%0 Journal Article %J Survey Methodology %T Bayesian Benchmarking of the Fay-Herriot Model Using Random Deletion. %A Nandram B. %A Erciulescu A.L. %A Cruze N. %B Survey Methodology %0 Journal Article %J Survey Methodology %T A Bivariate Hierarchical Bayesian Model for Estimating Cropland Cash Rental Rates at the County Level. %A Erciulescu A.L. %A Berg E. %A Cecere W. %A Ghosh M. %B Survey Methodology %0 Journal Article %J Environmental and Ecological Statistics %D 2018 %T Benchmarking a Triplet of Official Estimates. %A Erciulescu A.L. %A Cruze N. %A Nandram B. %B Environmental and Ecological Statistics %V 25 %P 523-547 %N 4 %0 Journal Article %J Journal of Survey Statistics and Methodology %D 2018 %T Bootstrap Confidence Intervals for Small Area Proportions %A Erciulescu A.L. %E Fuller W.A. %B Journal of Survey Statistics and Methodology %N DOI 10.1093/jssam/smy014 %0 Journal Article %J BMC Medical Informatics and Decision Making %D 2014 %T A Bayesian spatio-temporal approach for real-time detection of disease outbreaks: A case study %A A. F. Karr %A J. Zou %A G. S. Datta %A S. Grannis %A J. Lynch %B BMC Medical Informatics and Decision Making %V 14 %8 12/2014 %G eng %& 108 %R 10.1186/s12911-014-0108-4 %0 Journal Article %J Statistical Analysis and Data Mining %D 2014 %T Big data, big results: Knowledge discovery in output from large-scale analytics %A A. F. Karr %A R. Ferrell %A T. H. McCormick %A P. B. Ryan %B Statistical Analysis and Data Mining %V 7 %P 404-412 %8 09/2014 %G eng %N 5 %R 10.1002/sam.11237 %0 Journal Article %J Information Fusion %D 2012 %T Bayesian CAR models for syndromic surveillance on multiple data streams: Theory and practice %A A. F. Karr %A D. L. Banks %A G. Datta %A J. Lynch %A J. Niemi %A F. Vera %K Bayes %K CAR models %K Gibbs distribution %K Markov random field %K Syndromic surveillance %X

Syndromic surveillance has, so far, considered only simple models for Bayesian inference. This paper details the methodology for a serious, scalable solution to the problem of combining symptom data from a network of US hospitals for early detection of disease outbreaks. The approach requires high-end Bayesian modeling and significant computation, but the strategy described in this paper appears to be feasible and offers attractive advantages over the methods that are currently used in this area. The method is illustrated by application to ten quarters worth of data on opioid drug abuse surveillance from 636 reporting centers, and then compared to two other syndromic surveillance methods using simulation to create known signal in the drug abuse database.

%B Information Fusion %V 13 %P 105–116 %G eng %U http://dx.doi.org/10.1016/j.inffus.2009.10.005 %0 Journal Article %J Statistical Analysis and Data Mining %D 2012 %T Bayesian methodology for the analysis of spatial temporal surveillance data %A Zou, Jian %A Alan F. Karr %A Banks, David %A Heaton, Matthew J. %A Datta, Gauri %A Lynch, James %A Vera, Francisco %K conditional autoregressive process %K Markov random field %K spatial statistics %K spatio-temporal %K Syndromic surveillance %X

Early and accurate detection of outbreaks is one of the most important objectives of syndromic surveillance systems. We propose a general Bayesian framework for syndromic surveillance systems. The methodology incorporates Gaussian Markov random field (GMRF) and spatio-temporal conditional autoregressive (CAR) modeling. By contrast, most previous approaches have been based on only spatial or time series models. The model has appealing probabilistic representations as well as attractive statistical properties. Based on extensive simulation studies, the model is capable of capturing outbreaks rapidly, while still limiting false positives. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 5: 194–204, 2012

