Relevant bibliographies by topics / Bayesian logistic regression models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bayesian logistic regression models'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Bayesian logistic regression models.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Bayesian logistic regression models"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
9

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Bayesian logistic regression models"

1

Webster, Gregg. "Bayesian logistic regression models for credit scoring." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1005538.

Full text
Abstract:
The Bayesian approach to logistic regression modelling for credit scoring is useful when there are data quantity issues. Data quantity issues might occur when a bank is opening in a new location or there is change in the scoring procedure. Making use of prior information (available from the coefficients estimated on other data sets, or expert knowledge about the coefficients) a Bayesian approach is proposed to improve the credit scoring models. To achieve this, a data set is split into two sets, “old” data and “new” data. Priors are obtained from a model fitted on the “old” data. This model is assumed to be a scoring model used by a financial institution in the current location. The financial institution is then assumed to expand into a new economic location where there is limited data. The priors from the model on the “old” data are then combined in a Bayesian model with the “new” data to obtain a model which represents all the available information. The predictive performance of this Bayesian model is compared to a model which does not make use of any prior information. It is found that the use of relevant prior information improves the predictive performance when the size of the “new” data is small. As the size of the “new” data increases, the importance of including prior information decreases
APA, Harvard, Vancouver, ISO, and other styles
2

Richmond, James Howard. "Bayesian Logistic Regression Models for Software Fault Localization." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1326658577.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Ozturk, Olcay. "Bayesian Semiparametric Models For Nonignorable Missing Datamechanisms In Logistic Regression." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613241/index.pdf.

Full text
Abstract:
In this thesis, Bayesian semiparametric models for the missing data mechanisms of nonignorably missing covariates in logistic regression are developed. In the missing data literature, fully parametric approach is used to model the nonignorable missing data mechanisms. In that approach, a probit or a logit link of the conditional probability of the covariate being missing is modeled as a linear combination of all variables including the missing covariate itself. However, nonignorably missing covariates may not be linearly related with the probit (or logit) of this conditional probability. In our study, the relationship between the probit of the probability of the covariate being missing and the missing covariate itself is modeled by using a penalized spline regression based semiparametric approach. An efficient Markov chain Monte Carlo (MCMC) sampling algorithm to estimate the parameters is established. A WinBUGS code is constructed to sample from the full conditional posterior distributions of the parameters by using Gibbs sampling. Monte Carlo simulation experiments under different true missing data mechanisms are applied to compare the bias and efficiency properties of the resulting estimators with the ones from the fully parametric approach. These simulations show that estimators for logistic regression using semiparametric missing data models maintain better bias and efficiency properties than the ones using fully parametric missing data models when the true relationship between the missingness and the missing covariate has a nonlinear form. They are comparable when this relationship has a linear form.
APA, Harvard, Vancouver, ISO, and other styles
4

Kynn, Mary. "Eliciting Expert Knowledge for Bayesian Logistic Regression in Species Habitat Modelling." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16041/.

Full text
Abstract:
This research aims to develop a process for eliciting expert knowledge and incorporating this knowledge as prior distributions for a Bayesian logistic regression model. This work was motivated by the need for less data reliant methods of modelling species habitat distributions. A comprehensive review of the research from both cognitive psychology and the statistical literature provided specific recommendations for the creation of an elicitation scheme. These were incorporated into the design of a Bayesian logistic regression model and accompanying elicitation scheme. This model and scheme were then implemented as interactive, graphical software called ELICITOR created within the BlackBox Component Pascal environment. This software was specifically written to be compatible with existing Bayesian analysis software, winBUGS as an odd-on component. The model, elicitation scheme and software were evaluated through five case studies of various fauna and flora species. For two of these there were sufficient data for a comparison of expert and data-driven models. The case studies confirmed that expert knowledge can be quantified and formally incorporated into a logistic regression model. Finally, they provide a basis for a thorough discussion of the model, scheme and software extensions and lead to recommendations for elicitation research.
APA, Harvard, Vancouver, ISO, and other styles
5

Paz, Rosineide Fernando da. "Alternative regression models to beta distribution under bayesian approach." Universidade Federal de São Carlos, 2017. https://repositorio.ufscar.br/handle/ufscar/9146.

