Journal articles: 'Bayesian logistic regression models'

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Hwang, Jin-Soo, and Sung-Chan Kang. "Inferential Problems in Bayesian Logistic Regression Models." Korean Journal of Applied Statistics 24, no. 6 (December 31, 2011): 1149–60. http://dx.doi.org/10.5351/kjas.2011.24.6.1149.

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Wang, Xiaoyin. "Bayesian Relative Importance Analysis of Logistic Regression Models." Journal of Statistics Applications & Probability Letters 3, no. 2 (May 1, 2016): 53–69. http://dx.doi.org/10.18576/jsapl/030201.

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Wagner, Helga, and Christine Duller. "Bayesian model selection for logistic regression models with random intercept." Computational Statistics & Data Analysis 56, no. 5 (May 2012): 1256–74. http://dx.doi.org/10.1016/j.csda.2011.06.033.

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Ge, Yang, and Wenxin Jiang. "On Consistency of Bayesian Inference with Mixtures of Logistic Regression." Neural Computation 18, no. 1 (January 1, 2006): 224–43. http://dx.doi.org/10.1162/089976606774841594.

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This is a theoretical study of the consistency properties of Bayesian inference using mixtures of logistic regression models. When standard logistic regression models are combined in a mixtures-of-experts setup, a flexible model is formed to model the relationship between a binary (yes-no) response y and a vector of predictors x. Bayesian inference conditional on the observed data can then be used for regression and classification. This letter gives conditions on choosing the number of experts (i.e., number of mixing components) k or choosing a prior distribution for k, so that Bayesian inference is consistent, in the sense of often approximating the underlying true relationship between y and x. The resulting classification rule is also consistent, in the sense of having near-optimal performance in classification. We show these desirable consistency properties with a nonstochastic k growing slowly with the sample size n of the observed data, or with a random k that takes large values with nonzero but small probabilities.

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Chen, M. H., J. G. Ibrahim, and C. Yiannoutsos. "Prior elicitation, variable selection and Bayesian computation for logistic regression models." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 61, no. 1 (February 1999): 223–42. http://dx.doi.org/10.1111/1467-9868.00173.

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Bulso, Nicola, Matteo Marsili, and Yasser Roudi. "On the Complexity of Logistic Regression Models." Neural Computation 31, no. 8 (August 2019): 1592–623. http://dx.doi.org/10.1162/neco_a_01207.

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We investigate the complexity of logistic regression models, which is defined by counting the number of indistinguishable distributions that the model can represent (Balasubramanian, 1997 ). We find that the complexity of logistic models with binary inputs depends not only on the number of parameters but also on the distribution of inputs in a nontrivial way that standard treatments of complexity do not address. In particular, we observe that correlations among inputs induce effective dependencies among parameters, thus constraining the model and, consequently, reducing its complexity. We derive simple relations for the upper and lower bounds of the complexity. Furthermore, we show analytically that defining the model parameters on a finite support rather than the entire axis decreases the complexity in a manner that critically depends on the size of the domain. Based on our findings, we propose a novel model selection criterion that takes into account the entropy of the input distribution. We test our proposal on the problem of selecting the input variables of a logistic regression model in a Bayesian model selection framework. In our numerical tests, we find that while the reconstruction errors of standard model selection approaches (AIC, BIC, [Formula: see text] regularization) strongly depend on the sparsity of the ground truth, the reconstruction error of our method is always close to the minimum in all conditions of sparsity, data size, and strength of input correlations. Finally, we observe that when considering categorical instead of binary inputs, in a simple and mathematically tractable case, the contribution of the alphabet size to the complexity is very small compared to that of parameter space dimension. We further explore the issue by analyzing the data set of the “13 keys to the White House,” a method for forecasting the outcomes of US presidential elections.

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Jalava, Katri, Sirpa Räsänen, Kaija Ala-Kojola, Saara Nironen, Jyrki Möttönen, and Jukka Ollgren. "Binary Regression Models with Log-Link in the Cohort Studies." Open Epidemiology Journal 6, no. 1 (October 4, 2013): 18–20. http://dx.doi.org/10.2174/1874297101306010018.

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Regression models have been used to control confounding in food borne cohort studies, logistic regression has been commonly used due to easy converge. However, logistic regression provide estimates for OR only when RR estimate is lower than 10%, an unlikely situation in food borne outbreaks. Recent developments have resolved the binary model convergence problems applying log link. Food items significant in the univariable analysis were included for the multivariable analysis of two recent Finnish norovirus outbreaks. We used both log and logistic regression models in R and Bayesian model in Winbugs by SPSS and R. The log-link model could be used to identify the vehicle in the two norovirus outbreak datasets. Convergence problems were solved using Bayesian modelling. Binary model applying log link provided accurate and useful estimates of RR estimating the true risk, a suitable method of choice for multivariable analysis of outbreak cohort studies.

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Prasetyo, Rindang Bangun, Heri Kuswanto, Nur Iriawan, and Brodjol Sutijo Suprih Ulama. "Binomial Regression Models with a Flexible Generalized Logit Link Function." Symmetry 12, no. 2 (February 2, 2020): 221. http://dx.doi.org/10.3390/sym12020221.

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In binomial regression, a link function is used to join the linear predictor variables and the expectation of the response variable. This paper proposes a flexible link function from a new class of generalized logistic distribution, namely a flexible generalized logit (glogit) link. This approach considers both symmetric and asymmetric models, including the cases of lighter and heavier tails, as compared to standard logistic. The glogit is created from the inverse cumulative distribution function of the exponentiated-exponential logistic (EEL) distribution. Using a Bayesian framework, we conduct a simulation study to investigate the model performance compared to the most commonly used link functions, e.g., logit, probit, and complementary log–log. Furthermore, we compared the proposed model with several other asymmetric models using two previously published datasets. The results show that the proposed model outperforms the existing ones and provides flexibility fitting the experimental dataset. Another attractive aspect of the model are analytically tractable and can be easily implemented under a Bayesian approach.

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Pham, Huong T. T., and Hoa Pham. "On the existence of posterior mean for Bayesian logistic regression." Monte Carlo Methods and Applications 27, no. 3 (May 18, 2021): 277–88. http://dx.doi.org/10.1515/mcma-2021-2089.