%B Statistical Analysis and Data Mining %I Wiley Subscription Services, Inc., A Wiley Company %V 5 %P 194–204 %G eng %U http://dx.doi.org/10.1002/sam.10142 %R 10.1002/sam.10142 %0 Journal Article %J Journal of Agricultural, Biological, and Environmental Statistics %D 2011 %T A Bayesian Approach to Estimating Agricultural Yield Based on Multiple Repeated Surveys %A Jianqiang C. Wang %A S. H. Holan %A Balgobin Nandram %A Wendy Barboza %A Criselda Toto %A Edwin Anderson %K Bayesian hierarchical model %K Composite estimation %K Dynamic model %K Forecasting Model comparison %K Prediction %B Journal of Agricultural, Biological, and Environmental Statistics %V 17 %P 84-106 %8 October 29, 2011 %G eng %R 10.1007/s13253-011-0067-5 %0 Journal Article %J Journal of American Statistical Association %D 2010 %T Bayesian multiscale multiple imputation with implications to data confidentiality %A A. F. Karr %A S. H. Holan %A D. Toth %A M. A. R. Ferreira %X

Many scientific, sociological, and economic applications present data that are collected on multiple scales of resolution. One particular form of multiscale data arises when data are aggregated across different scales both longitudinally and by economic sector. Frequently, such datasets experience missing observations in a manner that they can be accurately imputed, while respecting the constraints imposed by the multiscale nature of the data, using the method we propose known as Bayesian multiscale multiple imputation. Our approach couples dynamic linear models with a novel imputation step based on singular normal distribution theory. Although our method is of independent interest, one important implication of such methodology is its potential effect on confidential databases protected by means of cell suppression. In order to demonstrate the proposed methodology and to assess the effectiveness of disclosure practices in longitudinal databases, we conduct a large-scale empirical study using the U.S. Bureau of Labor Statistics Quarterly Census of Employment and Wages (QCEW). During the course of our empirical investigation it is determined that several of the predicted cells are within 1% accuracy, thus causing potential concerns for data confidentiality.

%B Journal of American Statistical Association %V 105 %P 564-577 %G eng %0 Thesis %D 2003 %T Bayesian Stochastic Computation with application to Model Selection and Inverse Problems %A G. Molina %I Duke University %C Durham %G eng %9 masters %0 Book Section %B Foundations of Statistical Inference %D 2003 %T Bounding Entries in Multi-way Contingency Tables Given a Set of Marginal Totals %A Adrian Dobra %A Stephen E. Fienberg %E Haitovsky, Yoel %E Ritov, Yaacov %E Lerche, HansRudolf %X

We describe new results for sharp upper and lower bounds on the entries in multi-way tables of counts based on a set of released and possibly overlapping marginal tables. In particular, we present a generalized version of the shuttle algorithm proposed by Buzzigoli and Giusti that computes sharp integer bounds for an arbitrary set of fixed marginals. We also present two examples which illustrate the practical import of the bounds for assessing disclosure risk.

%B Foundations of Statistical Inference %S Contributions to Statistics %I Physica-Verlag HD %P 3-16 %@ 978-3-7908-0047-0 %G eng %U http://dx.doi.org/10.1007/978-3-642-57410-8_1 %R 10.1007/978-3-642-57410-8_1 %0 Conference Paper %B Foundations of Statistical Inference, Proceedings of the Shoresh Conference 2000 %D 2003 %T Bounding entries in multi-way contingency tables given a set of marginal totals %A A. Dobra %A S. E. Fienberg %B Foundations of Statistical Inference, Proceedings of the Shoresh Conference 2000 %I Spr %G eng %0 Conference Paper %B Proceedings of Conference on Foundation of Statistical Inference and Its Applications, Jerusalem %D 2002 %T Bounding entries in multi-way contingency tables given a set of marginal totals %A A. Dobra %A S. E. Fienberg %B Proceedings of Conference on Foundation of Statistical Inference and Its Applications, Jerusalem %I Springer-Verlag %G eng %0 Journal Article %J Statistics in Medicine %D 2000 %T Bayesian Analysis of Mortality Rates with Disease Maps %A Sun,Dongchu %A Tsuakawa, R. K. %A Kim, H. %A Z. He %X

This article summarizes our research on estimation of age-specific and age-adjusted mortality rates for chronic obstructive pulmonary disease (COPD) for white males. Our objectives are more precise and informative displays (than previously available) of geographic variation of the age-specific mortality rates for COPD, and investigation of the relationships between the geographic variation in mortality rates and the corresponding variation in selected covariates. For a given age class, our estimates are displayed in a choropleth map of mean rates. We develop a variation map that identifies the geographical areas where inferences are reliable. Here, the variation is measured by considering a set of maps produced using samples from the posterior distribution of the population mortality rates. Finally, we describe the spatial patterns in the age-specific maps and relate these to patterns in potential explanatory covariates such as smoking rate, annual rainfall, population density, elevation, and measures of air quality.