Full text
Abstract:
Submitted by Aelson Maciera (aelsoncm@terra.com.br) on 2017-09-27T18:09:58Z No. of bitstreams: 1 TeseRFP.pdf: 2142415 bytes, checksum: 8dcd8615da0b442e9f1b52f35364715b (MD5)
Approved for entry into archive by Ronildo Prado (producaointelectual.bco@ufscar.br) on 2017-10-10T18:16:14Z (GMT) No. of bitstreams: 1 TeseRFP.pdf: 2142415 bytes, checksum: 8dcd8615da0b442e9f1b52f35364715b (MD5)
Approved for entry into archive by Ronildo Prado (producaointelectual.bco@ufscar.br) on 2017-10-10T18:16:22Z (GMT) No. of bitstreams: 1 TeseRFP.pdf: 2142415 bytes, checksum: 8dcd8615da0b442e9f1b52f35364715b (MD5)
Made available in DSpace on 2017-10-10T18:23:04Z (GMT). No. of bitstreams: 1 TeseRFP.pdf: 2142415 bytes, checksum: 8dcd8615da0b442e9f1b52f35364715b (MD5) Previous issue date: 2017-08-25
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
The Beta distribution is a bounded domain distribution which has dominated the modeling the distribution of random variable that assume value between 0 and 1. Bounded domain distributions arising in various situations such as rates, proportions and index. Motivated by an analysis of electoral votes percentages (where a distribution with support on the positive real numbers was used, although a distribution with limited support could be more suitable) we focus on alternative distributions to Beta distribution with emphasis in regression models. In this work, initially we present the Simplex mixture model as a flexible model to modeling the distribution of bounded random variable then we extend the model to the context of regression models with the inclusion of covariates. The parameters estimation is discussed for both models considering Bayesian inference. We apply these models to simulated data sets in order to investigate the performance of the estimators. The results obtained were satisfactory for all the cases investigated. Finally, we introduce a parameterization of the L-Logistic distribution to be used in the context of regression models and we extend it to a mixture of mixed models.
A distribuição beta é uma distribuição com suporte limitado que tem dominado a modelagem de variáveis aleatórias que assumem valores entre 0 e 1. Distribuições com suporte limitado surgem em várias situações como em taxas, proporções e índices. Motivados por uma análise de porcentagens de votos eleitorais, em que foi assumida uma distribuição com suporte nos números reais positivos quando uma distribuição com suporte limitado seira mais apropriada, focamos em modelos alternativos a distribuição beta com enfase em modelos de regressão. Neste trabalho, apresentamos, inicialmente, um modelo de mistura de distribuições Simplex como um modelo flexível para modelar a distribuição de variáveis aleatórias que assumem valores em um intervalo limitado, em seguida estendemos o modelo para o contexto de modelos de regressão com a inclusão de covariáveis. A estimação dos parâmetros foi discutida para ambos os modelos, considerando o método bayesiano. Aplicamos os dois modelos a dados simulados para investigarmos a performance dos estimadores usados. Os resultados obtidos foram satisfatórios para todos os casos investigados. Finalmente, introduzimos a distribuição L-Logistica no contexto de modelos de regressão e posteriormente estendemos este modelo para o contexto de misturas de modelos de regressão mista.
APA, Harvard, Vancouver, ISO, and other styles
6

Zimmer, Zachary. "Predicting NFL Games Using a Seasonal Dynamic Logistic Regression Model." VCU Scholars Compass, 2006. http://scholarscompass.vcu.edu/etd_retro/97.

Full text
Abstract:
The article offers a dynamic approach for predicting the outcomes of NFL games using the NFL games from 2002-2005. A logistic regression model is used to predict the probability that one team defeats another. The parameters of this model are the strengths of the teams and a home field advantage factor. Since it assumed that a team's strength is time dependent, the strength parameters were assigned a seasonal time series process. The best model was selected using all the data from 2002 through the first seven weeks of 2005. The last weeks of 2005 were used for prediction estimates.
APA, Harvard, Vancouver, ISO, and other styles
7

Fu, Shuting. "Bayesian Logistic Regression Model with Integrated Multivariate Normal Approximation for Big Data." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/451.