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Abstract Existence conditions for posterior mean of Bayesian logistic regression depend on both chosen prior distributions and a likelihood function. In logistic regression, different patterns of data points can lead to finite maximum likelihood estimates (MLE) or infinite MLE of the regression coefficients. Albert and Anderson [On the existence of maximum likelihood estimates in logistic regression models, Biometrika 71 1984, 1, 1–10] gave definitions of different types of data points, which are complete separation, quasicomplete separation and overlap. Conditions for the existence of the MLE for logistic regression models were proposed under different types of data points. Based on these conditions, we propose the necessary and sufficient conditions for the existence of posterior mean under different choices of prior distributions. In this paper, a general wide class of priors, which are informative priors and non-informative priors having proper distributions and improper distributions, are considered for the existence of posterior mean. In addition, necessary and sufficient conditions for the existence of posterior mean for an individual coefficient is also proposed.

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Ducher, Michel, Emilie Kalbacher, François Combarnous, Jérome Finaz de Vilaine, Brigitte McGregor, Denis Fouque, and Jean Pierre Fauvel. "Comparison of a Bayesian Network with a Logistic Regression Model to Forecast IgA Nephropathy." BioMed Research International 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/686150.

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Models are increasingly used in clinical practice to improve the accuracy of diagnosis. The aim of our work was to compare a Bayesian network to logistic regression to forecast IgA nephropathy (IgAN) from simple clinical and biological criteria. Retrospectively, we pooled the results of all biopsies(n=155)performed by nephrologists in a specialist clinical facility between 2002 and 2009. Two groups were constituted at random. The first subgroup was used to determine the parameters of the models adjusted to data by logistic regression or Bayesian network, and the second was used to compare the performances of the models using receiver operating characteristics (ROC) curves. IgAN was found (on pathology) in 44 patients. Areas under the ROC curves provided by both methods were highly significant but not different from each other. Based on the highest Youden indices, sensitivity reached (100% versus 67%) and specificity (73% versus 95%) using the Bayesian network and logistic regression, respectively. A Bayesian network is at least as efficient as logistic regression to estimate the probability of a patient suffering IgAN, using simple clinical and biological data obtained during consultation.

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Whitehead, John, and David Williamson. "Bayesian decision procedures based on logistic regression models for dose-finding studies." Journal of Biopharmaceutical Statistics 8, no. 3 (January 1, 1998): 445–67. http://dx.doi.org/10.1080/10543409808835252.

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Zhang, Chun-Xia, Shuang Xu, and Jiang-She Zhang. "A novel variational Bayesian method for variable selection in logistic regression models." Computational Statistics & Data Analysis 133 (May 2019): 1–19. http://dx.doi.org/10.1016/j.csda.2018.08.025.

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Bidyuk, Peter, Aleksander Peter Gozhjy, and Alexandr T. Rofymchuk. "Forecasting based on Bayesian type models." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 3 (December 24, 2015): 6570–84. http://dx.doi.org/10.24297/ijct.v15i3.1672.

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A review of some Bayesian data analysis models is proposed, namely the models with one and several parameters. A methodology is developed for probabilistic models construction in the form of Bayesian networks using statistical data and expert estimates. The methodology provides a possibility for constructing high adequacy probabilistic models for solving the problems of classification and forecasting. An integrated dynamic network model is proposed that is based on combination of probabilistic and regression approaches; the model is distinguished with a possibility for multistep forecasts estimation. The forecast estimates computed with the dynamic model are compared with the results achieved with logistic regression combined with multiple regression. The best results were achieved in this case with the combined dynamic net model.Â

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de la Cruz, Rolando, Oslando Padilla, Mauricio A. Valle, and Gonzalo A. Ruz. "Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks." Mathematics 9, no. 6 (March 17, 2021): 639. http://dx.doi.org/10.3390/math9060639.

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This study aims to analyze and explore criminal recidivism with different modeling strategies: one based on an explanation of the phenomenon and another based on a prediction task. We compared three common statistical approaches for modeling recidivism: the logistic regression model, the Cox regression model, and the cure rate model. The parameters of these models were estimated from a Bayesian point of view. Additionally, for prediction purposes, we compared the Cox proportional model, a random survival forest, and a deep neural network. To conduct this study, we used a real dataset that corresponds to a cohort of individuals which consisted of men convicted of sexual crimes against women in 1973 in England and Wales. The results show that the logistic regression model tends to give more precise estimations of the probabilities of recidivism both globally and with the subgroups considered, but at the expense of running a model for each moment of the time that is of interest. The cure rate model with a relatively simple distribution, such as Weibull, provides acceptable estimations, and these tend to be better with longer follow-up periods. The Cox regression model can provide the most biased estimations with certain subgroups. The prediction results show the deep neural network’s superiority compared to the Cox proportional model and the random survival forest.

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Pokhrel, Keshav P., Taysseer Sharaf, Prem Bhandari, and Dirgha Ghimire. "Farm Exit Among Smallholder Farmers of Nepal: A Bayesian Logistic Regression Models Approach." Agricultural Research 9, no. 4 (March 16, 2020): 675–83. http://dx.doi.org/10.1007/s40003-020-00465-4.

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ARA, Anderson, Francisco LOUZADA, and Luis Aparecido MILAN. "CLASSIFICATION BINARY MODELS FOR BIOMEDICAL DATA: SIMPLE PROBABILISTIC NETWORKS AND LOGISTIC REGRESSION." REVISTA BRASILEIRA DE BIOMETRIA 36, no. 1 (March 28, 2018): 48. http://dx.doi.org/10.28951/rbb.v36i1.114.

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In the biomedical area a critical factor is whether a classication model is accurate enough in order to provide correct classication whether or not a patient has a certain disease. Several techniques may be used in order to accommodate such situation. In this context, Bayesian networks have emerged as a practical classication technology with successful applications in many elds. At the same time, logistic regression is a widely used statistical classication method and evidenced in the literature. In the current paper we focus on investigating the preditive performance of a probabilistic networks in its simple particular case, the so called naive Bayes network, compared to the logistic regression. A systematic simulation study is performed and the procedures are illustrated in some benchmark biomedical data sets. data sets.

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Gordóvil-Merino, Amalia, Joan Guàrdia-Olmos, Maribel Peró-Cebollero, and Emilia I. de la Fuente-Solanas. "Classical and Bayesian Estimation in the Logistic Regression Model Applied to Diagnosis of Child Attention Deficit Hyperactivity Disorder." Psychological Reports 106, no. 2 (April 2010): 519–33. http://dx.doi.org/10.2466/pr0.106.2.519-533.

