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12 results for “Markov Chain Monte Carlo (MCMC)”
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Research ArticleOpen accessIn the keywordsParameter-Expanded Data Augmentation for Analyzing Nominal Data With Missing Values Using Multinomial Probit ModelsSuwash Silwal, Xiao ZhangNominal data occur in many scientific fields, such as health-related studies, transportation, and econometrics. Due to the ubiquitousness of missing data, it is inevitable to analyze nominal data with missing values. As is well known, statistical methods for analyzing nominal variables are quite limited. The Multinomial Probit (MNP) model has been an essential tool for analyzing nominal categorical data, but … …Journal of Mathematics and Statistics, Vol. 22, Issue 1 · 23 May 2026 · 1,804 accessesView PDFCite
Research ArticleOpen accessIn the keywordsMCMC-Fuzzy: A Fuzzy Metric Applied to Bayesian Network Structure LearningBeatriz Wilges, Ademar Crotti Junior, Silvia Modesto NassarBayesian network structure learning is considered a complex task as the number of possible structures grows exponentially with the number of variables. Two main methods are used for Bayesian network structure learning: Conditional independence, a method in which a structure is created consistently with independence tests performed on data; and the heuristic search method that explores the structure space. Hybrid … …Journal of Computer Science, Vol. 14, Issue 8 · 24 Aug 2018 · 8,243 accessesView PDFCite
Research ArticleOpen accessIn the abstractBayesian Analysis of Longitudinal Ordinal Data Using Non-Identifiable Multivariate Probit ModelsXiao ZhangMultivariate probit models have been explored foranalyzing longitudinal ordinal data. However, the inherent identification issuein multivariate probit models requires the covariance matrix of the underlyinglatent multivariate normal variables to be a correlation matrix and thushinders the development of efficient Bayesian sampling methods. It is knownthat non-identifiable models may produce Markov Chain Monte Carlo (MCMC)samplers with better convergence and mixing than … …Journal of Mathematics and Statistics, Vol. 18, Issue 1 · 14 Nov 2022 · 6,044 accessesView PDFCite
Research ArticleOpen accessIn the abstractJoint Modelling of Longitudinal-Time-To-Event with Categorical Variable Indicators of Latent Classes: Application to Tuberculosis DataAdeboye Azeez, Mutambayi Ruffin, Ndege James, Qin YongsongIn many clinical and reliability research reports, the outcomes of basic interest is the time to a particular event happens in order to indicate the person’s “true” state of health or survival status. Different models have been used to analyze such data separately, but may be unsuitable if the longitudinal and health status measures are correlated. In this study, mixed … …Journal of Mathematics and Statistics, Vol. 16, Issue 1 · 9 Jun 2020 · 6,686 accessesView PDFCite
Research ArticleOpen accessIn the abstractFatigue Life Prediction of Screw Blade in Screw Sand Washing Machine under Random Load with Gauss DistributionJie Gong, Yun-Fei Fu, Wen Xia et al.The purpose of this study is to present a new method for estimating the fatigue life of the screw blade in the screw sand washing machine. To ensure the accuracy of numerical simulation, the loading area and the value of load are determined by means of the theoretical analysis. To ascertain the location of the stress peak and stress range, … …American Journal of Engineering and Applied Sciences, Vol. 9, Issue 4 · 22 Dec 2016 · 8,005 accessesView PDFCite
Research ArticleOpen accessIn the abstractStatistical Inferences of the Rest Lifetimes of Ball Bearings' Residual Lifetimes Based on Rayleigh Residual Type II Censored DataGhassan K. Abufoudeh, Omar M. Bdair, Raed R. Abu AwwadIn this study, we consider statistical inference problems for the residual life data that come from the Rayleigh model based on type II censored data. Maximum likelihood and Bayesian approaches are used to estimate the scale and location parameters for the Rayleigh model, the Gibbs sampling procedure is used to draw Markov Chain Monte Carlo (MCMC) samples and MCMC samples … …Journal of Mathematics and Statistics, Vol. 12, Issue 3 · 1 Aug 2016 · 7,161 accessesView PDFCite
Research ArticleOpen accessIn the abstractEstimation for Unknown Parameters of the Burr Type-XII Distribution Based on an Adaptive Progressive Type-II Censoring SchemeMontaser M. AmeinIn this study, the Maximum Likelihood Estimation (MLE) and Bayes estimation are exploited to make interval estimation based on adaptive progressive Type-II censoring for the Burr Type-XII distribution. Explicit form for the parameters of Bayes estimator doesn’t exist, so, Markov Chain Monte Carlo (MCMC) method is used as approximation to find posterior mean under squared error loss function. Real data … …Journal of Mathematics and Statistics, Vol. 12, Issue 2 · 29 Apr 2016 · 7,306 accessesView PDFCite
Research ArticleOpen accessIn the abstractBAYESIAN MODEL AVERAGING WITH MARKOV CHAIN MONTE CARLO FOR CALIBRATING TEMPERATURE FORECAST FROM COMBINATION OF TIME SERIES MODELSHeri Kuswanto, Mega Rahmatia SariGlobal warming is an important issue related to the climate and weather forecast. It is shown by significantly increasing the atmospheric temperature level. Hence, improving the forecast accuracy of temperature is an important issue. The forecast is commonly done by performing a deterministic forecast meaning that the system will generate a point forecast without taking into account the uncertainty induced … …Journal of Mathematics and Statistics, Vol. 9, Issue 4 · 29 Nov 2013 · 7,078 accessesView PDFCite
Research ArticleOpen accessIn the abstractBayesian Models for Time Series with Covariates, Trend, Seasonality, Autoregression and OutliersPitsanu Tongkhow, Nantachai KantananthaBayesian methods furnish an attractive approach to time series data analysis. This article proposes the forecasting models that can detect trend, seasonality, auto regression and outliers in time series data related to some covariates. Cumulative Weibull distribution functions for trend, dummy variables for seasonality, binary selections for outliers and latent autoregression for autocorrelated time series data are used for the … …Journal of Computer Science, Vol. 9, Issue 3 · 16 Apr 2013 · 6,653 accessesView PDFCite
Research ArticleOpen accessIn the abstractAssessing Convergence of the Markov Chain Monte Carlo Method in Multivariate CaseDenismar Alves Nogueira, Thelma Safadi, Daniel Furtado Ferreira, Eric Batista FerreiraThe formal convergence diagnosis of the Markov Chain Monte Carlo (MCMC) is made using univariate and multivariate criteria. In 1998, a multivariate extension of the univariate criterion of multiple sequences was proposed. However, due to some problems of that multivariate criterion, an alternative form of calculation was proposed in addition to the two new alternatives for multivariate convergence criteria. In … …Journal of Mathematics and Statistics, Vol. 8, Issue 4 · 8 Jan 2013 · 7,019 accessesView PDFCite