Full text
Abstract:
The analysis of big data is of great interest today, and this comes with challenges of improving precision and efficiency in estimation and prediction. We study binary data with covariates from numerous small areas, where direct estimation is not reliable, and there is a need to borrow strength from the ensemble. This is generally done using Bayesian logistic regression, but because there are numerous small areas, the exact computation for the logistic regression model becomes challenging. Therefore, we develop an integrated multivariate normal approximation (IMNA) method for binary data with covariates within the Bayesian paradigm, and this procedure is assisted by the empirical logistic transform. Our main goal is to provide the theory of IMNA and to show that it is many times faster than the exact logistic regression method with almost the same accuracy. We apply the IMNA method to the health status binary data (excellent health or otherwise) from the Nepal Living Standards Survey with more than 60,000 households (small areas). We estimate the proportion of Nepalese in excellent health condition for each household. For these data IMNA gives estimates of the household proportions as precise as those from the logistic regression model and it is more than fifty times faster (20 seconds versus 1,066 seconds), and clearly this gain is transferable to bigger data problems.
APA, Harvard, Vancouver, ISO, and other styles
8

Schoergendorfer, Angela. "BAYESIAN SEMIPARAMETRIC GENERALIZATIONS OF LINEAR MODELS USING POLYA TREES." UKnowledge, 2011. http://uknowledge.uky.edu/gradschool_diss/214.

Full text
Abstract:
In a Bayesian framework, prior distributions on a space of nonparametric continuous distributions may be defined using Polya trees. This dissertation addresses statistical problems for which the Polya tree idea can be utilized to provide efficient and practical methodological solutions. One problem considered is the estimation of risks, odds ratios, or other similar measures that are derived by specifying a threshold for an observed continuous variable. It has been previously shown that fitting a linear model to the continuous outcome under the assumption of a logistic error distribution leads to more efficient odds ratio estimates. We will show that deviations from the assumption of logistic error can result in great bias in odds ratio estimates. A one-step approximation to the Savage-Dickey ratio will be presented as a Bayesian test for distributional assumptions in the traditional logistic regression model. The approximation utilizes least-squares estimates in the place of a full Bayesian Markov Chain simulation, and the equivalence of inferences based on the two implementations will be shown. A framework for flexible, semiparametric estimation of risks in the case that the assumption of logistic error is rejected will be proposed. A second application deals with regression scenarios in which residuals are correlated and their distribution evolves over an ordinal covariate such as time. In the context of prediction, such complex error distributions need to be modeled carefully and flexibly. The proposed model introduces dependent, but separate Polya tree priors for each time point, thus pooling information across time points to model gradual changes in distributional shapes. Theoretical properties of the proposed model will be outlined, and its potential predictive advantages in simulated scenarios and real data will be demonstrated.
APA, Harvard, Vancouver, ISO, and other styles
9

Tang, Zhongwen. "LOF of logistic GEE models and cost efficient Bayesian optimal designs for nonlinear combinations of parameters in nonlinear regression models." Diss., Manhattan, Kan. : Kansas State University, 2008. http://hdl.handle.net/2097/1011.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Dikshit, Anubhav. "Omnichannel path to purchase : Viability of Bayesian Network as Market Attribution Models." Thesis, Linköpings universitet, Filosofiska fakulteten, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-165443.

Full text
Abstract:
Market attribution is the problem of interpreting the influence of advertisements onthe user’s decision process. Market attribution is a hard problem, and it happens to be asignificant reason for Google’s revenue. There are broadly two types of attribution models- data-driven and heuristics.This thesis focuses on the data driven attribution modeland explores the viability of using Bayesian Network as market attribution models andbenchmarks the performance against a logistic regression. The data used in this thesiswas prepossessed using undersampling technique. Furthermore, multiple techniques andalgorithms to learn and train Bayesian Network are explored and evaluated.For the given dataset, it was found that Bayesian Network can be used for market at-tribution modeling and that its performance is better than the baseline logistic model. Keywords: Market Attribution Model, Bayesian Network, Logistic Regression.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Bayesian logistic regression models"

1

Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. Iowa City, Iowa: ACT, Inc., 1997.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. Iowa City, Iowa: ACT, Inc., 1997.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Hilbe, Joseph. Logistic regression models. Boca Raton: Chapman & Hall/CRC, 2009.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Ronald, Christensen. Log-linear models and logistic regression. 2nd ed. New York: Springer, 1997.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