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The limitations inherent to classical estimation of the logistic regression models are known. The Bayesian approach in statistical analysis is an alternative to be considered, given that it makes it possible to introduce prior information about the phenomenon under study. The aim of the present work is to analyze binary and multinomial logistic regression simple models estimated by means of a Bayesian approach in comparison to classical estimation. To that effect, Child Attention Deficit Hyperactivity Disorder (ADHD) clinical data were analyzed. The sample included 286 participants of 6–12 years (78% boys, 22% girls) with ADHD positive diagnosis in 86.7% of the cases. The results show a reduction of standard errors associated to the coefficients obtained from the Bayesian analysis, thus bringing a greater stability to the coefficients. Complex models where parameter estimation may be easily compromised could benefit from this advantage.

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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 (September 30, 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 because of the rich hierarchical models, uncertainty quantification, and prior information they provide. The analysis was done on a real medical dataset created for 909 patients in Zakho city with a binary class label and seven independent variables. Three different prior distributions (Gaussian, Laplace and Cauchy) were investigated for our proposed model implemented by MCMC. The performance and behaviour of the Bayesian approach were illustrated and compared with the traditional classification algorithms on this dataset using 10-fold cross-validation. Experimental results show overall that classification under BLR with informative Gaussian priors performed better in terms of various accuracy metrics. It provides an accuracy of 92.53%, a recall of 94.85%, a precision of 91.42% and an F1 score of 93.11%. Experimental results suggest that it is worthwhile to explore the application of BLR to predictive modelling tasks in medical studies using informative prior distributions.

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Schmid, Christopher H., and Bernard Rosner. "A bayesian approach to logistic regression models having measurement error following a mixture distribution." Statistics in Medicine 12, no. 12 (June 1993): 1141–53. http://dx.doi.org/10.1002/sim.4780121204.

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Koslovsky, M. D., M. D. Swartz, L. Leon-Novelo, W. Chan, and A. V. Wilkinson. "Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates." Journal of Statistical Computation and Simulation 88, no. 3 (November 8, 2017): 575–96. http://dx.doi.org/10.1080/00949655.2017.1398255.

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Das, Iswar, Alfred Stein, Norman Kerle, and Vinay K. Dadhwal. "Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models." Geomorphology 179 (December 2012): 116–25. http://dx.doi.org/10.1016/j.geomorph.2012.08.004.

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Ntzoufras, Ioannis, Vasilis Palaskas, and Sotiris Drikos. "Bayesian models for prediction of the set-difference in volleyball." IMA Journal of Management Mathematics 32, no. 4 (April 12, 2021): 491–518. http://dx.doi.org/10.1093/imaman/dpab007.

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Abstract We study and develop Bayesian models for the analysis of volleyball match outcomes as recorded by the set-difference. Due to the peculiarity of the outcome variable (set-difference) which takes discrete values from $-3$ to $3$, we cannot consider standard models based on the usual Poisson or binomial assumptions used for other sports such as football/soccer. Hence, the first and foremost challenge was to build models appropriate for the set-difference of each volleyball match. Here we consider two major approaches: (a) an ordered multinomial logistic regression model and (b) a model based on a truncated version of the Skellam distribution. For the first model, we consider the set-difference as an ordinal response variable within the framework of multinomial logistic regression models. Concerning the second model, we adjust the Skellam distribution to account for the volleyball rules. We fit and compare both models with the same covariate structure as in Karlis & Ntzoufras (2003). Both models are fitted, illustrated and compared within Bayesian framework using data from both the regular season and the play-offs of the season 2016/17 of the Greek national men’s volleyball league A1.

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Rozoff, Christopher M., and James P. Kossin. "New Probabilistic Forecast Models for the Prediction of Tropical Cyclone Rapid Intensification." Weather and Forecasting 26, no. 5 (October 1, 2011): 677–89. http://dx.doi.org/10.1175/waf-d-10-05059.1.

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Abstract The National Hurricane Center currently employs a skillful probabilistic rapid intensification index (RII) based on linear discriminant analysis of the environmental and satellite-derived features from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) dataset. Probabilistic prediction of rapid intensity change in tropical cyclones is revisited here using two additional models: one based on logistic regression and the other on a naïve Bayesian framework. Each model incorporates data from the SHIPS dataset over both the North Atlantic and eastern North Pacific Ocean basins to provide the probability of exceeding the standard rapid intensification thresholds [25, 30, and 35 kt (24 h)−1] for 24 h into the future. The optimal SHIPS and satellite-based predictors of rapid intensification differ slightly between each probabilistic model and ocean basin, but each set of optimal predictors incorporates thermodynamic and dynamic aspects of the tropical cyclone’s environment (such as vertical wind shear) and its structure (such as departure from convective axisymmetry). Cross validation shows that both the logistic regression and Bayesian probabilistic models are skillful relative to climatology. Dependent testing indicates both models exhibit forecast skill that generally exceeds the skill of the present operational SHIPS-RII and a simple average of the probabilities provided by the logistic regression, Bayesian, and SHIPS-RII models provides greater skill than any individual model. For the rapid intensification threshold of 25 kt (24 h)−1, the three-member ensemble mean improves the Brier skill scores of the current operational SHIPS-RII by 33% in the North Atlantic and 52% in the eastern North Pacific.

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Bafumi, Joseph, Andrew Gelman, David K. Park, and Noah Kaplan. "Practical Issues in Implementing and Understanding Bayesian Ideal Point Estimation." Political Analysis 13, no. 2 (2005): 171–87. http://dx.doi.org/10.1093/pan/mpi010.

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Logistic regression models have been used in political science for estimating ideal points of legislators and Supreme Court justices. These models present estimation and identifiability challenges, such as improper variance estimates, scale and translation invariance, reflection invariance, and issues with outliers. We address these issues using Bayesian hierarchical modeling, linear transformations, informative regression predictors, and explicit modeling for outliers. In addition, we explore new ways to usefully display inferences and check model fit.

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Witteveen, Annemieke, Gabriela F. Nane, Ingrid M. H. Vliegen, Sabine Siesling, and Maarten J. IJzerman. "Comparison of Logistic Regression and Bayesian Networks for Risk Prediction of Breast Cancer Recurrence." Medical Decision Making 38, no. 7 (August 22, 2018): 822–33. http://dx.doi.org/10.1177/0272989x18790963.

