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Search Results: 1 - 10 of 303869 matches for " Brian J Reich "
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Discussion of "Statistical Modeling of Spatial Extremes" by A. C. Davison, S. A. Padoan and M. Ribatet
Benjamin Shaby,Brian J. Reich
Statistics , 2012, DOI: 10.1214/12-STS376D
Abstract: Discussion of "Statistical Modeling of Spatial Extremes" by A. C. Davison, S. A. Padoan and M. Ribatet [arXiv:1208.3378].
A multivariate semiparametric Bayesian spatial modeling framework for hurricane surface wind fields
Brian J. Reich,Montserrat Fuentes
Statistics , 2007, DOI: 10.1214/07-AOAS108
Abstract: Storm surge, the onshore rush of sea water caused by the high winds and low pressure associated with a hurricane, can compound the effects of inland flooding caused by rainfall, leading to loss of property and loss of life for residents of coastal areas. Numerical ocean models are essential for creating storm surge forecasts for coastal areas. These models are driven primarily by the surface wind forcings. Currently, the gridded wind fields used by ocean models are specified by deterministic formulas that are based on the central pressure and location of the storm center. While these equations incorporate important physical knowledge about the structure of hurricane surface wind fields, they cannot always capture the asymmetric and dynamic nature of a hurricane. A new Bayesian multivariate spatial statistical modeling framework is introduced combining data with physical knowledge about the wind fields to improve the estimation of the wind vectors. Many spatial models assume the data follow a Gaussian distribution. However, this may be overly-restrictive for wind fields data which often display erratic behavior, such as sudden changes in time or space. In this paper we develop a semiparametric multivariate spatial model for these data. Our model builds on the stick-breaking prior, which is frequently used in Bayesian modeling to capture uncertainty in the parametric form of an outcome. The stick-breaking prior is extended to the spatial setting by assigning each location a different, unknown distribution, and smoothing the distributions in space with a series of kernel functions. This semiparametric spatial model is shown to improve prediction compared to usual Bayesian Kriging methods for the wind field of Hurricane Ivan.
A spatial capture-recapture model for territorial species
Brian J. Reich,Beth Gardner
Statistics , 2014,
Abstract: Advances in field techniques have lead to an increase in spatially-referenced capture-recapture data to estimate a species' population size as well as other demographic parameters and patterns of space usage. Statistical models for these data have assumed that the number of individuals in the population and their spatial locations follow a homogeneous Poisson point process model, which implies that the individuals are uniformly and independently distributed over the spatial domain of interest. In many applications there is reason to question independence, for example when species display territorial behavior. In this paper, we propose a new statistical model which allows for dependence between locations to account for avoidance or territorial behavior. We show via a simulation study that accounting for this can improve population size estimates. The method is illustrated using a case study of small mammal trapping data to estimate avoidance and population density of adult female field voles (Microtus agrestis) in northern England.
A latent factor model for spatial data with informative missingness
Brian J. Reich,Dipankar Bandyopadhyay
Statistics , 2010, DOI: 10.1214/09-AOAS278
Abstract: A large amount of data is typically collected during a periodontal exam. Analyzing these data poses several challenges. Several types of measurements are taken at many locations throughout the mouth. These spatially-referenced data are a mix of binary and continuous responses, making joint modeling difficult. Also, most patients have missing teeth. Periodontal disease is a leading cause of tooth loss, so it is likely that the number and location of missing teeth informs about the patient's periodontal health. In this paper we develop a multivariate spatial framework for these data which jointly models the binary and continuous responses as a function of a single latent spatial process representing general periodontal health. We also use the latent spatial process to model the location of missing teeth. We show using simulated and real data that exploiting spatial associations and jointly modeling the responses and locations of missing teeth mitigates the problems presented by these data.
Discussion of "Estimating the historical and future probabilities of large terrorist event" by Aaron Clauset and Ryan Woodard
Brian J. Reich,Michael D. Porter
Statistics , 2014, DOI: 10.1214/13-AOAS614B
Abstract: Discussion of "Estimating the historical and future probabilities of large terrorist events" by Aaron Clauset and Ryan Woodard [arXiv:1209.0089].
A hierarchical max-stable spatial model for extreme precipitation
Brian J. Reich,Benjamin A. Shaby
Statistics , 2013, DOI: 10.1214/12-AOAS591
Abstract: Extreme environmental phenomena such as major precipitation events manifestly exhibit spatial dependence. Max-stable processes are a class of asymptotically-justified models that are capable of representing spatial dependence among extreme values. While these models satisfy modeling requirements, they are limited in their utility because their corresponding joint likelihoods are unknown for more than a trivial number of spatial locations, preventing, in particular, Bayesian analyses. In this paper, we propose a new random effects model to account for spatial dependence. We show that our specification of the random effect distribution leads to a max-stable process that has the popular Gaussian extreme value process (GEVP) as a limiting case. The proposed model is used to analyze the yearly maximum precipitation from a regional climate model.
