suk-Bok
    • Communications for Statistical Applications and Methods
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    • v.27 no.1
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    • pp.47-64
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    • 2020
  • In this paper, we derive some estimators of the scale parameter of the exponentiated half-logistic distribution based on the multiply Type-I hybrid censoring scheme. We assume that the shape parameter λ is known. We obtain the maximum likelihood estimator of the scale parameter σ. The scale parameter is estimated by approximating the given likelihood function using two different Taylor series expansions since the likelihood equation is not explicitly solved. We also obtain Bayes estimators using prior distribution. To obtain the Bayes estimators, we use the squared error loss function and general entropy loss function (shape parameter q = -0.5, 1.0). We also derive interval estimation such as the asymptotic confidence interval, the credible interval, and the highest posterior density interval. Finally, we compare the proposed estimators in the sense of the mean squared error through Monte Carlo simulation. The average length of 95% intervals and the corresponding coverage probability are also obtained.

Bayesian estimation for the exponential distribution based on generalized multiply Type-II hybrid censoring

  • Jeon, Young Eun;Kang, Suk-Bok
    • Communications for Statistical Applications and Methods
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    • v.27 no.4
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    • pp.413-430
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    • 2020
  • The multiply Type-II hybrid censoring scheme is disadvantaged by an experiment time that is too long. To overcome this limitation, we propose a generalized multiply Type-II hybrid censoring scheme. Some estimators of the scale parameter of the exponential distribution are derived under a generalized multiply Type-II hybrid censoring scheme. First, the maximum likelihood estimator of the scale parameter of the exponential distribution is obtained under the proposed censoring scheme. Second, we obtain the Bayes estimators under different loss functions with a noninformative prior and an informative prior. We approximate the Bayes estimators by Lindleys approximation and the Tierney-Kadane method since the posterior distributions obtained by the two priors are complicated. In addition, the Bayes estimators are obtained by using the Markov Chain Monte Carlo samples. Finally, all proposed estimators are compared in the sense of the mean squared error through the Monte Carlo simulation and applied to real data.

RELIABILITY ESTIMATION OF A MIXTURE EXPONENTIAL MODEL USIGN GIBBS SAMPLER

  • Kim, Hee-Cheul;Kim, Pyong-Koo
    • Journal of applied mathematics & informatics
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    • v.6 no.2
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    • pp.661-668
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    • 1999
  • Reliability estimation using Gibbs sampler considered for modeling mixture exponential reliability problems. Gibbs sampler is developed to compute the features of the posterior distribution. Bayesian estimation of complicated functions requires simpler esti-mation techniques due to the mathematical difficulties involved in the Bayes approach. The Maximum likelihood estimator and the Gibbs estimator of reliability of the system are derived. By simula-tion risk behaviors of derived estimators are compared. model de-termination based on relative error is considered. A numerical study with a simulated data set is provided.

A Study on Maximum Posterior Probability Estimator for Direction of Arrival Estimation of Incoming Signal (입사신호의 도래방향 추정을 위한 최대 사후 확률 추정기에 대한 연구)

  • Lee, Kwan-Hyeong;Park, Sung-Kon;Jeong, Youn-Seo
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.9 no.2
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    • pp.190-195
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    • 2016
  • In this paper, we are comparative analysis both class method and proposal method in order to estimation of incident signal direction on uniform array antenna system. Proposal method of this paper decrease error probability for a signal direction of arrival estimation using maximum posterior probability estimator. If it decrease to signal estimation direction error probability, signal direction of arrival can correctly estimate. Through simulation, we were comparative analysis proposed method and class method. Also, we were comparative analysis about signal estimation error probability with increasing array antenna element. We show the superior performance of the proposed method relative to the class method to decrease of signal estimation error probability about 12%.

Bayesian Hierachical Model using Gibbs Sampler Method: Field Mice Example (깁스 표본 기법을 이용한 베이지안 계층적 모형: 야생쥐의 예)

  • Song, Jae-Kee;Lee, Gun-Hee;Ha, Il-Do
    • Journal of the Korean Data and Information Science Society
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    • v.7 no.2
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    • pp.247-256
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    • 1996
  • In this paper, we applied bayesian hierarchical model to analyze the field mice example introduced by Demster et al.(1981). For this example, we use Gibbs sampler method to provide the posterior mean and compared it with LSE(Least Square Estimator) and MLR(Maximum Likelihood estimator with Random effect) via the EM algorithm.

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Spatio-Temporal Video De-interlacing Algorithm Based on MAP Estimation (MAP 예측기 기반의 시공간 동영상 순차주사화 알고리즘)

  • Lee, Ho-Taek;Song, Byung-Cheol
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.49 no.2
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    • pp.69-75
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    • 2012
  • This paper presents a novel de-interlacing algorithm that can make up motion compensation errors by using maximum a posteriori (MAP) estimator. First, a proper registration is performed between a current field and its adjacent fields, and the progressive frame corresponding to the current field is found via MAP estimator based on the computed registration information. Here, in order to obtain a stable solution, well-known bilateral total variation (BTV)-based regularization is employed. Next, so-called feathering artifacts are detected on a block basis effectively. So, edge-directional interpolation is applied to the pixels where feathering artifact may happen, instead of the above-mentioned temporal de-interlacing. Experimental results show that the PSNR of the proposed algorithm is on average 4dB higher than that of previous studies and provides the better subjective quality than the previous works.

A Bayesian Test for Simple Tree Ordered Alternative using Intrinsic Priors

  • Kim, Seong W.
    • Journal of the Korean Statistical Society
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    • v.28 no.1
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    • pp.73-92
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    • 1999
  • In Bayesian model selection or testing problems, one cannot utilize standard or default noninformative priors, since these priors are typically improper and are defined only up to arbitrary constants. The resulting Bayes factors are not well defined. A recently proposed model selection criterion, the intrinsic Bayes factor overcomes such problems by using a part of the sample as a training sample to get a proper posterior and then use the posterior as the prior for the remaining observations to compute the Bayes factor. Surprisingly, such Bayes factor can also be computed directly from the full sample by some proper priors, namely intrinsic priors. The present paper explains how to derive intrinsic priors for simple tree ordered exponential means. Some numerical results are also provided to support theoretical results and compare with classical methods.

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