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Purpose. For individualized follow-up, accurate prediction of locoregional recurrence (LRR) and second primary (SP) breast cancer risk is required. Current prediction models employ regression, but with large data sets, machine-learning techniques such as Bayesian Networks (BNs) may be better alternatives. In this study, logistic regression was compared with different BNs, built with network classifiers and constraint- and score-based algorithms. Methods. Women diagnosed with early breast cancer between 2003 and 2006 were selected from the Netherlands Cancer Registry (NCR) ( N = 37,320). BN structures were developed using 1) Bayesian network classifiers, 2) correlation coefficients with different cutoffs, 3) constraint-based learning algorithms, and 4) score-based learning algorithms. The different models were compared with logistic regression using the area under the receiver operating characteristic curve, an external validation set obtained from the NCR from 2007 and 2008 ( N = 12,308), and subgroup analyses for a high- and low-risk group. Results. The BNs with the most links showed the best performance in both LRR and SP prediction (c-statistic of 0.76 for LRR and 0.69 for SP). In the external validation, logistic regression generally outperformed the BNs in both SP and LRR (c-statistic of 0.71 for LRR and 0.64 for SP). The differences were nonetheless small. Although logistic regression performed best on most parts of the subgroup analysis, BNs outperformed regression with respect to average risk for SP prediction in low- and high-risk groups. Conclusions. Although estimates of regression coefficients depend on other independent variables, there is no assumed dependence relationship between coefficient estimators and the change in value of other variables as in the case of BNs. Nonetheless, this analysis suggests that regression is still more accurate or at least as accurate as BNs for risk estimation for both LRRs and SP tumors.

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Wang, Aobo, and David C. Wheeler. "Catchment Area Analysis Using Bayesian Regression Modeling." Cancer Informatics 14s2 (January 2015): CIN.S17297. http://dx.doi.org/10.4137/cin.s17297.

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A catchment area (CA) is the geographic area and population from which a cancer center draws patients. Defining a CA allows a cancer center to describe its primary patient population and assess how well it meets the needs of cancer patients within the CA. A CA definition is required for cancer centers applying for National Cancer Institute (NCI)-designated cancer center status. In this research, we constructed both diagnosis and diagnosis/treatment CAs for the Massey Cancer Center (MCC) at Virginia Commonwealth University. We constructed diagnosis CAs for all cancers based on Virginia state cancer registry data and Bayesian hierarchical logistic regression models. We constructed a diagnosis/treatment CA using billing data from MCC and a Bayesian hierarchical Poisson regression model. To define CAs, we used exceedance probabilities for county random effects to assess unusual spatial clustering of patients diagnosed or treated at MCC after adjusting for important demographic covariates. We used the MCC CAs to compare patient characteristics inside and outside the CAs. Among cancer patients living within the MCC CA, patients diagnosed at MCC were more likely to be minority, female, uninsured, or on Medicaid.

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Cheraghi, Zahra, Saharnaz Nedjat, Parvin Mirmiran, Nazanin Moslehi, Nasrin Mansournia, Mahyar Etminan, Mohammad Ali Mansournia, and Lawrence C. McCandless. "Effects of food items and related nutrients on metabolic syndrome using Bayesian multilevel modelling using the Tehran Lipid and Glucose Study (TLGS): a cohort study." BMJ Open 8, no. 12 (December 2018): e020642. http://dx.doi.org/10.1136/bmjopen-2017-020642.

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ObjectivesDiet and nutrition might play an important role in the aetiology of metabolic syndrome (MetS). Most studies that examine the effects of food intake on MetS have used conventional statistical analyses which usually investigate only a limited number of food items and are subject to sparse data bias. This study was undertaken with the goal of investigating the concurrent effect of numerous food items and related nutrients on the incidence of MetS using Bayesian multilevel modelling which can control for sparse data bias.DesignProspective cohort study.SettingThis prospective study was a subcohort of the Tehran Lipid and Glucose Study. We analysed dietary intake as well as pertinent covariates for cohort members in the fourth (2008–2011) and fifth (2011–2014) follow-up examinations. We fitted Bayesian multilevel model and compared the results with two logistic regression models: (1) full model which included all variables and (2) reduced model through backward selection of dietary variables.Participants3616 healthy Iranian adults, aged ≥20 years.Primary and secondary outcome measuresIncident cases of MetS.ResultsBayesian multilevel approach produced results that were more precise and biologically plausible compared with conventional logistic regression models. The OR and 95% confidence limits for the effects of the four foods comparing the Bayesian multilevel with the full conventional model were as follows: (1) noodle soup (1.20 (0.67 to 2.14) vs 1.91 (0.65 to 5.64)), (2) beans (0.96 (0.5 to 1.85) vs 0.55 (0.03 to 11.41)), (3) turnip (1.23 (0.68 to 2.23) vs 2.48 (0.82 to 7.52)) and (4) eggplant (1.01 (0.51 to 2.00) vs 1 09 396 (0.152×10–6to 768×1012)). For most food items, the Bayesian multilevel analysis gave narrower confidence limits than both logistic regression models, and hence provided the highest precision.ConclusionsThis study demonstrates that conventional regression methods do not perform well and might even be biased when assessing highly correlated exposures such as food items in dietary epidemiological studies. Despite the complexity of the Bayesian multilevel models and their inherent assumptions, this approach performs superior to conventional statistical models in studies that examine multiple nutritional exposures that are highly correlated.

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Li, Ang, Luis Pericchi, and Kun Wang. "Objective Bayesian Inference in Probit Models with Intrinsic Priors Using Variational Approximations." Entropy 22, no. 5 (April 30, 2020): 513. http://dx.doi.org/10.3390/e22050513.

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There is not much literature on objective Bayesian analysis for binary classification problems, especially for intrinsic prior related methods. On the other hand, variational inference methods have been employed to solve classification problems using probit regression and logistic regression with normal priors. In this article, we propose to apply the variational approximation on probit regression models with intrinsic prior. We review the mean-field variational method and the procedure of developing intrinsic prior for the probit regression model. We then present our work on implementing the variational Bayesian probit regression model using intrinsic prior. Publicly available data from the world’s largest peer-to-peer lending platform, LendingClub, will be used to illustrate how model output uncertainties are addressed through the framework we proposed. With LendingClub data, the target variable is the final status of a loan, either charged-off or fully paid. Investors may very well be interested in how predictive features like FICO, amount financed, income, etc. may affect the final loan status.

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Simmonds, Mark C., and Julian PT Higgins. "A general framework for the use of logistic regression models in meta-analysis." Statistical Methods in Medical Research 25, no. 6 (July 11, 2016): 2858–77. http://dx.doi.org/10.1177/0962280214534409.

