The Experts below are selected from a list of 59880 Experts worldwide ranked by ideXlab platform
David B Dunson - One of the best experts on this subject based on the ideXlab platform.
-
Bayesian Modeling of temporal dependence in large sparse contingency tables
Journal of the American Statistical Association, 2013Co-Authors: Tsuyoshi Kunihama, David B DunsonAbstract:It is of interest in many applications to study trends over time in relationships among categorical variables, such as age group, ethnicity, religious affiliation, political party, and preference for particular policies. At each time point, a sample of individuals provides responses to a set of questions, with different individuals sampled at each time. In such settings, there tend to be an abundance of missing data and the variables being measured may change over time. At each time point, we obtained a large sparse contingency table, with the number of cells often much larger than the number of individuals being surveyed. To borrow information across time in Modeling large sparse contingency tables, we propose a Bayesian autoregressive tensor factorization approach. The proposed model relies on a probabilistic Parafac factorization of the joint pmf characterizing the categorical data distribution at each time point, with autocorrelation included across times. We develop efficient computational methods th...
-
Bayesian Modeling of temporal dependence in large sparse contingency tables
arXiv: Methodology, 2012Co-Authors: Tsuyoshi Kunihama, David B DunsonAbstract:In many applications, it is of interest to study trends over time in relationships among categorical variables, such as age group, ethnicity, religious affiliation, political party and preference for particular policies. At each time point, a sample of individuals provide responses to a set of questions, with different individuals sampled at each time. In such settings, there tends to be abundant missing data and the variables being measured may change over time. At each time point, one obtains a large sparse contingency table, with the number of cells often much larger than the number of individuals being surveyed. To borrow information across time in Modeling large sparse contingency tables, we propose a Bayesian autoregressive tensor factorization approach. The proposed model relies on a probabilistic Parafac factorization of the joint pmf characterizing the categorical data distribution at each time point, with autocorrelation included across times. Efficient computational methods are developed relying on MCMC. The methods are evaluated through simulation examples and applied to social survey data.
-
Bayesian Modeling of the level and duration of fertility in the menstrual cycle
Biometrics, 2001Co-Authors: David B DunsonAbstract:Time to pregnancy studies that identify ovulation days and collect daily intercourse data can be used to estimate the day-specific probabilities of conception given intercourse on a single day relative to ovulation. In this article, a Bayesian semiparametric model is described for flexibly characterizing covariate effects and heterogeneity among couples in daily fecundability. The proposed model is characterized by the timing of the most fertile day of the cycle relative to ovulation, by the probability of conception due to intercourse on the most fertile day, and by the ratios of the daily conception probabilities for other days of the cycle relative to this peak probability. The ratios are assumed to be increasing in time to the peak and decreasing thereafter. Generalized linear mixed models are used to incorporate covariate and couple-specific effects on the peak probability and on the day-specific ratios. A Markov chain Monte Carlo algorithm is described for posterior estimation, and the methods are illustrated through application to caffeine data from a North Carolina pregnancy study.
Robert Tibshirani - One of the best experts on this subject based on the ideXlab platform.
-
empirical bayes methods and false discovery rates for microarrays
Genetic Epidemiology, 2002Co-Authors: Bradley Efron, Robert TibshiraniAbstract:In a classic two-sample problem, one might use Wilcoxon's statistic to test for a difference between treatment and control subjects. The analogous microarray experiment yields thousands of Wilcoxon statistics, one for each gene on the array, and confronts the statistician with a difficult simultaneous inference situation. We will discuss two inferential approaches to this problem: an empirical Bayes method that requires very little a priori Bayesian Modeling, and the frequentist method of "false discovery rates" proposed by Benjamini and Hochberg in 1995. It turns out that the two methods are closely related and can be used together to produce sensible simultaneous inferences.
-
empirical bayes methods and false discovery rates for microarrays
Genetic Epidemiology, 2002Co-Authors: Bradley Efron, Robert TibshiraniAbstract:In a classic two-sample problem, one might use Wilcoxon's statistic to test for a difference between treatment and control subjects. The analogous microarray experiment yields thousands of Wilcoxon statistics, one for each gene on the array, and confronts the statistician with a difficult simultaneous inference situation. We will discuss two inferential approaches to this problem: an empirical Bayes method that requires very little a priori Bayesian Modeling, and the frequentist method of “false discovery rates” proposed by Benjamini and Hochberg in 1995. It turns out that the two methods are closely related and can be used together to produce sensible simultaneous inferences. Genet. Epidemiol. 23:70–86, 2002. © 2002 Wiley-Liss, Inc.
Michael West - One of the best experts on this subject based on the ideXlab platform.
-
scalable Bayesian Modeling monitoring and analysis of dynamic network flow data
Journal of the American Statistical Association, 2018Co-Authors: Xi Chen, Kaoru Irie, David Banks, Robert Haslinger, Jewell Thomas, Michael WestAbstract:Traffic flow count data in networks arise in many applications, such as automobile or aviation transportation, certain directed social network contexts, and Internet studies. Using an example of In...
-
scalable Bayesian Modeling monitoring and analysis of dynamic network flow data
arXiv: Methodology, 2016Co-Authors: Xi Chen, Kaoru Irie, David Banks, Robert Haslinger, Jewell Thomas, Michael WestAbstract:Traffic flow count data in networks arise in many applications, such as automobile or aviation transportation, certain directed social network contexts, and Internet studies. Using an example of Internet browser traffic flow through site-segments of an international news website, we present Bayesian analyses of two linked classes of models which, in tandem, allow fast, scalable and interpretable Bayesian inference. We first develop flexible state-space models for streaming count data, able to adaptively characterize and quantify network dynamics efficiently in real-time. We then use these models as emulators of more structured, time-varying gravity models that allow formal dissection of network dynamics. This yields interpretable inferences on traffic flow characteristics, and on dynamics in interactions among network nodes. Bayesian monitoring theory defines a strategy for sequential model assessment and adaptation in cases when network flow data deviates from model-based predictions. Exploratory and sequential monitoring analyses of evolving traffic on a network of web site-segments in e-commerce demonstrate the utility of this coupled Bayesian emulation approach to analysis of streaming network count data.
Mark F J Steel - One of the best experts on this subject based on the ideXlab platform.
-
on Bayesian Modeling of fat tails and skewness
Journal of the American Statistical Association, 1998Co-Authors: Carmen Fernandez, Mark F J SteelAbstract:We consider a Bayesian analysis of linear regression models that can account for skewed error distributions with fat tails.The latter two features are often observed characteristics of empirical data sets, and we will formally incorporate them in the inferential process.A general procedure for introducing skewness into symmetric distributions is first proposed.Even though this allows for a great deal of flexibility in distributional shape, tail behaviour is not affected.In addition, the impact on the existence of posterior moments in a regression model with unknown scale under commonly used improper priors is quite limited.Applying this skewness procedure to a Student-$t$ distribution, we generate a ``skewed Student'' distribution, which displays both flexible tails and possible skewness, each entirely controlled by a separate scalar parameter. The linear regression model with a skewed Student error term is the main focus of the paper: we first characterize existence of the posterior distribution and its moments, using standard improper priors and allowing for inference on skewness and tail parameters.For posterior inference with this model, a numerical procedure is suggested, using Gibbs sampling with data augmentation. The latter proves very easy to implement and renders the analysis of quite challenging problems a practical possibility.Two examples illustrate the use of this model in empirical data analysis.
-
on Bayesian Modeling of fat tails and skewness
Journal of the American Statistical Association, 1998Co-Authors: Carmen Fernandez, Mark F J SteelAbstract:Abstract We consider a Bayesian analysis of linear regression models that can account for skewed error distributions with fat tails. The latter two features are often observed characteristics of empirical datasets, and we formally incorporate them in the inferential process. A general procedure for introducing skewness into symmetric distributions is first proposed. Even though this allows for a great deal of flexibility in distributional shape, tail behavior is not affected. Applying this skewness procedure to a Student t distribution, we generate a “skewed Student” distribution, which displays both flexible tails and possible skewness, each entirely controlled by a separate scalar parameter. The linear regression model with a skewed Student error term is the main focus of the article. We first characterize existence of the posterior distribution and its moments, using standard improper priors and allowing for inference on skewness and tail parameters. For posterior inference with this model, we suggest ...
Peter V E Mcclintock - One of the best experts on this subject based on the ideXlab platform.
-
missing data in longitudinal studies strategies for Bayesian Modeling and sensitivity analysis by m j daniels and j w hogan scope research monograph level professional statisticians and ph d students
Contemporary Physics, 2010Co-Authors: Peter V E McclintockAbstract:Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis, by M.J. Daniels and J.W. Hogan, Boca Raton, Chapman and Hall/CRC, 2008, xx + 303 pp., US$79.95 (hard...
