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作者：Klein, Nadja; Kneib, Thomas; Lang, Stefan
作者单位：University of Gottingen; University of Innsbruck
摘要：Frequent problems in applied research preventing the application of the classical Poisson log-linear model for analyzing count data include overdispersion, an excess of zeros compared to the Poisson distribution, correlated responses, as well as complex predictor structures comprising nonlinear effects of continuous covariates, interactions or spatial effects. We propose a general class of Bayesian generalized additive models for zero-inflated and overdispersed count data within the framework ...
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作者：Minnier, Jessica; Yuan, Ming; Liu, Jun S.; Cai, Tianxi
作者单位：Oregon Health & Science University; University of Wisconsin System; University of Wisconsin Madison; Harvard University; Harvard University; Harvard T.H. Chan School of Public Health
摘要：Genetic studies of complex traits have uncovered only a small number of risk markers explaining a small fraction of heritability and adding little improvement to disease risk prediction. Standard single marker methods may lack power in selecting informative markers or estimating effects. Most existing methods also typically do not account for nonlinearity. Identifying markers with weak signals and estimating their joint effects among many noninformative markers remains challenging. One potenti...
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作者：Lin, Wei; Feng, Rui; Li, Hongzhe
作者单位：Peking University; Peking University; University of Pennsylvania
摘要：In genetical genomics studies, it is important to jointly analyze gene expression data and genetic variants in exploring their associations with complex traits, where the dimensionality of gene expressions and genetic variants can both be much larger than the sample size. Motivated by such modern applications, we consider the problem of variable selection and estimation in high-dimensional sparse instrumental variables models. To overcome the difficulty of high dimensionality and unknown optim...
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作者：Borchers, D. L.; Stevenson, B. C.; Kidney, D.; Thomas, L.; Marques, T. A.
作者单位：University of St Andrews
摘要：A fundamental problem in wildlife ecology and management is estimation of population size or density. The two dominant methods in this area are capture-recapture (CR) and distance sampling (DS), each with its own largely separate literature. We develop a class of models that synthesizes them. It accommodates a spectrum of models ranging from nonspatial CR models (with no information on animal locations) through to DS and mark-recapture distance sampling (MRDS) models, in which animal locations...
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作者：Huang, Weibing; Lehalle, Charles-Albert; Rosenbaum, Mathieu
作者单位：Sorbonne Universite
摘要：Through the analysis of a dataset of ultra high frequency order book updates, we introduce a model which accommodates the empirical properties of the full order book together with the stylized facts of lower frequency financial data. To do so, we split the time interval of interest into periods in which a well chosen reference price, typically the midprice, remains constant. Within these periods, we view the limit order book as a Markov queuing system. Indeed, we assume that the intensities of...
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作者：Peterson, Christine; Stingo, Francesco C.; Vannucci, Marina
作者单位：Stanford University; Rice University; University of Texas System; UTMD Anderson Cancer Center
摘要：In this article, we propose a Bayesian approach to inference on multiple Gaussian graphical models. Specifically, we address the problem of inferring multiple undirected networks in situations where some of the networks may be unrelated, while others share common features. We link the estimation of the graph structures via a Markov random field (MRF) prior, which encourages common edges. We learn which sample groups have a shared graph structure by placing a spike-and-slab prior on the paramet...
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作者：Zhou, Zhengyi; Matteson, David S.; Woodard, Dawn B.; Henderson, Shane G.; Micheas, Athanasios C.
作者单位：Cornell University; Cornell University; Cornell University; University of Missouri System; University of Missouri Columbia
摘要：Ambulance demand estimation at fine time and location scales is critical for fleet management and dynamic deployment. We are motivated by the problem of estimating the spatial distribution of ambulance demand in Toronto, Canada, as it changes over discrete 2 hr intervals. This large-scale dataset is sparse at the desired temporal resolutions and exhibits location-specific serial dependence, daily, and weekly seasonality. We address these challenges by introducing a novel characterization of ti...
