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26 results for “bayesian estimation”
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Research ArticleOpen accessIn the abstractOn Bayesian Premium Estimators for Gamma Lindley Model under Squared Error Loss Function and Linex Loss FunctionAhmed Sadoun, Halim Zeghdoudi, Fatma Zohra Attoui, Mohamed Riad RemitaWe consider the Gamma Lindley distribution (GaL) as the conditional distribution of (X|θ,γ), we focus on the estimation of the Bayesian premium under squared error loss function (symmetric) and linear-exponential (Linex) loss function (asymmetric), using informative priors (the Gamma prior). Because of its difficulty and non-linearity, we use a numerical approximation for computing the Bayesian premium. Finally, a simulation and … …Journal of Mathematics and Statistics, Vol. 13, Issue 3 · 23 Sep 2017 · 7,043 accessesView PDFCite
Research ArticleOpen accessIn the abstractBurr-X Model Estimate using Bayesian and non-Bayesian ApproachesAbdullah Y. Al-HossainThe present paper is aimed at developing Bayesian and Maximum Likelihood estimations (ML) of the Burr type-X model of distribution when data are gathered from Type-II cumulative censoring with binomial eliminations. The procedures for getting the (ML) evaluations of the parameters are examined. The Bayes technique to get both point and interval estimators of the parameters are illustrated. The expected … …Journal of Mathematics and Statistics, Vol. 12, Issue 2 · 6 May 2016 · 7,210 accessesView PDFCite
Research ArticleOpen accessIn the abstractHIDDEN MARKOV MODELS WITH COVARIATES FOR ANALYSIS OF DEFECTIVE INDUSTRIAL MACHINE PARTSPornpit Sirima, Premysl PokornyMonthly counts of industrial machine part errors are modeled using a two-state Hidden Markov Model (HMM) in order to describe the effect of machine part error correction and the amount of time spent on the error correction on the likelihood of the machine part to be in a &#34defective&#34 or &#34non-defective&#34 state. The number of machine parts errors were collected … …Journal of Mathematics and Statistics, Vol. 10, Issue 3 · 26 Jul 2014 · 6,859 accessesView PDFCite
Research ArticleOpen accessIn the abstractSMOOTHING SPLINE IN SEMIPARAMETRIC ADDITIVE REGRESSION MODEL WITH BAYESIAN APPROACHRita Diana, I. Nyoman Budiantara, Purhadi, Satwiko DarmestoSemiparametric additive regression model is a combination of parametric and nonparametric regression models. The parametric components are not linear but following a polynomial pattern, while the nonparametric components are unknown pattern and assumed to be contained in the Sobolev space. The nonparametric components can be approximated by smoothing spline functions. In the development of smoothing spline, the classical statistical approach … …Journal of Mathematics and Statistics, Vol. 9, Issue 3 · 12 Jun 2013 · 7,252 accessesView PDFCite
Research ArticleOpen accessIn the abstractGENERALIZED LINEAR MIXED MODELS WITH SPATIAL RANDOM EFFECTS FOR SPATIO-TEMPORAL DATA: AN APPLICATION TO DENGUE FEVER MAPPINGKrisada Lekdee, Lily IngsrisawangThe Generalized Linear Mixed Models (GLMMs) with spatial random effects for spatio-temporal data are proposed. A hierarchical Bayesian method is used for parameter estimation. The random effects are assumed to be normally distributed and the spatial random effects are assumed to be proper Conditional Autoregressive (CAR) models. The proposed models are applied to Dengue fever data in Northern Thailand, including … …Journal of Mathematics and Statistics, Vol. 9, Issue 2 · 13 May 2013 · 8,065 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 abstractToward a Hierarchical Bayesian Framework for Modelling the Effect of Regional Diversity on Household ExpenditurePudji Ismartini, Nur Iriawan, Setiawan, Brodjol Sutijo Supri UlamaProblem statement: Household expenditure analysis was highly demanding for government in order to formulate its policy. Since household data was viewed as hierarchical structure with household nested in its regional residence which varies inter region, the contextual welfare analysis was needed. This study proposed to develop a hierarchical model for estimating household expenditure in an attempt to measure the effect … …Journal of Mathematics and Statistics, Vol. 8, Issue 2 · 25 Jun 2012 · 7,304 accessesView PDFCite
Research ArticleOpen accessIn the abstractRestructuring and Expanding Technology Acceptance Model Structural Equation Model and Bayesian ApproachMargaretha Ari Aggorowati, Nur Iriawan, Suhartono, Hasyim GautamaProblem statement: Technology Acceptance Model (TAM) is one of models that analyze user behavior to accept and use a new technology. SEM is the most statistical method which use in TAM analysis that provides the estimation strength of all hypothesized relationship between variables in a theoretical model. Consider to employing the standard SEM in TAM analysis which expected large data, … …American Journal of Applied Sciences, Vol. 9, Issue 4 · 10 Feb 2012 · 8,359 accessesView PDFCite
Research ArticleOpen accessIn the abstractBayesian Changepoint Analysis of the Extreme Rainfall EventsWan Zawiah Wan Zin, Abdul Aziz Jemain, Kamarulzaman IbrahimProblem statement: This study assesses recent changes in extremes of annual rainfall in Peninsular Malaysia based on daily rainfall data for 50 rain-gauged stations over the period 1975-2004. Approach: Eight indices that represent the extreme events are defined and analyzed, which are extreme Dry Spell (XDS), extreme Rain-Sum (XRS), extreme wet-day intensities at 95% and 99% percentiles (I95 and I99), … …Journal of Mathematics and Statistics, Vol. 8, Issue 1 · 6 Feb 2012 · 7,250 accessesView PDFCite
Research ArticleOpen accessIn the abstractPredicting the Severity of Breast Masses with Ensemble of Bayesian ClassifiersAlaa M. ElsayadProblem statement: This study evaluated two different Bayesian classifiers; tree augmented Naive Bayes and Markov blanket estimation networks in order to build an ensemble model for prediction the severity of breast masses. The objective of the proposed algorithm was to help physicians in their decisions to perform a breast biopsy on a suspicious lesion seen in a mammogram image or … …Journal of Computer Science, Vol. 6, Issue 5 · 31 May 2010 · 7,299 accessesView PDFCite