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Rank-Based Methods for Shrinkage and Selection

Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based;

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Nonparametric Statistical Methods Using R

nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and;

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Nonparametric Statistical Methods Using R

traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to;

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Supporting Shrinkage

Supporting Shrinkage describes a new approach to citizen-engaged, community-focused planning methods and technologies for cities and;

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Handbook of Molded Part Shrinkage and Warpage

for some plastics than others. Why is that? What can you do about it? The Handbook of Molded Part Shrinkage and Warpage is the first and only;

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Support Vector Data Description

This book is based on the basis that machine learningalgorithms and rank based statistical methods are abetter choice to develop a robust;

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Robust Nonparametric Statistical Methods

more models and methods and significantly extends the possible analyses based on ranks. New to the Second Edition A new;

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Regularization, Optimization, Kernels, and Support Vector Machines

-negative matrix factorization Examines low-rank matrix and tensor-based models Presents advanced kernel methods for batch and online machine;

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Regularization, Optimization, Kernels, and Support Vector Machines

portfolio selection Addresses non-negative matrix factorization Examines low-rank matrix and tensor-based models Presents;

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Specialized Molding Techniques

how to alleviate the problem by careful design of the molded part and the mold and by proper material selection. It also examines computer;

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Robust Statistical Methods with R, Second Edition

models concentrates mainly on the rank procedures, less sensitive to errors than other procedures. This book will be an invaluable resource for;

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Robust Statistical Methods with R, Second Edition

models concentrates mainly on the rank procedures, less sensitive to errors than other procedures. This book will be an invaluable resource for;

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Shrinking Cities

and decline-centered governance, which has important implications for questions of sustainability and resilience in U.S. cities. Finally, the;

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Shrinking Cities

and decline-centered governance, which has important implications for questions of sustainability and resilience in U.S. cities. Finally, the;

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Autogenous Shrinkage of Concrete

This book forms the proceedings of a workshop held in Hiroshima in June 1998 and derive from the work of a Technical Committee of the;

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Predicting swelling characteristics with swell-related parameters

engineering structures. It is necessary to identify these soils based on their degree of expansion. Direct measurement of swell potential and swelling;

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Autogenous Shrinkage of Concrete

This book forms the proceedings of a workshop held in Hiroshima in June 1998 and derive from the work of a Technical Committee of the;

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Learning to Rank for Information Retrieval and Natural Language Processing

, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include;

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Rank and Pseudo-Rank Procedures for Independent Observations in Factorial Designs: Using R and SAS

hypothesis tests and confidence intervals based on pseudo-ranks. Offering clear explanations, an overview of the modern rank- and pseudo-rank;

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Wavelets in Functional Data Analysis

visualization (using Andrews' plots), wavelet shrinkage (a simple, yet powerful, procedure for nonparametric models) and a selection of estimation and;

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Shrinkage Estimation for Mean and Covariance Matrices

modern and classical shrinkage estimation, but is also required for further development of the field. Beginning with the notion of decision;

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Regression And Time Series Model Selection

selection techniques for univariate and multivariate regression models, univariate and multivariate autoregressive models, nonparametric (including;

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Computational Methods for Modeling of Nonlinear Systems by Anatoli Torokhti and Phil Howlett

estimation; methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation;

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Fundamentals of Matrix Computations

decomposition based on adaptive over-complete dictionary, lower bounds for the low-rank matrix approximation and a semi-smoothing augmented lagrange;

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Feature Learning and Understanding: Algorithms and Applications

analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition;

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Feature Learning and Understanding

analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition;

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Supplier Selection

supplier selection criteria and various multi-criteria based supplier's evaluation methods for forward, reverse and mass customized supply chain;

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