%B Statistics in Medicine %V 19 %P 2015-2035 %G eng %0 Conference Proceedings %B Proceedings of the National Academy of Sciences of the United States of America %D 2000 %T Bounds for Cell Entries in Contingency Tables Given Marginal Totals and Decomposable Graphs %A Adrian Dobra %A Stephen E. Fienberg %X

Upper and lower bounds on cell counts in cross-classifications of nonnegative counts play important roles in a number of practical problems, inclusing statistical disclosure limitation, computer tomography, mass transportation, cell suppression, and data swapping. Some features of the Frechet bounds are well known, intuitive, and regularly used by those working on disclosure limitation methods, especially those for two-dimensional tables. We previously have described a series of results relating these bounds to theory on loglinear models for cross-classified counts. This paper provides the actual theory and proofs for the special case of decomposable loglinear models and their related independence graphs. It also includes an extension linked to the structure of reducible graphs and a discussion of the relevance of other results linked to nongraphical loglinear models.

%B Proceedings of the National Academy of Sciences of the United States of America %V 97 %P 11885-11892 %G eng %0 Journal Article %J Journal of Agricultural Biological and Environmental Statistics %D 1999 %T A bivariate Bayes method for improving the estimates of mortality rates with a twofold conditional autoregressive model %A Woodard, R. %A Sun,Dongchu %A Z. He %A Sheriff, S. %X

The Missouri Turkey Hunting Survey (MTHS) is a post-season mail survey conducted by the Missouri Department of Conservation to monitor and aid in the regulation of the turkey hunting season. Questionnaires are distributed after the hunting season to a simple random sample of persons who purchased permits to hunt wild turkey during the spring season. For the 1996 turkey hunting season 95,801 persons purchased hunting permits. From these individuals a simple random sample of 6,999 hunters were selected for the survey and 5,005 of these responded. The MTHS 1 Roger Woodard (E-mail: woodard@stat.missouri.edu) is a Ph.D student and Dongchu Sun (E-mail: dsun@stat.missouri.edu) is Associate Professor of Statistics, Department of Statistics, University of Missouri, Columbia, MO 65211. Zhuoqiong He (E-mail: HEZ@mail.conservation.state.mo.us) is a biometrician and Steven L. Sheri (E-mail: SHERIS@mail.conservation.state.mo.us) is a wildlife biometrics superv

%B Journal of Agricultural Biological and Environmental Statistics %G eng %0 Journal Article %J Journal of the American Statistical Association %D 1998 %T Bayesian Inference on Network Traffic Using Link Count Data %A Claudia Tebaldi %A Michael West %X

We study Bayesian models and methods for analysing network traffic counts in problems of inference about the traffic intensity between directed pairs of origins and destinations in networks. This is a class of problems very recently discussed by Vardi in a 1996 JASA article and is of interest in both communication and transportation network studies. The current article develops the theoretical framework of variants of the origin-destination flow problem and introduces Bayesian approaches to analysis and inference. In the first, the so-called fixed routing problem, traffic or messages pass between nodes in a network, with each message originating at a specific source node, and ultimately moving through the network to a predetermined destination node. All nodes are candidate origin and destination points. The framework assumes no travel time complications, considering only the number of messages passing between pairs of nodes in a specified time interval. The route count, or route flow, problem is to infer the set of actual number of messages passed between each directed origin-destination pair in the time interval, based on the observed counts flowing between all directed pairs of adjacent nodes. Based on some development of the theoretical structure of the problem and assumptions about prior distributional forms, we develop posterior distributions for inference on actual origin-destination counts and associated flow rates. This involves iterative simulation methods, or Markov chain Monte Carlo (MCMC), that combine Metropolis-Hastings steps within an overall Gibbs sampling framework. We discuss issues of convergence and related practical matters, and illustrate the approach in a network previously studied in Vardi’s article. We explore both methodological and applied aspects much further in a concrete problem of a road network in North Carolina, studied in transportation flow assessment contexts by civil engineers. This investigation generates critical insight into limitations of statistical analysis, and particularly of non-Bayesian approaches, due to inherent structural features of the problem. A truly Bayesian approach, imposing partial stochastic constraints through informed prior distributions, offers a way of resolving these problems and is consistent with prevailing trends in updating traffic flow intensities in this field. Following this, we explore a second version of the problem that introduces elements of uncertainty about routes taken by individual messages in terms of Markov selection of outgoing links for messages at any given node. For specified route choice probabilities, we introduce the concept of a super-network-namely, a fixed routing problem in which the stochastic problem may be embedded. This leads to solution of the stochastic version of the problem using the methods developed for the original formulation of the fixed routing problem. This is also illustrated. Finally, we discuss various related issues and model extensions, including inference on stochastic route choice selection probabilities, questions of missing data and partially observed link counts, and relationships with current research on road traffic network problems in which travel times within links are nonnegligible and may be estimated from additional data.