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Where individual participant data are available for every randomised trial in a meta-analysis of dichotomous event outcomes, “one-stage” random-effects logistic regression models have been proposed as a way to analyse these data. Such models can also be used even when individual participant data are not available and we have only summary contingency table data. One benefit of this one-stage regression model over conventional meta-analysis methods is that it maximises the correct binomial likelihood for the data and so does not require the common assumption that effect estimates are normally distributed. A second benefit of using this model is that it may be applied, with only minor modification, in a range of meta-analytic scenarios, including meta-regression, network meta-analyses and meta-analyses of diagnostic test accuracy. This single model can potentially replace the variety of often complex methods used in these areas. This paper considers, with a range of meta-analysis examples, how random-effects logistic regression models may be used in a number of different types of meta-analyses. This one-stage approach is compared with widely used meta-analysis methods including Bayesian network meta-analysis and the bivariate and hierarchical summary receiver operating characteristic (ROC) models for meta-analyses of diagnostic test accuracy.

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Wang, Zhiqiang. "Two Postestimation Commands for Assessing Confounding Effects in Epidemiological Studies." Stata Journal: Promoting communications on statistics and Stata 7, no. 2 (June 2007): 183–96. http://dx.doi.org/10.1177/1536867x0700700203.

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Confounding is a major issue in observational epidemiological studies. This paper describes two postestimation commands for assessing confounding effects. One command (confall) displays and plots all possible effect estimates against one of p-value, Akaike information criterion, or Bayesian information criterion. This computing-intensive procedure allows researchers to inspect the variability of the effect estimates from various possible models. Another command (chest) uses a stepwise approach to identify variables that have substantially changed the effect estimate. Both commands can be used after most common estimation commands in epidemiological studies, such as logistic regression, conditional logistic regression, Poisson regression, linear regression, and Cox proportional hazards models.

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Gillies, Christopher E., Theodore S. Jennaro, Michael A. Puskarich, Ruchi Sharma, Kevin R. Ward, Xudong Fan, Alan E. Jones, and Kathleen A. Stringer. "A Multilevel Bayesian Approach to Improve Effect Size Estimation in Regression Modeling of Metabolomics Data Utilizing Imputation with Uncertainty." Metabolites 10, no. 8 (August 6, 2020): 319. http://dx.doi.org/10.3390/metabo10080319.

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To ensure scientific reproducibility of metabolomics data, alternative statistical methods are needed. A paradigm shift away from the p-value toward an embracement of uncertainty and interval estimation of a metabolite’s true effect size may lead to improved study design and greater reproducibility. Multilevel Bayesian models are one approach that offer the added opportunity of incorporating imputed value uncertainty when missing data are present. We designed simulations of metabolomics data to compare multilevel Bayesian models to standard logistic regression with corrections for multiple hypothesis testing. Our simulations altered the sample size and the fraction of significant metabolites truly different between two outcome groups. We then introduced missingness to further assess model performance. Across simulations, the multilevel Bayesian approach more accurately estimated the effect size of metabolites that were significantly different between groups. Bayesian models also had greater power and mitigated the false discovery rate. In the presence of increased missing data, Bayesian models were able to accurately impute the true concentration and incorporating the uncertainty of these estimates improved overall prediction. In summary, our simulations demonstrate that a multilevel Bayesian approach accurately quantifies the estimated effect size of metabolite predictors in regression modeling, particularly in the presence of missing data.

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Kaplan, David, and Chansoon Lee. "Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments." Evaluation Review 42, no. 4 (April 11, 2018): 423–57. http://dx.doi.org/10.1177/0193841x18761421.

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This article provides a review of Bayesian model averaging as a means of optimizing the predictive performance of common statistical models applied to large-scale educational assessments. The Bayesian framework recognizes that in addition to parameter uncertainty, there is uncertainty in the choice of models themselves. A Bayesian approach to addressing the problem of model uncertainty is the method of Bayesian model averaging. Bayesian model averaging searches the space of possible models for a set of submodels that satisfy certain scientific principles and then averages the coefficients across these submodels weighted by each model’s posterior model probability (PMP). Using the weighted coefficients for prediction has been shown to yield optimal predictive performance according to certain scoring rules. We demonstrate the utility of Bayesian model averaging for prediction in education research with three examples: Bayesian regression analysis, Bayesian logistic regression, and a recently developed approach for Bayesian structural equation modeling. In each case, the model-averaged estimates are shown to yield better prediction of the outcome of interest than any submodel based on predictive coverage and the log-score rule. Implications for the design of large-scale assessments when the goal is optimal prediction in a policy context are discussed.

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Sakai, S., K. Kobayashi, J. Nakamura, S. Toyabe, and K. Akazawa. "Accuracy in the Diagnostic Prediction of Acute Appendicitis Based on the Bayesian Network Model." Methods of Information in Medicine 46, no. 06 (2007): 723–26. http://dx.doi.org/10.3414/me9066.

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Summary Objectives : The diagnosis of acute appendicitis is difficult, and a diagnostic error will often lead to either a perforation or the removal of a normal appendix. In this study, we constructed a Bayesian network model for the diagnosis of acute appendicitis and compared the diagnostic accuracy with other diagnostic models, such as the naive Bayes model, an artificial neural network model, and a logistic regression model. Methods : The data from 169 patients, who suffered from acute abdominal pain and who were suspected of having an acute appendicitis, were analyzed in this study. Nine variables were used for the evaluation of the accuracy of the four models for the diagnosis of an acute appendicitis. The naive Bayes model, the Bayesian network model, an artificial neural network model, and a logistic regression model were used i this study for the diagnosis of acute appendicitis. These four models were validated by using the “632 + bootstrap method” for resampling. The levels of accuracy of the four models for diagnosis were compared by the error rates and by the areas under the receiver operating characteristic curves. Results : Through the course of illness, 50.9% (86 of 169) of the patients were diagnosed as having an acute appendicitis. The error rate was the lowest in the Bayesian network model, as compared with the other diagnostic models. The area under the receiver operating characteristic curve analysis also showed that the Bayesian network model provided the most reliable results. Conclusion : The Bayesian network model provided the most accurate results in comparison to other models for the diagnosis of acute appendicitis.

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Proper, Jennifer, John Connett, and Thomas Murray. "Alternative models and randomization techniques for Bayesian response-adaptive randomization with binary outcomes." Clinical Trials 18, no. 4 (April 30, 2021): 417–26. http://dx.doi.org/10.1177/17407745211010139.