Mixture Likelihood Ratio Scan Statistic for Disease Outbreak Detection
Michael D. Porter,Jarad B. Niemi,Brian J. Reich
Quantitative Biology , 2012,
Abstract: Early detection of disease outbreaks is of paramount importance to implementing intervention strategies to mitigate the severity and duration of the outbreak. We build methodology that utilizes the characteristic profile of disease outbreaks to reduce the time to detection and false positive rate. We model daily counts through a Poisson distribution with additive background plus outbreak components. The outbreak component has a parametric form with unknown underlying parameters. A mixture likelihood ratio scan statistic is developed to maximize parameters over a window in time. This provides an alert statistic with early time to detection and low false positive rate. The methodology is demonstrated on three simulated data sets meant to represent E. coli, Cryptosporidium, and Influenza outbreaks.
A nonparametric Bayesian test of dependence
Yimin Kao,Brian J Reich,Howard D Bondell
Statistics , 2015,
Abstract: In this article, we propose a new method for the fundamental task of testing for dependence between two groups of variables. The response densities under the null hypothesis of independence and the alternative hypothesis of dependence are specified by nonparametric Bayesian models. Under the null hypothesis, the joint distribution is modeled by the product of two independent Dirichlet Process Mixture (DPM) priors; under the alternative, the full joint density is modeled by a multivariate DPM prior. The test is then based on the posterior probability of favoring the alternative hypothesis. The proposed test not only has good performance for testing linear dependence among other popular nonparametric tests, but is also preferred to other methods in testing many of the nonlinear dependencies we explored. In the analysis of gene expression data, we compare different methods for testing pairwise dependence between genes. The results show that the proposed test identifies some dependence structures that are not detected by other tests.
A class of covariate-dependent spatiotemporal covariance functions for the analysis of daily ozone concentration
Brian J. Reich,Jo Eidsvik,Michele Guindani,Amy J. Nail,Alexandra M. Schmidt
Statistics , 2012, DOI: 10.1214/11-AOAS482
Abstract: In geostatistics, it is common to model spatially distributed phenomena through an underlying stationary and isotropic spatial process. However, these assumptions are often untenable in practice because of the influence of local effects in the correlation structure. Therefore, it has been of prolonged interest in the literature to provide flexible and effective ways to model nonstationarity in the spatial effects. Arguably, due to the local nature of the problem, we might envision that the correlation structure would be highly dependent on local characteristics of the domain of study, namely, the latitude, longitude and altitude of the observation sites, as well as other locally defined covariate information. In this work, we provide a flexible and computationally feasible way for allowing the correlation structure of the underlying processes to depend on local covariate information. We discuss the properties of the induced covariance functions and methods to assess its dependence on local covariate information. The proposed method is used to analyze daily ozone in the southeast United States.
Quantile regression for mixed models with an application to examine blood pressure trends in China
Luke B. Smith,Montserrat Fuentes,Penny Gordon-Larsen,Brian J. Reich
Statistics , 2015, DOI: 10.1214/15-AOAS841
Abstract: Cardiometabolic diseases have substantially increased in China in the past 20 years and blood pressure is a primary modifiable risk factor. Using data from the China Health and Nutrition Survey, we examine blood pressure trends in China from 1991 to 2009, with a concentration on age cohorts and urbanicity. Very large values of blood pressure are of interest, so we model the conditional quantile functions of systolic and diastolic blood pressure. This allows the covariate effects in the middle of the distribution to vary from those in the upper tail, the focal point of our analysis. We join the distributions of systolic and diastolic blood pressure using a copula, which permits the relationships between the covariates and the two responses to share information and enables probabilistic statements about systolic and diastolic blood pressure jointly. Our copula maintains the marginal distributions of the group quantile effects while accounting for within-subject dependence, enabling inference at the population and subject levels. Our population-level regression effects change across quantile level, year and blood pressure type, providing a rich environment for inference. To our knowledge, this is the first quantile function model to explicitly model within-subject autocorrelation and is the first quantile function approach that simultaneously models multivariate conditional response. We find that the association between high blood pressure and living in an urban area has evolved from positive to negative, with the strongest changes occurring in the upper tail. The increase in urbanization over the last twenty years coupled with the transition from the positive association between urbanization and blood pressure in earlier years to a more uniform association with urbanization suggests increasing blood pressure over time throughout China, even in less urbanized areas. Our methods are available in the R package BSquare.
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