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作者：Witten, Daniela M.; Tibshirani, Robert
作者单位：Stanford University
摘要：We propose covariance-regularized regression, a family of methods for prediction in high dimensional settings that uses a shrunken estimate of the inverse covariance matrix of the features to achieve superior prediction. An estimate of the inverse covariance matrix is obtained by maximizing the log-likelihood of the data, under a multivariate normal model, subject to a penalty; it is then used to estimate coefficients for the regression of the response onto the features. We show that ridge reg...
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作者：Klueppelberg, Claudia; Kuhn, Gabriel
作者单位：Technical University of Munich
摘要：We extend the standard approach of correlation structure analysis for dimension reduction of high dimensional statistical data. The classical assumption of a linear model for the distribution of a random vector is replaced by the weaker assumption of a model for the copula. For elliptical copulas a correlation-like structure remains, but different margins and non-existence of moments are possible. After introducing the new concept and deriving some theoretical results we observe in a simulatio...
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作者：Maity, Arnab; Carroll, Raymond J.; Mammen, Enno; Chatterjee, Nilanjan
作者单位：Texas A&M University System; Texas A&M University College Station; University of Mannheim; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI)
摘要：Motivated from the problem of testing for genetic effects on complex traits in the presence of gene-environment interaction, we develop score tests in general semiparametric regression problems that involves Tukey style 1 degree-of-freedom form of interaction between parametrically and non-parametrically modelled covariates. We find that the score test in this type of model, as recently developed by Chatterjee and co-workers in the fully parametric setting, is biased and requires undersmoothin...
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作者：Pokern, Yvo; Stuart, Andrew M.; Wiberg, Petter
作者单位：University of Warwick
摘要：Hypoelliptic diffusion processes can be used to model a variety of phenomena in applications ranging from molecular dynamics to audio signal analysis. We study parameter estimation for such processes in situations where we observe some components of the solution at discrete times. Since exact likelihoods for the transition densities are typically not known, approximations are used that are expected to work well in the limit of small intersample times Delta t and large total observation times N...
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作者：Shively, Thomas S.; Sager, Thomas W.; Walker, Stephen G.
作者单位：University of Texas System; University of Texas Austin; University of Kent
摘要：The paper proposes two Bayesian approaches to non-parametric monotone function estimation. The first approach uses a hierarchical Bayes framework and a characterization of smooth monotone functions given by Ramsay that allows unconstrained estimation. The second approach uses a Bayesian regression spline model of Smith and Kohn with a mixture distribution of constrained normal distributions as the prior for the regression coefficients to ensure the monotonicity of the resulting function estima...
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作者：Jansen, Maarten; Nason, Guy P.; Silverman, B. W.
作者单位：University of Bristol; KU Leuven; University of Oxford
摘要：For regularly spaced one-dimensional data, wavelet shrinkage has proven to be a compelling method for non-parametric function estimation. We create three new multiscale methods that provide wavelet-like transforms both for data arising on graphs and for irregularly spaced spatial data in more than one dimension. The concept of scale still exists within these transforms, but as a continuous quantity rather than dyadic levels. Further, we adapt recent empirical Bayesian shrinkage techniques to e...
