Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 8 of 8 for “"Shrinkage estimation"”.
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Shrinkage Estimation for Aalen's Additive Model
… functions of time. In this project we consider estimation of Aalen's nonparametric regression coefficients when some uncertain prior information is available about these coefficients. More precisely, we combine unrestricted estimators and estimators that are restricted by a linear hypothesis …
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Comparison of ridge and other shrinkage estimation techniques
Shrinkage estimation is an increasingly popular class of biased parameter estimation techniques, vital when the columns of the matrix of independent variables X exhibit dependencies or near dependencies. These dependencies often lead to serious problems in least squares estimation: inflated …
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Distributed Supervised Statistical Learning
… of machines to deploy is addressed for several estimation methods, notable answers to the question are reviewed in this literature. We have considered a specific class of Liu-type shrinkage estimation methods for distributed statistical inference. We also conduct a Monte Carlo simulation study …
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Geometric Methods for Point Estimation
… dissertation is on geometric aspects of point estimation problems. In the first half of this work, we examine the estimation of location parameters for non-Euclidean data that lies in a known manifold or metric space. Ideas from statistical decision theory motivate the construction of new …
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Model-based methods for high-dimensional multivariate analysis
… the regression coefficient matrix through shrinkage estimation of the parameters of the inverse regression, or the conditional distribution of the predictors given the responses. We establish a convergence rate bound for estimators in our class and we study two examples, which respectively …
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Contributions to the Analysis of Experiments Using Empirical Bayes Techniques
… and practical significance. We perform estimation via an empirical Bayes data analysis methodology that has been detailed in the recent literature. But then propose an alternative to the usual next step in determining optimal factor level settings. Instead of implementing variable or …