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Robust Methods for Data Reduction

Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these;

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Robust Methods for Data Reduction

Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these;

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Functional Genomics

This collection of robust, readily reproducible methods for microarray-based studies includes expert guidance in the optimal data analysis;

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Sufficient Dimension Reduction

expansion. Reflects most recent advances such as nonlinear sufficient dimension reduction, dimension folding for tensorial data, as well as;

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Sufficient Dimension Reduction

tensorial data, as well as sufficient dimension reduction for functional data. Includes a set of computer codes written in R that;

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Robust Statistics

. * Covers regression methods for data mining applications. * Includes examples with real data and applications using the S-Plus robust statistics;

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Robust Methods in Biostatistics

exactly match the postulated models as it is the case for example with outliers. Robust Methods in Biostatistics proposes robust alternatives to;

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Model Reduction of Complex Dynamical Systems

kernel-based methods; surrogate modeling for design and optimization, with special emphasis on control and data assimilation; model reduction;

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System Reduction for Nanoscale IC Design

cycles in the microelectronics industry, and proposes methods of model reduction which facilitate circuit and device simulation for specific;

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System Reduction for Nanoscale IC Design

cycles in the microelectronics industry, and proposes methods of model reduction which facilitate circuit and device simulation for specific;

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Robust Statistics for Signal Processing

. Topics covered include advanced robust methods for complex-valued data, robust covariance estimation, penalized regression models, dependent data;

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Robust Multivariate Analysis

by theory. The robust techniques are illustrated for methods such as principal component analysis, canonical correlation analysis, and factor;

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Robust Statistics

most appropriate robust methods for their problems Features computational algorithms for the core methods Robust statistics research;

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Positive 1d and 2D Systems

treatment explaining the genesis of randomized algorithms in the principles of probability theory to their use for robust analysis and controller;

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Modern Nonparametric, Robust and Multivariate Methods

methods for complex data structures. Some examples from statistical signal processing are also given. The volume is dedicated to Hannu Oja on the;

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Introduction to Robust Estimation and Hypothesis Testing

easy-to-use software. This trusted resource provides an overview of modern robust methods, including improved techniques for dealing with;

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Interpolatory Methods for Model Reduction

over the past two decades, covering both classical projection frameworks for model reduction and data-driven, nonintrusive frameworks. This;

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Simulation and Robust Statistics

This book addresses the topics statistical simulation and robust estimation. It is accompanied by several software packages for the open;

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Robust Correlation Theory & Aplications

corresponding robust and non-robust estimation procedures. Every chapter contains a set of examples with simulated and real-life data. Key features;

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Understanding Robust And Exploratory Data Analysis

some of the preeminent statisticians of the 20th century, Understanding of Robust and Exploratory Data Analysis explains why and how to use;

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Introduction to Robust Estimation and Hypothesis Testing

software. Modern robust methods provide improved techniques for dealing with outliers, skewed distribution curvature and heteroscedasticity that;

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Rank-Based Methods for Shrinkage and Selection

selection Methodology for robust data science using penalized rank estimators Theory and methods of penalized rank dispersion for ridge, LASSO and;

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Recent Advances in Robust Statistics: Theory and Applications

interested in robust statistics, data analysis and related areas. The conference is meant for theoretical and applied statisticians, data analysts;

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Nonlinear Dimensionality Reduction Techniques

This book proposes tools for analysis of multidimensional and metric data, by establishing a state-of-the-art of the existing solutions and;

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Robust Cluster Analysis and Variable Selection

author focuses on the robust clustering methods he found to be the most useful on simulated data and real-time applications. The book provides;

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Methods for Reliability Improvement and Risk Reduction

of human activity. It reviews existing methods for risk reduction that can be classified as domain-independent and introduces the following new;

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Data Reduction and Error Analysis for the Physical Sciences

and graduate level, and to provide tools for data reduction and error analysis commonly required in the physical sciences. The presentation;

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