%B Journal of the American Statistical Association %V 93 %P 557-573 %8 06/1998 %G eng %U http://www.jstor.org/stable/2670105http://www.jstor.org/stable/2670105 %0 Book Section %B Statistics in Science and Technology: Case Studies 4 %D 1998 %T Bayesian Mixture Models in Exploration of Structure-Activity Relationships in Drug Design %A Susan Paddock %A Michael West %A S. Stanley Young %A M. Clyde %B Statistics in Science and Technology: Case Studies 4 %I Springer-Verlag %G eng %0 Journal Article %J Environmental and Ecological Statistics %D 1997 %T Bayes methods for combining disease and exposure data in assessing environmental justice %A Waller, Lance A. %A Louis, Thomas A. %A Carlin, Bradley P. %K environmental equity %K hierarchical model %K Markov chain Monte Carlo %K regulation %X

Environmental justice reflects the equitable distribution of the burden of environmental hazards across various sociodemographic groups. The issue is important in environmental regulation, siting of hazardous waste repositories and prioritizing remediation of existing sources of exposure. We propose a statistical framework for assessing environmental justice. The framework includes a quantitative assessment of environmental equity based on the cumulative distribution of exposure within population subgroups linked to disease incidence through a dose-response function. This approach avoids arbitrary binary classifications of individuals solely as ’exposed’ or ’unexposed’. We present a Bayesian inferential approach, implemented using Markov chain Monte Carlo methods, that accounts for uncertainty in both exposure and response. We illustrate our method using data on leukemia deaths and exposure to toxic chemical releases in Allegheny County, Pennsylvania.

%B Environmental and Ecological Statistics %I Kluwer Academic Publishers %V 4 %P 267-281 %G eng %U http://dx.doi.org/10.1023/A%3A1018586715034 %R 10.1023/A:1018586715034 %0 Journal Article %J Journal of Environmental Engineering %D 1996 %T Bayesian Model for Fate and Transport of Polychlorinated Biphenyl in Upper Hudson River %A Steinberg, Laura J. %A Reckhow, Kenneth H. %A Wolpert, Robert L. %B Journal of Environmental Engineering %V 122 %G eng %N 5 %0 Journal Article %J Journal of Environmental Engineering %D 1996 %T Bayesian Model for Fate and Transport of Polychlorinated Biphenyl in Upper Hudson River %A Steinberg, Laura J. %A Reckhow, Kenneth H. %A Wolpert, Robert L. %K Bayesian analysis %K Hudson River %K PCB %K simulation models %K transport phenomena %X

Modelers of contaminant fate and transport in surface waters typically rely on literature values when selecting parameter values for mechanistic models. While the expert judgment with which these selections are made is valuable, the information contained in contaminant concentration measurements should not be ignored. In this full-scale Bayesian analysis of polychlorinated biphenyl (PCB) contamination in the upper Hudson River, these two sources of information are combined using Bayes’ theorem. A simulation model for the fate and transport of the PCBs in the upper Hudson River forms the basis of the likelihood function while the prior density is developed from literature values. The method provides estimates for the anaerobic biodegradation half-life, aerobic biodegradation plus volatilization half-life, contaminated sediment depth, and resuspension velocity of 4,400 d, 3.2 d, 0.32 m, and 0.02 m/yr, respectively. These are significantly different than values obtained with more traditional methods, and are shown to produce better predictions than those methods when used in a cross-validation study.

%B Journal of Environmental Engineering %V 122 %P 341-349 %8 May 1996 %G eng %R http://dx.doi.org/10.1061/(ASCE)0733-9372(1996)122:5(341)