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Robust Nonlinear Regression

; Theories of Robust Nonlinear Methods; Robust and Classical Nonlinear Regression with Autocorrelated and Heteroscedastic errors; Outlier Detection;

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Robust Regression And Outlier Detection

the research in high-breakdown affine equivariant estimators and includes an extensive bibliography in robust regression, outlier diagnostics;

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New Developments in Unsupervised Outlier Detection

This book enriches unsupervised outlier detection research by proposing several new distance-based and density-based outlier scores in a k;

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Outlier Detection for Temporal Data

have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large;

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Robust Representation for Data Analytics

This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world;

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The LORELIA Residual Test

outlier test based on robust linear regression is proposed which provides a general solution to the above problem. The LORELIA (LOcal RELIAbility;

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Rank-Based Methods for Shrinkage and Selection

on rank Robust statistics is an important field in contemporary mathematics and applied statistical methods. Rank-Based Methods for;

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Robust Regression

Robust Regression: Analysis and Applications characterizes robust estimators in terms of how much they weigh. Each observation discusses;

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Multi model Jumping Systems Robust Filtering and Fault Detection

-frequency robust filtering and higher order moment robust filtering schemes, as well as fault detection problems for multi-model jump systems, such;

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Outlier Detection Techniques and Applications

This book, drawing on recent literature, highlights several methodologies for the detection of outliers and explains how to apply them to;

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Algorithms for Regression and Classification

The focus of this dissertation is on robust regression and classification in genetic association studies. In the context of robust;

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Outlier Analysis

, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains;

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Outlier Analysis

, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains;

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Outlier Ensembles

) differences in the ensemble techniques for the classification and outlier detection problems are explored. These subtle differences do impact the;

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Developing VO2max Prediction Models Using Machine Learning Methods

Vector Machines (SVM), Multi-layer Feed-forward Artificial Neural Networks (MFANN) and Multiple Linear Regression (MLR) on different data sets;

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Chemometrics with R

specific topics in the area of chemometrics, such as outlier detection, and biomarker identification. The corresponding R code is provided for all;

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Distributionally Robust Learning

structure in the predictors; (iii) distributionally robust multi-output regression and multiclass classification; (iv) optimal decision making that;

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Finding Ghosts in Your Data

language to build a robust service for anomaly detection against a variety of data types. The book starts with an overview of what anomalies and;

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Regression Analysis by Example

of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. The book now includes a new chapter on the;

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Modeling in Mathematics

representing noisy sounds with short-time sinusoids, robust Bayesian regularized estimation based on regression model and robust quadratic regression;

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Finding Ghosts in Your Data

language to build a robust service for anomaly detection against a variety of data types. The book starts with an overview of what anomalies and;

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Robust Statistics

regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust;

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Robust Statistics

types of scale estimates, asymptotic minimax theory, regression, robust covariance, and robust design. In addition to an extended treatment;

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Let's Get Rich to Save Energy

, outlier detection, fixed effects and the addition of new variables. An inverted U-relationship could be found for GNI per capita in all the tests;

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State Estimation for Robotics

filter, sigmapoint and particle filters, robust estimation for outlier rejection, and continuous-time trajectory estimation and its connection to;

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The Maximum Consensus Problem

Outlier-contaminated data is a fact of life in computer vision. For computer vision applications to perform reliably and accurately;

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The Maximum Consensus Problem

Outlier-contaminated data is a fact of life in computer vision. For computer vision applications to perform reliably and accurately;

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