Last edited by Yozshuramar

Friday, July 31, 2020 | History

3 edition of **Robust estimation** found in the catalog.

Robust estimation

Robert G. Staudte

- 121 Want to read
- 32 Currently reading

Published
**1980**
by Queen"s University in Kingston, Ont
.

Written in English

- Estimation theory.,
- Robust statistics.

**Edition Notes**

Includes bibliographies.

Statement | by Robert G. Staudte. |

Series | Queen"s papers in pure and applied mathematics ;, no. 53 |

Classifications | |
---|---|

LC Classifications | QA3 .Q38 no. 53, QA276.8 .Q38 no. 53 |

The Physical Object | |

Pagination | iv, 111 p. : |

Number of Pages | 111 |

ID Numbers | |

Open Library | OL4229237M |

LC Control Number | 80509447 |

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 : Elsevier Science. About this book 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.

Maximum Likelihood Estimation with Stata, Fourth Edition, is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in providing comprehensive coverage of Stata’s ml command for writing ML estimators, the book presents an overview of the underpinnings of maximum likelihood and how to think about ML estimation. The first systematic, book-length treatment of the subject. Begins with a general introduction and the formal mathematical background behind qualitative and quantitative robustness. Stresses concepts. Provides selected numerical algorithms for computing robust estimates, as well as convergence proofs. Tables contain quantitative robustness information for a variety of estimates.5/5(1).

About This Book. 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. Praise for Robust Portfolio Optimization and Management In the half century since Harry Markowitz introduced his elegant theory for selecting portfolios, investors and scholars have extended and refined its application to a wide range of real-world problems, culminating in the contents of this masterful book. Fabozzi, Kolm, Pachamanova, and Focardi deserve high praise for producing a.

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Introduction to Robust Estimation and Hypothesis Testing, Second Edition, focuses on the practical applications of modern, 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.

This is useful text that I referenced in my book on the bootstrap. The level of the book is intermediate and is appropriate for an advanced undergraduate or a graduate course in modern statistical inference.

It covers both the robust estimation of location and scale and has a nice bibliography of the literature as of Cited by: Purchase Introduction to Robust Estimation and Hypothesis Testing - 2nd Edition. Print Book & E-Book. ISBNIntroduction 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. Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, Second Edition reflects new developments in estimation theory and design techniques.

As the title suggests, the major feature of this edition is the inclusion of robust methods. Three new chapters cover Robust estimation book robust Kalman filter, H-infinity filtering, and H Cited Robust estimation book Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not statistical methods have been developed for many common problems, such as estimating location, scale, and regression motivation is to produce statistical methods that are not unduly affected by outliers.

Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, Second Edition reflects new developments in estimation theory and design techniques. As the title suggests, the major feature of this edition is the inclusion of robust by: Abstract.

This paper contains a new approach toward a theory of robust estimation; it treats in detail the asymptotic theory of estimating a location parameter for contaminated normal distributions, and exhibits estimators—intermediaries between sample mean and sample median—that are asymptotically most robust (in a sense to be specified) among all translation invariant estimators.

ROBETH (written in ANSI FORTRAN 77) is a systematized collection of algorithms that allows computation of a broad class of procedures based on M- and high-breakdown point estimation, including robust regression, robust testing of linear hypotheses, and robust coveriances. This book describes the computational procedures included in ROBETH.

A Series of Reference Books and Textbooks Editor FRANK L. LEWIS, PH.D. Professor Automation and Robotics Research Institute The University of Texas at Arlington Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, Second Edition,Frank L. Half-Day 1: Introduction to Robust Estimation Techniques 18 / 34 The Outlier ProblemMeasuring RobustnessLocation M-EstimationRegression M-EstimationExample From Molecular Spectroscopy Estimation of the Scale Parameter The scale parameter ˙is usually unknown.

But it. Robust statistics research results of the last decade included in this 2 nd edition include: fast deterministic robust regression, finite-sample robustness, robust regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust mixed linear models.

In robust statistics, robust regression is a form of regression analysis designed to overcome some limitations of traditional parametric and non-parametric sion analysis seeks to find the relationship between one or more independent variables and a dependent n widely used methods of regression, such as ordinary least squares, have favourable properties if their.

Get this from a library. Introduction to robust estimation and hypothesis testing. [Rand R Wilcox] -- 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. The theory of robust estimation is based on specified properties of specified estimators under specified conditions. This book was written as the result of a study undertaken to establish the. robust application of quantitative techniques, there is now a widespread recognition for the need of a disciplined approach to the analysis and management of investments.

In this book we bring together concepts from ﬁnance, economic the-ory, robust statistics, econometrics, and robust. Of the many books on robust control this appears to be the most readable. At least its introduction and motivation are readable.

Chen, C., T., Linear System Theory and Design, Oxford University Press, Inc., Notes: This book is a resource for those interested in the mathematical details of.

Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, Second Edition reflects new developments in estimation theory and design techniques.

As the title suggests, the major feature of this edition is the inclusion of robust methods. We use robust optimization principles to provide robust maximum likelihood estimators that are protected against data errors. Both types of input data errors are considered: (a) the adversarial type, modeled using the notion of uncertainty sets, and (b) the probabilistic type, modeled by distributions.

Two books ab out practical application of robust. Robust M-estimation of scale and regression paramet ers can be performed using the. rlm function, introduced in Section. Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, 2e Written for senior undergraduate or first-year graduate courses, this book covers estimation theory and design techniques important in navigation, communication systems, and signal processing.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.

Introduction to Robust Estimating and Hypothesis Testing, 4th Editon, is a ‘how-to’ on the application of robust methods using available 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 :