O'Connell, Ann. Logistic Regression Models for Ordinal Response Variables. 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., 2006. http://dx.doi.org/10.4135/9781412984812.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Regression modeling strategies: With applications to linear models, logistic regression, and survival analysis. New York: Springer, 2001.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Guttman, Irwin. Bayesian assessment of assumptions of regression analysis. Toronto: University of Toronto, Dept. of Statistics, 1988.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Bagchi, Parthasarathy. Bayesian assessment of assumptions of regression analysis. Toronto: University of Toronto, Dept. of Statistics, 1989.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Pilz, Jürgen. Bayesian estimation and experimental design in linear regression models. Chichester: Wiley, 1991.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Pilz, Jürgen. Bayesian estimation and experimental design in linear regression models. 2nd ed. Chichester: Wiley, 1991.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Bayesian logistic regression models"

1

Heumann, Christian, and Moritz Grenke. "An Efficient Model Averaging Procedure for Logistic Regression Models Using a Bayesian Estimator with Laplace Prior." In Statistical Modelling and Regression Structures, 79–90. Heidelberg: Physica-Verlag HD, 2009. http://dx.doi.org/10.1007/978-3-7908-2413-1_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Maneejuk, Paravee, Woraphon Yamaka, and Duentemduang Nachaingmai. "Bayesian Analysis of the Logistic Kink Regression Model Using Metropolis-Hastings Sampling." In Beyond Traditional Probabilistic Methods in Economics, 1073–83. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04200-4_78.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Friendly, Michael, David Meyer, and Achim Zeileis. "Logistic Regression Models." In Discrete Data Analysis with R, 261–322. Boca Raton : Taylor & Francis, 2016. | Series: Chapman & hall/CRC texts in statistical science series ; 120 | “A CRC title.”: Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b19022-10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Bisong, Ekaba. "Logistic Regression." In Building Machine Learning and Deep Learning Models on Google Cloud Platform, 243–50. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4470-8_20.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Niranjan, Mahesan. "On Sequential Bayesian Logistic Regression." In Neural Nets WIRN Vietri-99, 3–11. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0877-1_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Albert, Jim. "Regression Models." In Bayesian Computation with R, 205–34. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92298-0_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kelly, Dana, and Curtis Smith. "Bayesian Regression Models." In Springer Series in Reliability Engineering, 141–63. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-187-5_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Wilson, Jeffrey R., and Kent A. Lorenz. "Hierarchical Logistic Regression Models." In ICSA Book Series in Statistics, 201–24. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23805-0_10.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Hoffmann, John P. "A Brief Introduction to Logistic Regression." In Linear Regression Models, 337–54. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003162230-16.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Billio, Monica, Roberto Casarin, and Matteo Iacopini. "Bayesian Tensor Regression Models." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 149–53. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89824-7_28.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Bayesian logistic regression models"

1

Niranjan, M. "Sequential Bayesian computation of logistic regression models." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.759927.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Xu, Zuobing, and Ram Akella. "A bayesian logistic regression model for active relevance feedback." In the 31st annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390334.1390375.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Pavlyshenko, B. "Machine learning, linear and Bayesian models for logistic regression in failure detection problems." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840828.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Huttunen, Heikki, Tapio Manninen, and Jussi Tohka. "Bayesian error estimation and model selection in sparse logistic regression." In 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2013. http://dx.doi.org/10.1109/mlsp.2013.6661987.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Zhang, Zhi-yong, and Bai-lin Yang. "A relevance feedback based on Bayesian logistic regression for 3D model retrieval." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5620071.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Mladenov, Martin, Craig Boutilier, Dale Schuurmans, Ofer Meshi, Gal Elidan, and Tyler Lu. "Logistic Markov Decision Processes." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/346.

Full text
Abstract:
User modeling in advertising and recommendation has typically focused on myopic predictors of user responses. In this work, we consider the long-term decision problem associated with user interaction. We propose a concise specification of long-term interaction dynamics by combining factored dynamic Bayesian networks with logistic predictors of user responses, allowing state-of-the-art prediction models to be seamlessly extended. We show how to solve such models at scale by providing a constraint generation approach for approximate linear programming that overcomes the variable coupling and non-linearity induced by the logistic regression predictor. The efficacy of the approach is demonstrated on advertising domains with up to 2^54 states and 2^39 actions.
APA, Harvard, Vancouver, ISO, and other styles
7

Nguyen, Vu, Dinh Phung, Trung Le, and Hung Bui. "Discriminative Bayesian Nonparametric Clustering." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/355.

