5 edition of **Robustness in data analysis** found in the catalog.

- 329 Want to read
- 16 Currently reading

Published
**2002** by VSP in Utrecht, Boston .

Written in English

- Robust statistics.,
- Mathematical statistics.

**Edition Notes**

Includes bibliographical references (p. 291-308) and index.

Statement | Georgy L. Shevlyakov and Nikita O. Vilchevski. |

Series | Modern probability and statistics, |

Contributions | Vilʹchevskiĭ, N. O. |

Classifications | |
---|---|

LC Classifications | QA276 .S465 2002 |

The Physical Object | |

Pagination | viii, 310 p. : |

Number of Pages | 310 |

ID Numbers | |

Open Library | OL3626914M |

ISBN 10 | 9067643513 |

LC Control Number | 2002413978 |

OCLC/WorldCa | 49229049 |

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The book takes a look at the purposes and limitations of data analysis, likelihood, shape, and adaptive inference, statistical inference and measurement of entropy, and the robustness of a hierarchical.

Our work represents new results related to robustness and data analysis technologies, having denite accents both on theoretical aspects and practical needs of data processing: we have written the book.

This chapter presents the study of robustness. The Monte Carlo study should be an active process of data analysis, and not just a passive evaluation of estimate performance. In the Monte Carlo, each. robustness analysis.

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ISBN: OCLC Number: Description: viii, pages: illustrations ; 25 cm. Contents: 1. Introduction --Huber minimax approach --Hampel approach Optimization. This book presents recent research on robustness in econometrics.

Robust data processing techniques – i.e., techniques that yield results minimally affected by outliers – and their applications to real-life economic and financial situations are the main focus of this book.

The book. Scientific Inference, Data Analysis, and Robustness: Proceedings of a Conference Conducted by the Mathematics Research Center, the University of Wisconsin—Madison, November 4–6, - Kindle Manufacturer: Academic Press.

Robustness in Data Analysis (Modern Probability and Statistics) (Modern Probability and Statistics, 6) Hardcover – Decem by Georgy L. Shevlyakov (Author) › Visit Amazon's Author: Georgy L. Shevlyakov, Nikita O. Vilchevski. Robustness Analysis Robustness analysis provides an approach to the structuring of problem situations in which uncertainty is high, and where decisions can or must be staged sequentially.

The specific. The individual contributions of leading experts provide a textbook-style overview of the topic, supplemented by current research results and questions.

The statistical theory and methods in this. The final result will not do, it is very interesting to see whether initial results comply with the later ones as robustness testing intensifies through the paper/study. Cite 1 Recommendation. So a "sensitivity analysis" or a "robustness analysis" using Monte Carlo or Bayesian or other methods is an analysis aimed at checking if a certain method is robust.

This volume addresses the field of robust statistics and data analysis. Topics covered include: a probability-free approach in data analysis; minimax variance estimators of location, scale, Read more.

The book takes a look at the purposes and limitations of data analysis, likelihood, shape, and adaptive inference, statistical inference and measurement of entropy, and the robustness of a Book Edition: 1.

This book provides a broad coverage of the recent advances in robustness analysis in decision aiding, optimization, and analytics. It offers a comprehensive illustration of the challenges that robustness. Sensitivity analysis. Robust Bayesian analysis, also called Bayesian sensitivity analysis, investigates the robustness of answers from a Bayesian analysis to uncertainty about the precise details of the.

Rosenberg and Stephen [ROSENBERG] introduced robustness analysis as a way for filling the gap between analysis (the what) and design (the how).From that point of view robustness analysis is a.

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. Robustness analysis in JavaBayes. The core inference engine in JavaBayes provides support for robustness analysis of Bayesian networks.

Robustness analysis employs sets of distributions to. An established authority in the field of robust statistics, Dr. Huber is the author or coauthor of four books and more than seventy journal articles in the areas of statistics and data analysis.

all that this may imply for policy analysis and economic insight. Examples are pervasive, appearing in almost every area of applied econometrics. For exam-ple, of the 98 papers published in The American.

The book covers the definition of poverty indicators, data collection and integration methods, the impact of sampling design, weighting and variance estimation, the issue of SAE.

Read "Robustness and Complex Data Structures Festschrift in Honour of Ursula Gather" by available from Rakuten Kobo.

This Festschrift in honour of Ursula Gather’s 60th birthday deals with Brand: Springer Berlin Heidelberg. Robustness Tests for Quantitative Research The uncertainty researchers face in specifying their estimation models threa-tens the validity of their inferences.

In regression analyses of observational File Size: KB. The book contains new results related to robustness and data analysis technologies, including both theoretical aspects and practical needs of data processing, which have been relatively inaccessible as.

Robustness testing allows researchers to explore the stability of their main estimates to plausible variations in model specifications. This highly accessible book presents the logic of robustness Cited by: $\begingroup$ Robust is usually a word that applies to an algorithm's ability to return correct and useful output (or at least mitigate damages and fail gracefully) in the face of hostile input such as missing.

This book presents recent research on robustness in econometrics. Robust data processing techniques – i.e., techniques that yield results minimally affected by outliers – and their applications to real-life economic and financial situations are the main focus of this book.

uval is a structure whose field names and values are the uncertain variable names and values to use for simulation. You can set uval to [] to use nominal values for the uncertain variables or vary uval to.

"Sensitivity analysis" is often used in the context of missing data. Many convenient methods are valid if data are missing at random (MAR), but you can never really be certain that your data are MAR. A way. Robustness analysis (after Roy) This use of the term ‘robustness analysis’ entered the literature some 13 years after it was first introduced in the sense employed in this article.

As with sensitivity analysis this. The Essential Roles of Robustness Analysis You will refine both your use case text and your static model as a result of robustness analysis. Within the ICONIX process, this simple but highly useful technique. Econometricians must make educated guesses about the structure of the data generation processes in non-experiment al data.

The studies that result rely on these structural assumptions can be File Size: KB. Robustness analysis is discussed in Chapter 5, together with its comparison with geometrical strength and covariance analyses. Chapter 6 is devoted to describing how robustness analysis is implemented File Size: 6MB.

Robustness in Data Analysis Criteria and Methods. Series:Modern Probability and Statistics. Book Book Series. Frontmatter Pages I-VI. Get Access to Full Text. Preface Pages VII-VIII. This analysis confirms what the diskmargin analysis suggested.

The closed-loop system is quite robust, in terms of stability, to the variations modeled by the uncertain parameters Delta1, Delta2, and p.

In fact. Originally published in hardcover inthis book is now offered in a Wiley Classics Library edition. A contributed volume, edited by some of the preeminent statisticians of the 20th Price: $ Robustness in Data Analysis Criteria and Methods.

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Abstract: Robustness tests emerged as social scientists response to the uncertainty they face in specifying empirical models. We argue that the logic of robustness testing warrants a fundamental File Size: KB.