-
作者：Finley, Andrew O.; Banerjee, Sudipto; McRoberts, Ronald E.
作者单位：Michigan State University; Michigan State University; University of Minnesota System; University of Minnesota Twin Cities; United States Department of Agriculture (USDA); United States Forest Service
摘要：Spatially explicit data layers of tree species assemblages, referred to as forest types or forest type groups, are a key component in large-scale assessments of forest sustainability, biodiversity, timber biomass, carbon sinks and forest health monitoring. This paper explores the utility of coupling georeferenced national forest inventory (NFI) data with readily available and spatially complete environmental predictor variables through spatially-varying multinomial logistic regression models t...
-
作者：Qiu, Peihua; Yang, Rong; Potegal, Michael
作者单位：University of Minnesota System; University of Minnesota Twin Cities; Bristol-Myers Squibb; University of Minnesota System; University of Minnesota Twin Cities
摘要：Although anger is an important emotion that underlies much overt aggression at great social cost, little is known about how to quantify anger or to specify the relationship between anger and the overt behaviors that express it. This paper proposes a novel statistical model which provides both a metric for the intensity of anger and an approach to determining the quantitative relationship between anger intensity and the specific behaviors that it controls. From observed angry behaviors, we reco...
-
作者：Rossell, David
作者单位：Barcelona Institute of Science & Technology; Institute for Research in Biomedicine - IRB Barcelona
摘要：Hierarchical models are a powerful tool for high-throughput data with a small to moderate number of replicates, as they allow sharing information across units of information, for example, genes. We propose two such models and show its increased sensitivity in microarray differential expression applications. We build on the gamma-gamma hierarchical model introduced by Kendziorski et al. [Statist. Med. 22 (2003) 3899-3914] and Newton et al. [Biostatistics 5 (2004) 155-176], by addressing importa...
-
作者：Yuan, Ming
作者单位：University System of Georgia; Georgia Institute of Technology
摘要：We consider nonparametric estimation of the state price density encapsulated in option prices. Unlike usual density estimation problems, we only observe option prices and their corresponding strike prices rather than samples from the state price density. We propose to model the state price density directly with a nonparametric mixture and estimate it using least squares. We show that although the minimization is taken over an infinitely dimensional function space, the minimizer always admits a...
-
作者：Kim, Sungduk; Xi, Yingmei; Chen, Ming-Hui
作者单位：National Institutes of Health (NIH) - USA; NIH Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD); Biogen; University of Connecticut
摘要：To address an important risk classification issue that arises in clinical practice, we propose a new mixture model via latent cure rate markers for survival data with a cure fraction. In the proposed model, the latent cure rate markers are modeled via a multinomial logistic regression and patients who share the same cure rate are classified into the same risk group. Compared to available cure rate models, the proposed model fits better to data from a prostate cancer clinical trial. In addition...
-
作者：Shabalin, Andrey A.; Weigman, Victor J.; Perou, Charles M.; Nobel, Andrew B.
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
摘要：The search for sample-variable associations is an important problem in the exploratory analysis of high dimensional data. Biclustering methods search for sample-variable associations in the form of distinguished submatrices of the data matrix. (The rows and columns of a submatrix need not be contiguous.) In this paper we propose and evaluate a statistically motivated biclustering procedure (LAS) that finds large average submatrices within a given real-valued data matrix. The procedure operates...
