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作者：Li, Youjuan; Liu, Yufeng; Zhu, Ji
作者单位：University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill
摘要：In this article we consider quantile regression in reproducing kernel Hilbert spaces, which we call kernel quantile regression (KQR). We make three contributions: (1) we propose an efficient algorithm that computes the entire solution path of the KQR, with essentially the same computational cost as fitting one KQR model; (2) we derive a simple formula for the effective dimension of the KQR model, which allows convenient selection of the regularization parameter; and (3) we develop an asymptoti...
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作者：Liang, Faming; Liu, Chuanhai; Carroll, Raymond J.
作者单位：Texas A&M University System; Texas A&M University College Station; Purdue University System; Purdue University
摘要：The Wang-Landau (WL) algorithm is an adaptive Markov chain Monte Carlo algorithm used to calculate the spectral density for a physical system. A remarkable feature of the WL algorithm is that it is not trapped by local energy minima, which is very important for systems with rugged energy landscapes. This feature has led to many successful applications of the algorithm in statistical physics and biophysics; however, there does not exist rigorous theory to support its convergence, and the estima...
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作者：Kosorok, Michael R.; Fine, Jason P.
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 Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
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作者：Guan, Yongtao; Loh, Ji Meng
作者单位：Yale University; Columbia University
摘要：When modeling inhomogeneous spatial point patterns, it is of interest to fit a parametric model for the first-order intensity function (FOIF) of the process in terms of some measured covariates. Estimates for the regression coefficients, say, can be obtained by maximizing a Poisson maximum likelihood criterion. Little work has been done on the asymptotic distribution of except in some special cases. In this article we show that is asymptotically normal for a general class of mixing processes. ...
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作者：Mandel, Micha; Gauthier, Susan A.; Guttmann, Charles R. G.; Weiner, Howard L.; Betensky, Rebecca A.
作者单位：Hebrew University of Jerusalem; Harvard University; Harvard Medical School; Harvard University Medical Affiliates; Brigham & Women's Hospital; Harvard University; Harvard University Medical Affiliates; Brigham & Women's Hospital; Harvard Medical School; Harvard University; Harvard University Medical Affiliates; Brigham & Women's Hospital; Harvard Medical School; Harvard University; Harvard Medical School
摘要：The expanded disability status scale (EDSS) is an ordinal score that measures progression in multiple sclerosis (MS). Progression is defined as reaching EDSS of a certain level (absolute progression) or increasing EDSS by one point (relative progression). Survival methods for time to progression are not adequate for such data because they do not exploit the EDSS level at the end of follow-up. Instead, we suggest a Markov transitional model applicable for repeated categorical or ordinal data. T...
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作者：Zhu, Hongtu; Zhang, Heping; Ibrahim, Joseph G.; Peterson, Bradley S.
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Yale University; University of North Carolina; University of North Carolina Chapel Hill; Columbia University; New York State Psychiatry Institute
摘要：Diffusion tensor imaging has been widely used to reconstruct the structure and orientation of fibers in biological tissues, particularly in the white matter of the brain, because it can track the effective diffusion of water along those fibers. The raw diffusion-weighted images from which diffusion tensors are estimated, however, inherently contain noise. Noise in the images produces uncertainty in the estimation of the tensors (which are 3 x 3 positive-definite matrices) and of their derived ...
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作者：Kadane, Joseph B.
作者单位：Carnegie Mellon University
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作者：Wang, Xiaohui; Ray, Shubhankar; Mallick, Bani K.
作者单位：University of Texas System; University of Texas Rio Grande Valley; Merck & Company; Texas A&M University System; Texas A&M University College Station
摘要：We propose classification models for binary and multicategory data where the predictor is a random function. We use Bayesian modeling with wavelet basis functions that have nice approximation properties over a large class of functional spaces and can accommodate a wide variety of functional forms observed in real life applications. We develop an unified hierarchical model to encompass both the adaptive wavelet-based function estimation model and the logistic classification model. We couple tog...
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作者：Dahl, David B.; Newton, Michael A.
作者单位：Texas A&M University System; Texas A&M University College Station; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
摘要：Multiple hypothesis testing and clustering have been the subject of extensive research in high-dimensional inference, yet these problems usually have been treated separately. By defining true clusters in terms of shared parameter values, we could improve the sensitivity of individual tests, because more data bearing on the same parameter values are available. We develop and evaluate a hybrid methodology that uses clustering information to increase testing sensitivity and accommodates uncertain...
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作者：Smith, Michael; Fahrmeir, Ludwig
作者单位：University of Melbourne; University of Sydney; University of Munich
摘要：We propose a procedure to undertake Bayesian variable selection and model averaging for a series of regressions located on a lattice. For those regressors that are in common in the regressions, we consider using an Ising prior to smooth spatially the indicator variables representing whether or not the variable is zero or nonzero in each regression. This smooths spatially the probabilities that each independent variable is nonzero in each regression and indirectly smooths spatially the regressi...
