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Minimax Estimation In Linear Regression With Convex Polyhedral Constraints
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Book Synopsis Minimax estimation in linear regression with convex polyhedral constraints by : Peter Stahlecker
Download or read book Minimax estimation in linear regression with convex polyhedral constraints written by Peter Stahlecker and published by . This book was released on 1990 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Minimax Estimation in Linear Regression with Convex Polyhedral Constraints by : P. Stahlecker
Download or read book Minimax Estimation in Linear Regression with Convex Polyhedral Constraints written by P. Stahlecker and published by . This book was released on 1990 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On minimax estimation in linear regression models with ellipsoidal constraints by : Norbert Christopeit
Download or read book On minimax estimation in linear regression models with ellipsoidal constraints written by Norbert Christopeit and published by . This book was released on 1991 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Restricted Parameter Space Estimation Problems by : Constance van Eeden
Download or read book Restricted Parameter Space Estimation Problems written by Constance van Eeden and published by Springer Science & Business Media. This book was released on 2006-12-15 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph is addressed to anyone interested in the subject of restrict- parameter-space estimation, and in particular to those who want to learn, or bring their knowledge up to date, about (in)admissibility and minimaxity problems for such parameter spaces. The coverage starts in the early 1950s when the subject of inference for - stricted parameter spaces began to be studied and ends around the middle of 2004. It presents known, and also some new, results on (in)admissibility and minimaxity for nonsequential point estimation problems in restricted ?ni- dimensional parameter spaces. Relationships between various results are d- cussed and open problems are pointed out. Few complete proofs are given, but outlines of proofs are often supplied. The reader is always referred to the published papers and often results are clari?ed by presenting examples of the kind of problems an author solves, or of problems that cannot be solved by a particular result. The monograph does not touch on the subject of testing hypotheses in - stricted parameter spaces. The latest books on that subject are by Robertson, Wright and Dykstra (1988) and Akkerboom (1990), but many new results in that area have been obtained since. The monograph does have a chapter in which questions about the existence of maximum likelihood estimators are discussed. Some of their properties are also given there as well as some algorithms for computing them. Most of these results cannot be found in the Robertson, Wright, Dykstra book.
Book Synopsis Minimax Estimation in Linear Regression Under Restrictions by : Helge Blaker
Download or read book Minimax Estimation in Linear Regression Under Restrictions written by Helge Blaker and published by . This book was released on 1998 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Minimax Estimation in the Linear Regression Model with Fuzzy Inequality Constraints by : Henning Knautz
Download or read book Minimax Estimation in the Linear Regression Model with Fuzzy Inequality Constraints written by Henning Knautz and published by . This book was released on 2000 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On the representation of the linear minimax estimator in the convex linear model by : Hilmar Drygas
Download or read book On the representation of the linear minimax estimator in the convex linear model written by Hilmar Drygas and published by . This book was released on 1993 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Journal of Statistical Planning and Inference by :
Download or read book Journal of Statistical Planning and Inference written by and published by . This book was released on 1993 with total page 886 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Theory and Method Abstracts by :
Download or read book Statistical Theory and Method Abstracts written by and published by . This book was released on 1995 with total page 728 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Multiple Testing and Minimax Estimation in Sparse Linear Regression by : Weijie Su
Download or read book Multiple Testing and Minimax Estimation in Sparse Linear Regression written by Weijie Su and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In many real-world statistical problems, we observe a response variable of interest together with a large number of potentially explanatory variables of which a majority may be irrelevant. For this type of problem, controlling the false discovery rate (FDR) guarantees that most of the selected variables, often termed discoveries in a scientific context, are truly explanatory and thus replicable. Inspired by ideas from the Benjamini-Hochberg procedure (BHq), this thesis proposes a new method named SLOPE to control the FDR in sparse high-dimensional linear regression. SLOPE is a computationally efficient procedure that works by regularizing the fitted coefficients according to their ranks: the higher the rank, the larger the penalty. This adaptive regularization is analogous to the BHq, which compares more significant p-values with more stringent thresholds. Under orthogonal designs, SLOPE with the BHq critical values is proven to control FDR at any given level. Moreover, we demonstrate empirically that this method also appears to have appreciable inferential properties under more general design matrices while offering substantial power. The thesis proceeds to explore the estimation properties of SLOPE. Although SLOPE was developed from a multiple testing viewpoint, we show the surprising result that it achieves optimal squared errors under Gaussian random designs. This optimality holds under a weak assumption on the l0-sparsity level of the underlying signals, and is sharp in the sense that this is the best possible error any estimator can achieve. An appealing feature is that SLOPE does not require any knowledge of the degree of sparsity, and yet automatically adapts to yield optimal total squared errors over a wide range of l0-sparsity classes. Finally, we conclude this thesis by focusing on Nesterov's accelerated scheme, which is integral to a fast algorithmic implementation of SLOPE. Specifically, we prove that, as the step size vanishes, this scheme converges in a rigorous sense to a second-order ordinary differential equation (ODE). This continuous time ODE allows for a better understanding of Nesterov's scheme, and thus it can serve as a tool for analyzing and generalizing this scheme. A fruitful application of this tool yields a family of schemes with similar convergence rates. The ODE interpretation also suggests restarting Nesterov's scheme leading to a new algorithm, which is proven to converge at a linear rate whenever the objective is strongly convex.
Download or read book Discussiones Mathematicae written by and published by . This book was released on 2000 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis ˜Aœ Note on Minimax Estimation in Linear Regression by : Karsten Schmidt
Download or read book ˜Aœ Note on Minimax Estimation in Linear Regression written by Karsten Schmidt and published by . This book was released on 1991 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Approximate linear minimax estimation in regression analysis with ellipsoidal constraints by : Peter Stahlecker
Download or read book Approximate linear minimax estimation in regression analysis with ellipsoidal constraints written by Peter Stahlecker and published by . This book was released on 1987 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Quasi minimax estimation in the linear regression model by : Peter Stahlecker
Download or read book Quasi minimax estimation in the linear regression model written by Peter Stahlecker and published by . This book was released on 1984 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Minimax Estimation in Regression and Random Censorship Models by : Eduard N. Belitser
Download or read book Minimax Estimation in Regression and Random Censorship Models written by Eduard N. Belitser and published by . This book was released on 2000 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Minimax estimation with additional linear restrictions by : Bernd Schipp
Download or read book Minimax estimation with additional linear restrictions written by Bernd Schipp and published by . This book was released on 1985 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mathematical Reviews written by and published by . This book was released on 2003 with total page 844 pages. Available in PDF, EPUB and Kindle. Book excerpt: