Search results for: robust-estimation-and-testing

Robust Estimation and Testing

Author : Robert G. Staudte
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An introduction to the theory and methods of robust statistics, providing students with practical methods for carrying out robust procedures in a variety of statistical contexts and explaining the advantages of these procedures. In addition, the text develops techniques and concepts likely to be useful in the future analysis of new statistical models and procedures. Emphasizing the concepts of breakdown point and influence functon of an estimator, it demonstrates the technique of expressing an estimator as a descriptive measure from which its influence function can be derived and then used to explore the efficiency and robustness properties of the estimator. Mathematical techniques are complemented by computational algorithms and Minitab macros for finding bootstrap and influence function estimates of standard errors of the estimators, robust confidence intervals, robust regression estimates and their standard errors. Includes examples and problems.

Introduction to Robust Estimation and Hypothesis Testing

Author : Rand R. Wilcox
File Size : 71.7 MB
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"This book focuses on the practical aspects of modern and robust statistical methods. The increased accuracy and power of modern methods, versus conventional approaches to the analysis of variance (ANOVA) and regression, is remarkable. Through a combination of theoretical developments, improved and more flexible statistical methods, and the power of the computer, it is now possible to address problems with standard methods that seemed insurmountable only a few years ago"--

Robust Estimation and Testing

Author : Jiazhong You
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"High breakdown point, bounded influence and high efficiency at the Gaussian model are desired properties of robust regression estimators. Robustness of validity, robustness of efficiency and high breakdown point size and power are the fundamental goals in robust testing. The objective of this dissertation is to examine the finite-sample properties of robust estimators and tests, and to find some useful applications for them. This is accomplished by extensive Monte Carlo experiments and other inference techniques in various contamination situations. In the linear regression model with an outlying regressor and deviations from the normal error distribution, robust estimators demonstrate noticeable advantages over the standard LS and maximum likelihood (ML) estimators. Our findings reveal that the finite-sample behavior of the robust estimators is very different from their asymptotic properties. The robust properties of estimators carry over to test statistics based on these estimators. The robust tests we proposed can achieve to the large extent the fundamental goals in robust testing. Economic applications on modelling the household consumption behavior and testing for (G)ARCH effects show that one can capture big gains from the appropriate utilization of the robust methods even at very simple models." --

Robust Estimation and Testing of Location for Symmetric Stable Distributions

Author : Ateq Ahmed M. Al-Ghamedi
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Robust Estimation and Hypothesis Testing

Author : Moti Lal Tiku
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In Statistical Theory And Practice, A Certain Distribution Is Usually Assumed And Then Optimal Solutions Sought. Since Deviations From An Assumed Distribution Are Very Common, One Cannot Feel Comfortable With Assuming A Particular Distribution And Believing It To Be Exactly Correct. That Brings The Robustness Issue In Focus. In This Book, We Have Given Statistical Procedures Which Are Robust To Plausible Deviations From An Assumed Mode. The Method Of Modified Maximum Likelihood Estimation Is Used In Formulating These Procedures. The Modified Maximum Likelihood Estimators Are Explicit Functions Of Sample Observations And Are Easy To Compute. They Are Asymptotically Fully Efficient And Are As Efficient As The Maximum Likelihood Estimators For Small Sample Sizes. The Maximum Likelihood Estimators Have Computational Problems And Are, Therefore, Elusive. A Broad Range Of Topics Are Covered In This Book. Solutions Are Given Which Are Easy To Implement And Are Efficient. The Solutions Are Also Robust To Data Anomalies: Outliers, Inliers, Mixtures And Data Contaminations. Numerous Real Life Applications Of The Methodology Are Given.

Introduction to Robust Estimation and Hypothesis Testing 3rd Edition

Author : Rand Wilcox
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This revised book provides a thorough explanation of the foundation of robust methods, incorporating the latest updates on R and S-Plus, robust ANOVA (Analysis of Variance) and regression. It guides advanced students and other professionals through the basic strategies used for developing practical solutions to problems, and provides a brief background on the foundations of modern methods, placing the new methods in historical context. Author Rand Wilcox includes chapter exercises and many real-world examples that illustrate how various methods perform in different situations. Introduction to Robust Estimation and Hypothesis Testing, Second Edition, focuses on the practical applications of modern, robust methods which can greatly enhance our chances of detecting true differences among groups and true associations among variables. * Covers latest developments in robust regression * Covers latest improvements in ANOVA * Includes newest rank-based methods * Describes and illustrated easy to use software.

Estimation and Testing of Location for Arbitrarily Right Censored Data

Author : Stephanie Green
File Size : 33.35 MB
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Robust Estimation and Hypothesis Testing

Author : Moti Lal Tiku
File Size : 56.32 MB
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Introduction to Robust Estimation and Hypothesis Testing

