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Robust Nonlinear Regression

The first book to discuss robust aspects of nonlinear regression-with applications using R software Robust Nonlinear Regression: with;

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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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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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Distributionally Robust Learning

series of learning problems, including (i) distributionally robust linear regression; (ii) distributionally robust regression with group;

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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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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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Robust Statistics

results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust;

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Regression Modeling: Methods, Theory, and Computation with SAS

methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy;

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Regression Modeling: Methods, Theory, and Computation with SAS

methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy;

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Introduction to Robust Estimation and Hypothesis Testing

included to support comprehension and practice. Includes the latest developments in robust regression Provides many new, improved;

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Introduction to Robust Estimation and Hypothesis Testing

-Plus, robust ANOVA (Analysis of Variance) and regression. It guides advanced students and other professionals through the basic strategies used;

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Regression And Time Series Model Selection

wavelets) and semiparametric regression models, and quasi-likelihood and robust regression models. Information-based model selection criteria are;

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Introductory Statistics for Business and Economics

This Fourth Edition includes new sections on graphs, robust estimation, expected value and the bootstrap, in addition to new material on;

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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 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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Student Workbook to accompany Introductory Statistics for Business and Economics 4e and Introductory Statistics 5e

This Fourth Edition includes new sections on graphs, robust estimation, expected value and the bootstrap, in addition to new material on;

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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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Applied Regression Analysis

chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures;

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Robust Multivariate Analysis

theory is given. The text develops among the first practical robust regression and robust multivariate location and dispersion estimators backed;

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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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Solutions Manual to accompany Modern Regression Methods, 2e

analysis. The book provides a unique treatment of fundamental regression methods, such as diagnostics, transformations, robust regression, and;

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Introduction to Robust Estimation and Hypothesis Testing

> Extensive revisions to cover the latest developments in robust regression Covers latest improvements in ANOVA Includes;

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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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Introductory Statistics

literature) to support the theoretical discussion. Emphasizes the regression model, including nonlinear and multiple regression. Topics covered;

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Foundations of Behavioral Statistics

only a single dependent variable, ranging from classical and robust location descriptive statistics, through effect sizes, and on through ANOVA;

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Regression Modeling Strategies

parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression. As in the;

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Regression Modeling Strategies

parametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression. As in the;

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Einde inhoud

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