-
作者：Wang, Lan; Qu, Annie
作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Illinois System; University of Illinois Urbana-Champaign
摘要：Model selection for marginal regression analysis of longitudinal data is challenging owing to the presence of correlation and the difficulty of specifying the full likelihood, particularly for correlated categorical data. The paper introduces a novel Bayesian information criterion type model selection procedure based on the quadratic inference function, which does not require the full likelihood or quasi-likelihood. With probability approaching 1, the criterion selects the most parsimonious co...
-
作者：Wang, Yuedong; Ma, Yanyuan; Carroll, Raymond J.
作者单位：University of California System; University of California Santa Barbara; Texas A&M University System; Texas A&M University College Station; University of Neuchatel
摘要：Microarrays are one of the most widely used high throughput technologies. One of the main problems in the area is that conventional estimates of the variances that are required in the t-statistic and other statistics are unreliable owing to the small number of replications. Various methods have been proposed in the literature to overcome this lack of degrees of freedom problem. In this context, it is commonly observed that the variance increases proportionally with the intensity level, which h...
-
作者：Berkes, Istvan; Gabrys, Robertas; Horvath, Lajos; Kokoszka, Piotr
作者单位：Utah System of Higher Education; Utah State University; Utah System of Higher Education; University of Utah; Graz University of Technology
摘要：Principal component analysis has become a fundamental tool of functional data analysis. It represents the functional data as X-i(t)=mu(t)+Sigma(1 < l
-
作者：Sun, Wenguang; Cai, T. Tony
作者单位：University of Pennsylvania
摘要：The paper considers the problem of multiple testing under dependence in a compound decision theoretic framework. The observed data are assumed to be generated from an underlying two-state hidden Markov model. We propose oracle and asymptotically optimal data-driven procedures that aim to minimize the false non-discovery rate FNR subject to a constraint on the false discovery rate FDR. It is shown that the performance of a multiple-testing procedure can be substantially improved by adaptively e...
-
作者：Petrone, Sonia; Guindani, Michele; Gelfand, Alan E.
作者单位：Bocconi University; University of New Mexico; Duke University
摘要：In functional data analysis, curves or surfaces are observed, up to measurement error, at a finite set of locations, for, say, a sample of n individuals. Often, the curves are homogeneous, except perhaps for individual-specific regions that provide heterogeneous behaviour (e.g. 'damaged' areas of irregular shape on an otherwise smooth surface). Motivated by applications with functional data of this nature, we propose a Bayesian mixture model, with the aim of dimension reduction, by representin...
-
作者：Lee, Myoung-jae
作者单位：Korea University
摘要：For a binary treatment nu=0, 1 and the corresponding 'potential response'Y-0 for the control group (nu=0) and Y-1 for the treatment group (nu=1), one definition of no treatment effect is that Y-0 and Y-1 follow the same distribution given a covariate vector X. Koul and Schick have provided a non-parametric test for no distributional effect when the realized response (1-nu)Y-0+nu Y-1 is fully observed and the distribution of X is the same across the two groups. This test is thus not applicable ...
-
作者：Bondell, Howard D.; Li, Lexin
作者单位：North Carolina State University
摘要：The family of inverse regression estimators that was recently proposed by Cook and Ni has proven effective in dimension reduction by transforming the high dimensional predictor vector to its low dimensional projections. We propose a general shrinkage estimation strategy for the entire inverse regression estimation family that is capable of simultaneous dimension reduction and variable selection. We demonstrate that the new estimators achieve consistency in variable selection without requiring ...
-
作者：Hu, Jianhua; Johnson, Valen E.
作者单位：University of Texas System; UTMD Anderson Cancer Center
摘要：Existing Bayesian model selection procedures require the specification of prior distributions on the parameters appearing in every model in the selection set. In practice, this requirement limits the application of Bayesian model selection methodology. To overcome this limitation, we propose a new approach towards Bayesian model selection that uses classical test statistics to compute Bayes factors between possible models. In several test cases, our approach produces results that are similar t...
-
作者：Lu, Zudi; Steinskog, Dag Johan; Tjostheim, Dag; Yao, Qiwei
作者单位：University of Bergen; University of London; London School Economics & Political Science; Curtin University; University of Adelaide; Nansen Environmental & Remote Sensing Center (NERSC); Bjerknes Centre for Climate Research; Peking University
摘要：We propose an adaptive varying-coefficient spatiotemporal model for data that are observed irregularly over space and regularly in time. The model is capable of catching possible non-linearity (both in space and in time) and non-stationarity (in space) by allowing the auto-regressive coefficients to vary with both spatial location and an unknown index variable. We suggest a two-step procedure to estimate both the coefficient functions and the index variable, which is readily implemented and ca...
-
作者：Bissantz, Nicolai; Claeskens, Gerda; Holzmann, Hajo; Munk, Axel
作者单位：Ruhr University Bochum; KU Leuven; Helmholtz Association; Karlsruhe Institute of Technology; University of Gottingen
摘要：We propose two test statistics for use in inverse regression problems Y=K theta+epsilon, where K is a given linear operator which cannot be continuously inverted. Thus, only noisy, indirect observations Y for the function theta are available. Both test statistics have a counterpart in classical hypothesis testing, where they are called the order selection test and the data-driven Neyman smooth test. We also introduce two model selection criteria which extend the classical Akaike information cr...
