2015

最新文章

• Bayesian Generalized Additive Models for Location, Scale, and Shape for Zero-Inflated and Overdispersed Count Data AI翻译
  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 ...
• Risk Classification With an Adaptive Naive Bayes Kernel Machine Model AI翻译
  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...
• Randomized Controlled Field Trials of Predictive Policing AI翻译
  作者：Mohler, G. O.; Short, M. B.; Malinowski, Sean; Johnson, Mark; Tita, G. E.; Bertozzi, Andrea L.; Brantingham, P. J.
  作者单位：Santa Clara University; University System of Georgia; Georgia Institute of Technology; University of California System; University of California Irvine; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles
  摘要：The concentration of police resources in stable crime hotspots has proven effective in reducing crime, but the extent to which police can disrupt dynamically changing crime hotspots is unknown. Police must be able to anticipate the future location of dynamic hotspots to disrupt them. Here we report results of two randomized controlled trials of near real-time epidemic-type aftershock sequence (ETAS) crime forecasting, one trial within three divisions of the Los Angeles Police Department and th...
• Estimation of Multiple-Regime Threshold Autoregressive Models With Structural Breaks AI翻译
  作者：Yau, Chun Yip; Tang, Chong Man; Lee, Thomas C. M.
  作者单位：Chinese University of Hong Kong; University of California System; University of California Davis
  摘要：The threshold autoregressive (TAR) model is a class of nonlinear time series models that have been widely used in many areas. Due to its nonlinear nature, one major difficulty in fitting a TAR model is the estimation of the thresholds. As a first contribution, this article develops an automatic procedure to estimate the number and values of the thresholds, as well as the corresponding AR order and parameter values in each regime. These parameter estimates are defined as the minimizers of an ob...
• Matching a Distribution by Matching Quantiles Estimation AI翻译
  作者：Sgouropoulos, Nikolaos; Yao, Qiwei; Yastremiz, Claudia
  作者单位：University of London; London School Economics & Political Science; Peking University; Bank of England
  摘要：Motivated by the problem of selecting representative portfolios for backtesting counterparty credit risks, we propose a matching quantiles estimation (MQE) method for matching a target distribution by that of a linear combination of a set of random variables. An iterative procedure based on the ordinary least-squares estimation (OLS) is proposed to compute MQE. MQE can be easily modified by adding a LASSO penalty term if a sparse representation is desired, or by restricting the matching within...
• Regularization Methods for High-Dimensional Instrumental Variables Regression With an Application to Genetical Genomics AI翻译
  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...
• Some Counterclaims Undermine Themselves in Observational Studies AI翻译
  作者：Rosenbaum, Paul R.
  作者单位：University of Pennsylvania
  摘要：Claims based on observational studies that a treatment has certain effects are often met with counterclaims asserting that the treatment is without effect, that associations are produced by biased treatment assignment. Some counterclaims undermine themselves in the following specific sense: presuming the counterclaim to be true may strengthen the support that the original data provide for the original claim, so that the counterclaim fails in its role as a critique of the original claim. In mat...
• Testing and Modeling Dependencies Between a Network and Nodal Attributes AI翻译
  作者：Fosdick, Bailey K.; Hoff, Peter D.
  作者单位：Colorado State University System; Colorado State University Fort Collins; University of Washington; University of Washington Seattle
  摘要：Network analysis is often focused on characterizing the dependencies between network relations and node-level attributes. Potential relationships are typically explored by modeling the network as a function of the nodal attributes or by modeling the attributes as a function of the network. These methods require specification of the exact nature of the association between the network and attributes, reduce the network data to a small number of summary statistics, and are unable to provide predi...
• Unifying Amplitude and Phase Analysis: A Compositional Data Approach to Functional Multivariate Mixed-Effects Modeling of Mandarin Chinese AI翻译
  作者：Hadjipantelis, P. Z.; Aston, J. A. D.; Mueller, H. G.; Evans, J. P.
  作者单位：University of Warwick; University of Warwick; University of Cambridge; University of California System; University of California Davis; Academia Sinica - Taiwan
  摘要：Mandarin Chinese is characterized by being a tonal language; the pitch (or F-0) of its utterances carries considerable linguistic information. However, speech samples from different individuals are subject to changes in amplitude and phase, which must be accounted for in any analysis that attempts to provide a linguistically meaningful description of the language. A joint model for amplitude, phase, and duration is presented, which combines elements from functional data analysis, compositional...
• A Unifying Model for Capture-Recapture and Distance Sampling Surveys of Wildlife Populations AI翻译
  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...