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Background: Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and increasing sample size requirements. The implementation of these designs using the Thompson sampling methods has generally assumed a simple beta-binomial probability model in the literature; however, the effect of these choices on the resulting design operating characteristics relative to other reasonable alternatives has not been fully examined. Motivated by the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial, we posit that a logistic probability model coupled with an urn or permuted block randomization method will alleviate some of the practical limitations engendered by the conventional implementation of a two-arm Bayesian response-adaptive design with binary outcomes. In this article, we discuss up to what extent this solution works and when it does not. Methods: A computer simulation study was performed to evaluate the relative merits of a Bayesian response-adaptive design for the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial using the Thompson sampling methods based on a logistic regression probability model coupled with either an urn or permuted block randomization method that limits deviations from the evolving target allocation ratio. The different implementations of the response-adaptive design were evaluated for type I error rate control across various null response rates and power, among other performance metrics. Results: The logistic regression probability model engenders smaller average sample sizes with similar power, better control over type I error rate, and more favorable treatment arm sample size distributions than the conventional beta-binomial probability model, and designs using the alternative randomization methods have a negligible chance of a sample size imbalance in the wrong direction. Conclusion: Pairing the logistic regression probability model with either of the alternative randomization methods results in a much improved response-adaptive design in regard to important operating characteristics, including type I error rate control and the risk of a sample size imbalance in favor of the inferior treatment.

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Zhao, Jingyuan, and Zehua Chen. "A Two-Stage Penalized Logistic Regression Approach to Case-Control Genome-Wide Association Studies." Journal of Probability and Statistics 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/642403.

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We propose a two-stage penalized logistic regression approach to case-control genome-wide association studies. This approach consists of a screening stage and a selection stage. In the screening stage, main-effect and interaction-effect features are screened by usingL1-penalized logistic like-lihoods. In the selection stage, the retained features are ranked by the logistic likelihood with the smoothly clipped absolute deviation (SCAD) penalty (Fan and Li, 2001) and Jeffrey’s Prior penalty (Firth, 1993), a sequence of nested candidate models are formed, and the models are assessed by a family of extended Bayesian information criteria (J. Chen and Z. Chen, 2008). The proposed approach is applied to the analysis of the prostate cancer data of the Cancer Genetic Markers of Susceptibility (CGEMS) project in the National Cancer Institute, USA. Simulation studies are carried out to compare the approach with the pair-wise multiple testing approach (Marchini et al. 2005) and the LASSO-patternsearch algorithm (Shi et al. 2007).

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Pawełek, Barbara, Jadwiga Kostrzewska, Maciej Kostrzewski, and Krzysztof Gałuszka. "Evaluation of the financial condition of companies after the announcement of arrangement bankruptcy: application of the classical and Bayesian logistic regression." Przegląd Statystyczny 67, no. 1 (August 18, 2020): 5–32. http://dx.doi.org/10.5604/01.3001.0014.1782.

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The aim of this paper is to present the results of an assessment of the financial condition of companies from the construction industry after the announcement of arrangement bankruptcy, in comparison to the condition of healthy companies. The logistic regression model estimated by means of the maximum likelihood method and the Bayesian approach were used. The first achievement of our study is the assessment of the financial condition of companies from the construction industry after the announcement of bankruptcy. The second achievement is the application of an approach combining the classical and Bayesian logistic regression models to assess the financial condition of companies in the years following the declaration of bankruptcy, and the presentation of the benefits of such a combination. The analysis described in the paper, carried out in most part by means of the ML logistic regression model, was supplemented with information yielded by the application of the Bayesian approach. In particular, the analysis of the shape of the posterior distribution of the repeat bankruptcy probability makes it possible, in some cases, to observe that the financial condition of a company is not clear, despite clear assessments made on the basis of the point estimations.

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Mu, J., S. Cui, and P. Reinartz. "BUILDING DETECTION USING AERIAL IMAGES AND DIGITAL SURFACE MODELS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 159–65. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-159-2017.

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In this paper a method for building detection in aerial images based on variational inference of logistic regression is proposed. It consists of three steps. In order to characterize the appearances of buildings in aerial images, an effective bag-of-Words (BoW) method is applied for feature extraction in the first step. In the second step, a classifier of logistic regression is learned using these local features. The logistic regression can be trained using different methods. In this paper we adopt a fully Bayesian treatment for learning the classifier, which has a number of obvious advantages over other learning methods. Due to the presence of hyper prior in the probabilistic model of logistic regression, approximate inference methods have to be applied for prediction. In order to speed up the inference, a variational inference method based on mean field instead of stochastic approximation such as Markov Chain Monte Carlo is applied. After the prediction, a probabilistic map is obtained. In the third step, a fully connected conditional random field model is formulated and the probabilistic map is used as the data term in the model. A mean field inference is utilized in order to obtain a binary building mask. A benchmark data set consisting of aerial images and digital surfaced model (DSM) released by ISPRS for 2D semantic labeling is used for performance evaluation. The results demonstrate the effectiveness of the proposed method.

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Elwood, Richard W. "Calculating Probability in Sex Offender Risk Assessment." International Journal of Offender Therapy and Comparative Criminology 62, no. 5 (November 18, 2016): 1262–80. http://dx.doi.org/10.1177/0306624x16677784.

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Risk is the probability of an adverse event or outcome. In a previous article, I compared the Bayesian and Frequentist models of defining probability. This article compares the Bayesian and regression models of quantifying probability. Both approaches are widely used in the biomedical and behavioral sciences even though they yield different results. No consensus has emerged as to which is more appropriate. The choice between them remains controversial. This article concludes that the Bayesian model provides a viable alternative to logistic regression and may be more useful in quantifying the absolute recidivism risk of individual sex offenders. It shows how evaluators can easily calculate Bayesian probabilities and their associated credible intervals from an actuarial data set. Last, the article proposes a forensic practice guideline that evaluators do not conclude that an offender meets an absolute risk threshold unless the subject’s risk exceeds the threshold by a credible margin of error.

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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 Bayesian multilevel logistic regression models. The data used for this study comes from the 2016 Ethiopian Demographic and Health Survey which was conducted by the Central Statistical Agency (CSA). The statistical method of data analysis used for this study is the Bayesian multilevel binary logistic regression model in general and the Bayesian multilevel logistic regression for the random coefficient model in particular. The convergences of parameters are estimated by using Markov chain Monte-Carlo (MCMC) using SPSS and MLwiN software. The descriptive result revealed that out of the 7171 women who are supposed to use ANC services, 2479 (34.6%) women were not receiving ANC services, while 4692 (65.4%) women were receiving ANC services. Moreover, women in the Somali and Afar regions are the least users of ANC. Using the Bayesian multilevel binary logistic regression of random coefficient model factors, place of residence, religion, educational attainment of women, husband educational level, employment status of husband, beat, household wealth index, and birth order were found to be the significant factors for usage of ANC. Regional variation in the usage of ANC was significant.