Full text
Abstract:
We propose a general framework for discriminative Bayesian nonparametric clustering to promote the inter-discrimination among the learned clusters in a fully Bayesian nonparametric (BNP) manner. Our method combines existing BNP clustering and discriminative models by enforcing latent cluster indices to be consistent with the predicted labels resulted from probabilistic discriminative model. This formulation results in a well-defined generative process wherein we can use either logistic regression or SVM for discrimination. Using the proposed framework, we develop two novel discriminative BNP variants: the discriminative Dirichlet process mixtures, and the discriminative-state infinite HMMs for sequential data. We develop efficient data-augmentation Gibbs samplers for posterior inference. Extensive experiments in image clustering and dynamic location clustering demonstrate that by encouraging discrimination between induced clusters, our model enhances the quality of clustering in comparison with the traditional generative BNP models.
APA, Harvard, Vancouver, ISO, and other styles
8

"Using the Bayesian Logistic Regression Model to determine the relationship of demographics and Hyperaldosteronism." In 21st International Congress on Modelling and Simulation (MODSIM2015). Modelling and Simulation Society of Australia and New Zealand, 2015. http://dx.doi.org/10.36334/modsim.2015.h1.bartolucci.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Buettner, Florian, Sarah Gulliford, Steve Webb, and Mike Partridge. "Using Bayesian Logistic Regression with High-Order Interactions to Model Radiation-Induced Toxicities Following Radiotherapy." In 2009 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2009. http://dx.doi.org/10.1109/icmla.2009.65.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Foulds, James R., Mijung Park, Kamalika Chaudhuri, and Max Welling. "Variational Bayes in Private Settings (VIPS) (Extended Abstract)." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/705.

Full text
Abstract:
Many applications of Bayesian data analysis involve sensitive information such as personal documents or medical records, motivating methods which ensure that privacy is protected. We introduce a general privacy-preserving framework for Variational Bayes (VB), a widely used optimization-based Bayesian inference method. Our framework respects differential privacy, the gold-standard privacy criterion. The iterative nature of variational Bayes presents a challenge since iterations increase the amount of noise needed to ensure privacy. We overcome this by combining: (1) an improved composition method, called the moments accountant, and (2) the privacy amplification effect of subsampling mini-batches from large-scale data in stochastic learning. We empirically demonstrate the effectiveness of our method on LDA topic models, evaluated on Wikipedia. In the full paper we extend our method to a broad class of models, including Bayesian logistic regression and sigmoid belief networks.
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Bayesian logistic regression models"

1

Churchill, Alexandrea, and Grace Kissling. Convergence in Mixed Effects Logistic Regression Models. Journal of Young Investigators, February 2019. http://dx.doi.org/10.22186/jyi.36.2.18-35.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Johnston, Katherine. Bayesian Regression of Thermodynamic Models of Redox Active Materials. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1389915.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Stefanski, L. A., R. J. Carroll, and D. Ruppert. Optimally Bounded Score Functions for Generalized Linear Models with Applications to Logistic Regression. Fort Belvoir, VA: Defense Technical Information Center, April 1985. http://dx.doi.org/10.21236/ada160348.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Haubrich, Julia, Sarah Benz, Ullrich Isermann, Beat Schäffer, Rainer Schmid, Dirk Schreckenberg, Jean Marc Wunderli, and Rainer Guski. Leq+X - Lärmexposition, Ereignishäufigkeiten und Belästigung: Re-Analyse von Daten zur Belästigung und Schlafstörung durch Fluglärm an deutschen und Schweizer Flughäfen. Universitätsbibliothek der Ruhr-Universität Bochum, 2020. http://dx.doi.org/10.46586/rub.164.139.

Full text
Abstract:
In this study, part of the data sets from 4 large Swiss and German aircraft noise impact studies are re-analysed using logistic multi-level regression models. The aim is to investigate the assumptions that the prediction of a) the percentage of persons highly annoyed by aircraft noise or b) the percentage of persons highly sleep disturbed by aircraft noise can be improved if (i) instead of the energy-equivalent continuous noise level alone, either additional or alternative, more frequency-based aircraft noise metrics and (ii) also airport-specific characteristics are used as predictors. The results support both assumptions; both regarding the percentage of persons highly annoyed and regarding the percentage of persons highly sleep disturbed.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Bayesian logistic regression models' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography