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Relevant bibliographies by topics / Bayesian, Logistic regression, MCMC, NCAA

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Journal articles
Dissertations / Theses

Academic literature on the topic 'Bayesian, Logistic regression, MCMC, NCAA'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "Bayesian, Logistic regression, MCMC, NCAA"

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Acquah, Henry De-Graft. "Bayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm." Journal of Social and Development Sciences 4, no. 4 (2013): 193–97. http://dx.doi.org/10.22610/jsds.v4i4.751.

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This paper introduces Bayesian analysis and demonstrates its application to parameter estimation of the logistic regression via Markov Chain Monte Carlo (MCMC) algorithm. The Bayesian logistic regression estimation is compared with the classical logistic regression. Both the classical logistic regression and the Bayesian logistic regression suggest that higher per capita income is associated with free trade of countries. The results also show a reduction of standard errors associated with the coefficients obtained from the Bayesian analysis, thus bringing greater stability to the coefficients.

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Hassan, Masoud M. "A Fully Bayesian Logistic Regression Model for Classification of ZADA Diabetes Dataset." Science Journal of University of Zakho 8, no. 3 (2020): 105–11. http://dx.doi.org/10.25271/sjuoz.2020.8.3.707.

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Classification of diabetes data with existing data mining and machine learning algorithms is challenging and the predictions are not always accurate. We aim to build a model that effectively addresses these challenges (misclassification) and can accurately diagnose and classify diabetes. In this study, we investigated the use of Bayesian Logistic Regression (BLR) for mining such data to diagnose and classify various diabetes conditions. This approach is fully Bayesian suited for automating Markov Chain Monte Carlo (MCMC) simulation. Using Bayesian methods in analysing medical data is useful be

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Li, Gongli, Yueze Liu, Han Li, Ruikuan Yao, and Chenyang Li. "MCMC impute missing values and Bayesian variable selection for logistic regression model to predict Pima Indian Diabetes." Journal of Physics: Conference Series 1865, no. 4 (2021): 042087. http://dx.doi.org/10.1088/1742-6596/1865/4/042087.

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Kitabo, Cheru Atsmegiorgis, and Ehit Tesfu Damtie. "Bayesian Multilevel Analysis of Utilization of Antenatal Care Services in Ethiopia." Computational and Mathematical Methods in Medicine 2020 (July 4, 2020): 1–11. http://dx.doi.org/10.1155/2020/8749753.

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In sub-Saharan Africa, 72% of pregnant women received an antenatal care visit at least once in their pregnancy period. Ethiopia has one of the highest rates of maternal mortality in sub-Saharan African countries. So, this high maternal mortality levels remain a major public health problem. According to EDHS, 2016, the antenatal care (ANC), delivery care (DC), and postnatal care (PNC) were 62%, 73%, and 13%, respectively, indicating that ANC is in a low level. The main objective of this study was to examine the factors that affect the utilization of antenatal care services in Ethiopia using Bay

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Masaoud, Elmabrok, and Henrik Stryhn. "A comparison of statistical methods for the analysis of binary repeated measures data with additional hierarchical structure." Journal of Statistical Research 54, no. 1 (2020): 1–25. http://dx.doi.org/10.47302/jsr.2020540101.

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The objective of the study was to compare statistical methods for the analysis of binary repeated measures data with an additional hierarchical level. Random effects true models with autocorrelated ($\rho=1$, 0.9 or 0.5) subject random effects were used in this simulation study. The settings of the simulation were chosen to reflect a real veterinary somatic cell count dataset, except that the within--subject time series were balanced, complete and of fixed length (4 or 8 time points). Four fixed effects parameters were studied: binary predictors at the subject and cluster levels, respectively,

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Patcharaporn Paokanta, Napat Harnpornchai, Nopasit Chakpitak, Michele Ceccarelli, and Somdet Srichairatanakool. "Parameter Estimation of Binomial Logistic Regression Based on Classical (Maximum Likelihood) and Bayesian (MCMC) Approach for Screening B-Thalassemia." International Journal of Intelligent Information Processing 3, no. 1 (2012): 90–100. http://dx.doi.org/10.4156/ijiip.vol3.issue1.9.

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KNÜRR, TIMO, ESA LÄÄRÄ, and MIKKO J. SILLANPÄÄ. "Genetic analysis of complex traits via Bayesian variable selection: the utility of a mixture of uniform priors." Genetics Research 93, no. 4 (2011): 303–18. http://dx.doi.org/10.1017/s0016672311000164.

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SummaryA new estimation-based Bayesian variable selection approach is presented for genetic analysis of complex traits based on linear or logistic regression. By assigning a mixture of uniform priors (MU) to genetic effects, the approach provides an intuitive way of specifying hyperparameters controlling the selection of multiple influential loci. It aims at avoiding the difficulty of interpreting assumptions made in the specifications of priors. The method is compared in two real datasets with two other approaches, stochastic search variable selection (SSVS) and a re-formulation of Bayes B ut

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Tseng, Shih-Hsien, and Tien Son Nguyen. "A Method for Visualizing Posterior Probit Model Uncertainty in the Early Prediction of Fraud for Sustainability Development." Axioms 10, no. 3 (2021): 178. http://dx.doi.org/10.3390/axioms10030178.

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Corporate fraud is not only curtailed investors’ rights and privileges but also disrupts the overall market economy. For this reason, the formulation of a model that could help detect any unusual market fluctuations would be essential for investors. Thus, we propose an early warning system for predicting fraud associated with financial statements based on the Bayesian probit model while examining historical data from 1999 to 2017 with 327 businesses in Taiwan to create a visual method to aid in decision making. In this study, we utilize a parametric estimation via the Markov Chain Monte Carlo

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Sharma, Tarang, Peter Gøtzsche, and Oliver Kuss. "VP26 Comparing Statistical Methods For Meta-Analysis Of Rare Event Data." International Journal of Technology Assessment in Health Care 33, S1 (2017): 158–59. http://dx.doi.org/10.1017/s0266462317003166.

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INTRODUCTION:We aimed to identify the validity and robustness of effect estimates for serious rare adverse events in clinical study reports of antidepressant trials, across different meta-analysis methods for rare binary events data (1,2).METHODS:Four serious rare adverse events (all-cause mortality, suicidality, aggressive behaviour and akathisia) were meta-analyzed using different methods (3). The Yusuf-Peto odds ratio (OR), which ignores studies with no events in the treatment arms, was compared with the alternative approaches of generalized linear mixed models (GLMM), conditional logistic

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Juhan, Nurliyana, Yong Zulina Zubairi, Zarina Mohd Khalid, and Ahmad Syadi And Mahmood Zuhdi. "Identifying Risk Factors for Female Cardiovascular Disease Patients in Malaysia: A Bayesian Approach." MATEMATIKA 34, no. 3 (2018): 15–23. http://dx.doi.org/10.11113/matematika.v34.n3.1135.

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Cardiovascular disease (CVD) includes coronary heart disease, cerebrovasculardisease (stroke), peripheral artery disease, and atherosclerosis of the aorta. All femalesface the threat of CVD. But becoming aware of symptoms and signs is a great challengesince most adults at increased risk of cardiovascular disease (CVD) have no symptoms orobvious signs especially in females. The symptoms may be identified by the assessmentof their risk factors. The Bayesian approach is a specific way in dealing with this kindof problem by formalizing a priori beliefs and of combining them with the available ob-s

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Dissertations / Theses on the topic "Bayesian, Logistic regression, MCMC, NCAA"

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Marjerison, William M. "Bayesian Logistic Regression with Spatial Correlation: An Application to Tennessee River Pollution." Digital WPI, 2006. https://digitalcommons.wpi.edu/etd-theses/1115.

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"We analyze data (length, weight and location) from a study done by the Army Corps of Engineers along the Tennessee River basin in the summer of 1980. The purpose is to predict the probability that a hypothetical channel catfish at a location studied is toxic and contains 5 ppm or more DDT in its filet. We incorporate spatial information and treate it separetely from other covariates. Ultimately, we want to predict the probability that a catfish from the unobserved location is toxic. In a preliminary analysis, we examine the data for observed locations using frequentist logistic regression,

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Bruzzone, Andrea. "P-SGLD : Stochastic Gradient Langevin Dynamics with control variates." Thesis, Linköpings universitet, Statistik och maskininlärning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-140121.

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Year after years, the amount of data that we continuously generate is increasing. When this situation started the main challenge was to find a way to store the huge quantity of information. Nowadays, with the increasing availability of storage facilities, this problem is solved but it gives us a new issue to deal with: find tools that allow us to learn from this large data sets. In this thesis, a framework for Bayesian learning with the ability to scale to large data sets is studied. We present the Stochastic Gradient Langevin Dynamics (SGLD) framework and show that in some cases its approxima

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Nelson, Bryan. "Modeling the NCAA Tournament Through Bayesian Logistic Regression." 2012. http://digital.library.duq.edu/u?/etd,154158.

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Many rating systems exist that order the Division I teams in Men's College Basketball that compete in the NCAA Tournament, such as seeding teams on an S-curve, and the Pomeroy and Sagarin ratings, simplifying the process of choosing winners to a comparison of two numbers. Rather than creating a rating system, we analyze each matchup by using the difference between the teams' individual regular season statistics as the independent variables. We use an MCMC approach and logistic regression along with several model selection techniques to arrive at models for predicting the winner of each game.

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Deschênes, Alexandre. "Régression logistique bayésienne : comparaison de densités a priori." Thèse, 2015. http://hdl.handle.net/1866/12575.

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La régression logistique est un modèle de régression linéaire généralisée (GLM) utilisé pour des variables à expliquer binaires. Le modèle cherche à estimer la probabilité de succès de cette variable par la linéarisation de variables explicatives. Lorsque l’objectif est d’estimer le plus précisément l’impact de différents incitatifs d’une campagne marketing (coefficients de la régression logistique), l’identification de la méthode d’estimation la plus précise est recherchée. Nous comparons, avec la méthode MCMC d’échantillonnage par tranche, différentes densités a priori spécifiées selo

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