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Młynarczyk, Dorota, Carmen Armero, Virgilio Gómez-Rubio, and Pedro Puig. "Bayesian Analysis of Population Health Data." Mathematics 9, no. 5 (March 9, 2021): 577. http://dx.doi.org/10.3390/math9050577.

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The analysis of population-wide datasets can provide insight on the health status of large populations so that public health officials can make data-driven decisions. The analysis of such datasets often requires highly parameterized models with different types of fixed and random effects to account for risk factors, spatial and temporal variations, multilevel effects and other sources on uncertainty. To illustrate the potential of Bayesian hierarchical models, a dataset of about 500,000 inhabitants released by the Polish National Health Fund containing information about ischemic stroke incidence for a 2-year period is analyzed using different types of models. Spatial logistic regression and survival models are considered for analyzing the individual probabilities of stroke and the times to the occurrence of an ischemic stroke event. Demographic and socioeconomic variables as well as drug prescription information are available at an individual level. Spatial variation is considered by means of region-level random effects.

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Aljarallah, Reem, and Samer A. Kharroubi. "Use of Bayesian Markov Chain Monte Carlo Methods to Model Kuwait Medical Genetic Center Data: An Application to Down Syndrome and Mental Retardation." Mathematics 9, no. 3 (January 27, 2021): 248. http://dx.doi.org/10.3390/math9030248.

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Logit, probit and complementary log-log models are the most widely used models when binary dependent variables are available. Conventionally, these models have been frequentists. This paper aims to demonstrate how such models can be implemented relatively quickly and easily from a Bayesian framework using Gibbs sampling Markov chain Monte Carlo simulation methods in WinBUGS. We focus on the modeling and prediction of Down syndrome (DS) and Mental retardation (MR) data from an observational study at Kuwait Medical Genetic Center over a 30-year time period between 1979 and 2009. Modeling algorithms were used in two distinct ways; firstly, using three different methods at the disease level, including logistic, probit and cloglog models, and, secondly, using bivariate logistic regression to study the association between the two diseases in question. The models are compared in terms of their predictive ability via R2, adjusted R2, root mean square error (RMSE) and Bayesian Deviance Information Criterion (DIC). In the univariate analysis, the logistic model performed best, with R2 (0.1145), adjusted R2 (0.114), RMSE (0.3074) and DIC (7435.98) for DS, and R2 (0.0626), adjusted R2 (0.0621), RMSE (0.4676) and DIC (23120) for MR. In the bivariate case, results revealed that 7 and 8 out of the 10 selected covariates were significantly associated with DS and MR respectively, whilst none were associated with the interaction between the two outcomes. Bayesian methods are more flexible in handling complex non-standard models as well as they allow model fit and complexity to be assessed straightforwardly for non-nested hierarchical models.

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Nguefack-Tsague, Georges, and Ingo Bulla. "A Focused Bayesian Information Criterion." Advances in Statistics 2014 (October 14, 2014): 1–8. http://dx.doi.org/10.1155/2014/504325.

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Myriads of model selection criteria (Bayesian and frequentist) have been proposed in the literature aiming at selecting a single model regardless of its intended use. An honorable exception in the frequentist perspective is the “focused information criterion” (FIC) aiming at selecting a model based on the parameter of interest (focus). This paper takes the same view in the Bayesian context; that is, a model may be good for one estimand but bad for another. The proposed method exploits the Bayesian model averaging (BMA) machinery to obtain a new criterion, the focused Bayesian model averaging (FoBMA), for which the best model is the one whose estimate is closest to the BMA estimate. In particular, for two models, this criterion reduces to the classical Bayesian model selection scheme of choosing the model with the highest posterior probability. The new method is applied in linear regression, logistic regression, and survival analysis. This criterion is specially important in epidemiological studies in which the objective is often to determine a risk factor (focus) for a disease, adjusting for potential confounding factors.

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Kang, Hejun, and Shelley M. Alexander. "Relative accuracy of spatial predictive models for lynx Lynx canadensis derived using logistic regression-AIC, multiple criteria evaluation and Bayesian approaches." Current Zoology 55, no. 1 (February 1, 2009): 28–40. http://dx.doi.org/10.1093/czoolo/55.1.28.

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Abstract We compared probability surfaces derived using one set of environmental variables in three Geographic Information Systems (GIS) -based approaches: logistic regression and Akaike’s Information Criterion (AIC), Multiple Criteria Evaluation (MCE), and Bayesian Analysis (specifically Dempster-Shafer theory). We used lynx Lynx canadensis as our focal species, and developed our environment relationship model using track data collected in Banff National Park, Alberta, Canada, during winters from 1997 to 2000. The accuracy of the three spatial models were compared using a contingency table method. We determined the percentage of cases in which both presence and absence points were correctly classified (overall accuracy), the failure to predict a species where it occurred (omission error) and the prediction of presence where there was absence (commission error). Our overall accuracy showed the logistic regression approach was the most accurate (74.51%). The multiple criteria evaluation was intermediate (39.22%), while the Dempster-Shafer (D-S) theory model was the poorest (29.90%). However, omission and commission error tell us a different story: logistic regression had the lowest commission error, while D-S theory produced the lowest omission error. Our results provide evidence that habitat modellers should evaluate all three error measures when ascribing confidence in their model. We suggest that for our study area at least, the logistic regression model is optimal. However, where sample size is small or the species is very rare, it may also be useful to explore and/or use a more ecologically cautious modelling approach (e.g. Dempster-Shafer) that would over-predict, protect more sites, and thereby minimize the risk of missing critical habitat in conservation plans.

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Primo, Cristina, Christopher A. T. Ferro, Ian T. Jolliffe, and David B. Stephenson. "Calibration of Probabilistic Forecasts of Binary Events." Monthly Weather Review 137, no. 3 (March 1, 2009): 1142–49. http://dx.doi.org/10.1175/2008mwr2579.1.

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Abstract Probabilistic forecasts of atmospheric variables are often given as relative frequencies obtained from ensembles of deterministic forecasts. The detrimental effects of imperfect models and initial conditions on the quality of such forecasts can be mitigated by calibration. This paper shows that Bayesian methods currently used to incorporate prior information can be written as special cases of a beta-binomial model and correspond to a linear calibration of the relative frequencies. These methods are compared with a nonlinear calibration technique (i.e., logistic regression) using real precipitation forecasts. Calibration is found to be advantageous in all cases considered, and logistic regression is preferable to linear methods.

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Fisher, Charles K., and Pankaj Mehta. "Bayesian Feature Selection with Strongly Regularizing Priors Maps to the Ising Model." Neural Computation 27, no. 11 (November 2015): 2411–22. http://dx.doi.org/10.1162/neco_a_00780.