Author : Rand R. Wilcox
File Size : 80.83 MB
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Introduction to Robust Estimating and Hypothesis Testing, 4th Editon, is a ‘how-to’ on the application of robust methods using available software. Modern robust methods provide improved techniques for dealing with outliers, skewed distribution curvature and heteroscedasticity that can provide substantial gains in power as well as a deeper, more accurate and more nuanced understanding of data. Since the last edition, there have been numerous advances and improvements. They include new techniques for comparing groups and measuring effect size as well as new methods for comparing quantiles. Many new regression methods have been added that include both parametric and nonparametric techniques. The methods related to ANCOVA have been expanded considerably. New perspectives related to discrete distributions with a relatively small sample space are described as well as new results relevant to the shift function. The practical importance of these methods is illustrated using data from real world studies. The R package written for this book now contains over 1200 functions. New to this edition 35% revised content Covers many new and improved R functions New techniques that deal with a wide range of situations Extensive revisions to cover the latest developments in robust regression Covers latest improvements in ANOVA Includes newest rank-based methods Describes and illustrated easy to use software

Identification robust Estimation and Testing of the Zero beta CAPM

Author : Marie-Claude Beaulieu
File Size : 69.9 MB
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Robust and Distributed Hypothesis Testing

Author : Gökhan Gül
File Size : 67.49 MB
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This book generalizes and extends the available theory in robust and decentralized hypothesis testing. In particular, it presents a robust test for modeling errors which is independent from the assumptions that a sufficiently large number of samples is available, and that the distance is the KL-divergence. Here, the distance can be chosen from a much general model, which includes the KL-divergence as a very special case. This is then extended by various means. A minimax robust test that is robust against both outliers as well as modeling errors is presented. Minimax robustness properties of the given tests are also explicitly proven for fixed sample size and sequential probability ratio tests. The theory of robust detection is extended to robust estimation and the theory of robust distributed detection is extended to classes of distributions, which are not necessarily stochastically bounded. It is shown that the quantization functions for the decision rules can also be chosen as non-monotone. Finally, the book describes the derivation of theoretical bounds in minimax decentralized hypothesis testing, which have not yet been known. As a timely report on the state-of-the-art in robust hypothesis testing, this book is mainly intended for postgraduates and researchers in the field of electrical and electronic engineering, statistics and applied probability. Moreover, it may be of interest for students and researchers working in the field of classification, pattern recognition and cognitive radio.

Decentralized Clustering Based on Robust Estimation and Hypothesis Testing

Author : Dominique Pastor
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Robust Estimation and Bootstrap Testing for the Delta Distribution with Applications in Marine Sciences

Author : Abeer F. A. Al-Khouli
File Size : 53.19 MB
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Misspecification Testing and Robust Estimation of the Market Model

Author : Terence C. Mills
File Size : 65.23 MB
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Parameter Estimation and Hypothesis Testing in Linear Models

Author : Karl-Rudolf Koch
File Size : 32.95 MB
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A treatment of estimating unknown parameters, testing hypotheses and estimating confidence intervals in linear models. Readers will find here presentations of the Gauss-Markoff model, the analysis of variance, the multivariate model, the model with unknown variance and covariance components and the regression model as well as the mixed model for estimating random parameters. A chapter on the robust estimation of parameters and several examples have been added to this second edition. The necessary theorems of vector and matrix algebra and the probability distributions of test statistics are derived so as to make this book self-contained. Geodesy students as well as those in the natural sciences and engineering will find the emphasis on the geodetic application of statistical models extremely useful.

Robust Planning and Analysis of Experiments

Author : Christine Mueller
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Robust statistics and the design of experiments are two of the fastest growing fields in contemporary statistics. Up to now, there has been very little overlap between these fields. This is the first book to link these two areas by studying the influence of the design on the efficiency and robustness of robust estimators and tests. The classical approaches of experimental design and robust statistics are introduced before the areas are linked, and the author shows that robust statistical procedures profit by an appropriate choice of the design and that efficient designs for a robust statistical analysis are more applicable.

Robust Methods in Biostatistics

Author : Stephane Heritier
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Robust statistics is an extension of classical statistics that specifically takes into account the concept that the underlying models used to describe data are only approximate. Its basic philosophy is to produce statistical procedures which are stable when the data do not exactly match the postulated models as it is the case for example with outliers. Robust Methods in Biostatistics proposes robust alternatives to common methods used in statistics in general and in biostatistics in particular and illustrates their use on many biomedical datasets. The methods introduced include robust estimation, testing, model selection, model check and diagnostics. They are developed for the following general classes of models: Linear regression Generalized linear models Linear mixed models Marginal longitudinal data models Cox survival analysis model The methods are introduced both at a theoretical and applied level within the framework of each general class of models, with a particular emphasis put on practical data analysis. This book is of particular use for research students,applied statisticians and practitioners in the health field interested in more stable statistical techniques. An accompanying website provides R code for computing all of the methods described, as well as for analyzing all the datasets used in the book.

Misspecification Testing and Robust Estimation of the Market Model and Their Implications for the Event Studies

Author : Terence C. Mills
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Estimating Betas for the FT SE Industry Baskets

Author : Terence C. Mills
File Size : 20.93 MB
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Outliers in Statistical Data

Author : Vic Barnett
File Size : 80.56 MB
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Why do outlying observations arise and what should one do about them?. The accommodation approach: robust estimation and testing. Accommodation procedures for univariate samples. Testing for discordancy: principles and criteria. Specific discordancy tests for outliers in univariate sample. Outliers in directional data. Outlying sub-sample: slippage tests. Outliers in multivariate data. The outlier problem for structured data: regression, the linear model, and designed experiments. Outliers in time series: a little-explored area. Bayesian approaches to outliers. Perspective. Statistical tables.