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Identifying small subsets of features that are relevant for prediction and classification tasks is a central problem in machine learning and statistics. The feature selection task is especially important, and computationally difficult, for modern data sets where the number of features can be comparable to or even exceed the number of samples. Here, we show that feature selection with Bayesian inference takes a universal form and reduces to calculating the magnetizations of an Ising model under some mild conditions. Our results exploit the observation that the evidence takes a universal form for strongly regularizing priors—priors that have a large effect on the posterior probability even in the infinite data limit. We derive explicit expressions for feature selection for generalized linear models, a large class of statistical techniques that includes linear and logistic regression. We illustrate the power of our approach by analyzing feature selection in a logistic regression-based classifier trained to distinguish between the letters B and D in the notMNIST data set.

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Venkatesh, Veeramuthu, M. M. Anishin Raj, K. Mohamed Sajith, R. Anushiadevi, and T. Suriya Praba. "A precision-based diagnostic model ADOBE-accurate detection of breast cancer using logistic regression approach." Journal of Intelligent & Fuzzy Systems 39, no. 6 (December 4, 2020): 8419–26. http://dx.doi.org/10.3233/jifs-189160.

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Cancer is a prevalent disease which comes in several forms. The need of the hour in cancer research is to be able to diagnose cancer in its early stages. The furthermost common forms of cancer among women us breast cancer. In recent times, there has been a drastic increase in the number of breast cancer cases among women. As a wide range of medical data is available in electronic form and with easy access to Machine Learning(ML) techniques disease progression risk evaluation has been made easier. These ML tools can aid in giving us complex insights from the massive amounts of available data. Some of the techniques used for developing predictive models for perfect decision making in cancer research are Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs), and Decision Trees (DTs). Although it is acceptable that ML is used to predict cancer progression, we need some level of validation. In this paper, we have come up with a review of several ML methods in modelling cancer progression. We discuss several predictive models based on supervised ML techniques and the inputs given by users, along with the data available. The results that were obtained from Logistic Regression show us that this method gave a significantly higher accuracy than most other classifiers. The best accuracy is 98.2%, however, the best precision and recall is 100 and 98.60% correspondingly.

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Theijssen, Daphne, Louis ten Bosch, Lou Boves, Bert Cranen, and Hans van Halteren. "Choosing alternatives: Using Bayesian Networks and memory-based learning to study the dative alternation." Corpus Linguistics and Linguistic Theory 9, no. 2 (October 25, 2013): 227–62. http://dx.doi.org/10.1515/cllt-2013-0007.

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AbstractIn existing research on syntactic alternations such as the dative alternation, (give her the apple vs. give the apple to her), the linguistic data is often analysed with the help of logistic regression models. In this article, we evaluate the use of logistic regression for this type of research, and present two different approaches: Bayesian Networks and Memory-based learning. For the Bayesian Network, we use the higher-level semantic features suggested in the literature, while we limit ourselves to lexical items in the memory-based approach. We evaluate the suitability of the three approaches by applying them to a large data set (>11,000 instances) extracted from the British National Corpus, and comparing their quality in terms of classification accuracy, their interpretability in the context of linguistic research, and their actual classification of individual cases. Our main finding is that the classifications are very similar across the three approaches, also when employing lexical items instead of the higher-level features, because most of the alternation is determined by the verb and the length of the two objects (here: her and the apple).

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Makatjane, Katleho, Ntebogang Moroke, and Diteboho Xaba. "On the Prediction of the Inflation Crises of South Africa Using Markov-Switching Bayesian Vector Autoregressive and Logistic Regression Models." Journal of Social Economics Research 5, no. 1 (2018): 10–28. http://dx.doi.org/10.18488/journal.35.2018.51.10.28.

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Nguyen, Danh Thanh, Ngo Van Dau, and Dung Quoc Ta. "Applying logistic regression method to determine combinatorial optimization of landslide-related factors and construct landslide hazard map in Khanh Vinh district, Khanh Hoa Province." Science and Technology Development Journal 20, K4 (July 31, 2017): 76–83. http://dx.doi.org/10.32508/stdj.v20ik4.1121.

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The purpose of this study is to produce landslide hazard map in Khanh Vinh district, Khanh Hoa province using logistic regression method integrated with GIS analytical tools. The spatial relationship between landslide-related factors such as topography; lithology; vegetation; maximum precipitation in year; distance from roads; distance from drainages; distance from faults and the distribution of landslides were used in the landslide hazard analyses. Using success rate and prediction rate curve assess the fit and accuracy of logistic regression method. The results show that this method have the goodness of fit and the high accuracy (Areas Under Curves - AUC = 0.8 ~ 0.9). Bayesian Model Average (BMA) of the R statistical software was applied to identify the most influential factors and the combinatorial optimization models of landslide-related factors. There are four the most important landslide-related factors and five combinatorial optimization models of landslide-related factors. Model 3 (slope angle, slope aspect, altitude, distance from roads and maximum precipitation in year) is the best optimization.

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Masuda, Michele M., and Robert P. Stone. "Bayesian logistic mixed-effects modelling of transect data: relating red tree coral presence to habitat characteristics." ICES Journal of Marine Science 72, no. 9 (September 18, 2015): 2674–83. http://dx.doi.org/10.1093/icesjms/fsv163.

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Abstract The collection of continuous data on transects is a common practice in habitat and fishery stock assessments; however, the application of standard regression models that assume independence to serially correlated data is problematic. We show that generalized linear mixed models (GLMMs), i.e. generalized linear models for longitudinal data, that are normally used for studies performed over time can also be applied to other types of clustered or serially correlated data. We apply a specific GLMM for longitudinal data, a hierarchical Bayesian logistic mixed-effects model (BLMM), to a marine ecology dataset obtained from submersible video recordings of the seabed on transects at two sites in the Gulf of Alaska. The BLMM was effective in relating the presence of red tree corals (Primnoa pacifica; i.e. binary data) to habitat characteristics: the presence of red tree corals is highly associated with bedrock as the primary substrate (estimated odds ratio 9–19), high to very high seabed roughness (estimated odds ratio 3–5), and medium to high slope (estimated odds ratio 2–3). The covariate depth was less important at the sites. We also demonstrate and compare two methods of model checking: full and mixed posterior predictive assessments, the latter of which provided a more realistic assessment, and we calculate the variance partition coefficient for reporting the variation explained by multiple levels of the hierarchical model.

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Journal articles on the topic 'Bayesian logistic regression models'

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