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Robust Estimation Based On Grouped Adjusted Data
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Book Synopsis Robust Estimation Based on Grouped-adjusted Data in Linear Regression Models by : Stanford University. Econometric Workshop
Download or read book Robust Estimation Based on Grouped-adjusted Data in Linear Regression Models written by Stanford University. Econometric Workshop and published by . This book was released on 1985 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Estimation Based on Grouped-adjusted Data in Censored Regression Models by : Stanford University. Econometric Workshop
Download or read book Robust Estimation Based on Grouped-adjusted Data in Censored Regression Models written by Stanford University. Econometric Workshop and published by . This book was released on 1986 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Estimation Based on Grouped-adjusted Data by : Kazumitsu Nawata
Download or read book Robust Estimation Based on Grouped-adjusted Data written by Kazumitsu Nawata and published by . This book was released on 1986 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Estimation for Grouped Data by : Maria-Pia Victoria-Feser
Download or read book Robust Estimation for Grouped Data written by Maria-Pia Victoria-Feser and published by . This book was released on 1996 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Estimation for Grouped Data by : Maria-Pia Victoria-Feser
Download or read book Robust Estimation for Grouped Data written by Maria-Pia Victoria-Feser and published by . This book was released on 1996 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robustness in Statistics by : Robert L. Launer
Download or read book Robustness in Statistics written by Robert L. Launer and published by . This book was released on 1979 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to robust estimation; The robustness of residual displays; Robust smoothing; Robust pitman-like estimators; Robust estimation in the presence of outliers; Study of robustness by simulation: particularly improvement by adjustment and combination; Robust techniques for the user; Application of robust regression to trajectory data reduction; Tests for censoring of extreme values (especially) when population distributions are incompletely defined; Robust estimation for time series autoregressions; Robust techniques in communication; Robustness in the strategy of scientific model building; A density-quantile function perspective on robust.
Book Synopsis Random Sample Consensus by : Fouad Sabry
Download or read book Random Sample Consensus written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2024-04-30 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is Random Sample Consensus Random sample consensus, also known as RANSAC, is an iterative method that is used to estimate the parameters of a mathematical model based on a collection of observed data that includes outliers. This method is used in situations where the outliers are permitted to have no impact on the values of the estimates. The conclusion is that it is also possible to view it as a tool for detecting outliers. An algorithm is considered to be non-deterministic if it is able to generate a suitable result only with a certain probability, and this likelihood increases as the number of iterations that are permitted via the method increases. In 1981, Fischler and Bolles, who were working at SRI International, were the ones who initially published the algorithm. In order to solve the Location Determination Problem (LDP), which is a problem in which the objective is to find the points in space that project onto an image and then convert those points into a set of landmarks with known positions, they utilized RANSAC. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Random sample consensus Chapter 2: Estimator Chapter 3: Least squares Chapter 4: Outlier Chapter 5: Cross-validation (statistics) Chapter 6: Errors and residuals Chapter 7: Mixture model Chapter 8: Robust statistics Chapter 9: Image stitching Chapter 10: Resampling (statistics) (II) Answering the public top questions about random sample consensus. (III) Real world examples for the usage of random sample consensus in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Random Sample Consensus.
Book Synopsis Introduction to Robust Estimation and Hypothesis Testing by : Rand R. Wilcox
Download or read book Introduction to Robust Estimation and Hypothesis Testing written by Rand R. Wilcox and published by Academic Press. This book was released on 2021-09-18 with total page 930 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Robust Estimating and Hypothesis Testing, Fifth Edition is a useful ‘how-to’ on the application of robust methods utilizing easy-to-use software. This trusted resource provides an overview of modern robust methods, including improved techniques for dealing with outliers, skewed distribution curvature, and heteroscedasticity that can provide substantial gains in power. Coverage includes techniques for comparing groups and measuring effect size, current methods for comparing quantiles, and expanded regression methods for both parametric and nonparametric techniques. The practical importance of these varied methods is illustrated using data from real world studies. Over 1700 R functions are included to support comprehension and practice. Includes the latest developments in robust regression Provides many new, improved and accessible R functions Offers comprehensive coverage of ANOVA and ANCOVA methods
Book Synopsis Robust Estimation with Discrete Explanatory Variables by : Pavel Cizek
Download or read book Robust Estimation with Discrete Explanatory Variables written by Pavel Cizek and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The least squares estimator is probably the most frequently used estimation method in regression analysis. Unfortunately, it is also quite sensitive to data contamination and model misspecification. Although there are several robust estimators designed for parametric regression models that can be used in place of least squares, these robust estimators cannot be easily applied to models containing binary and categorical explanatory variables. Therefore, I design a robust estimator that can be used for any linear regression model no matter what kind of explanatory variables the model contains. Additionally, I propose an adaptive procedure that maximizes the efficiency of the proposed estimator for a given data set while preserving its robustness.
Book Synopsis Estimation of Generalized Regression Models by the Grouping Method by : Kazumitsu Nawata
Download or read book Estimation of Generalized Regression Models by the Grouping Method written by Kazumitsu Nawata and published by . This book was released on 1993 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robustness in Statistics by : Robert L. Launer
Download or read book Robustness in Statistics written by Robert L. Launer and published by Academic Press. This book was released on 2014-05-12 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robustness in Statistics contains the proceedings of a Workshop on Robustness in Statistics held on April 11-12, 1978, at the Army Research Office in Research Triangle Park, North Carolina. The papers review the state of the art in statistical robustness and cover topics ranging from robust estimation to the robustness of residual displays and robust smoothing. The application of robust regression to trajectory data reduction is also discussed. Comprised of 14 chapters, this book begins with an introduction to robust estimation, paying particular attention to iteration schemes and error structure of estimators. Sensitivity and influence curves as well as their connection with jackknife estimates are described. The reader is then introduced to a simple analog of trimmed means that can be used for studying residuals from a robust point-of-view; a class of robust estimators (called P-estimators) based on the location and scale-invariant Pitman estimators of location; and robust estimation in the presence of outliers. Subsequent chapters deal with robust regression and its use to reduce trajectory data; tests for censoring of extreme values, especially when population distributions are incompletely defined; and robust estimation for time series autoregressions. This monograph should be of interest to mathematicians and statisticians.
Book Synopsis Statistical Theory and Method Abstracts by :
Download or read book Statistical Theory and Method Abstracts written by and published by . This book was released on 1997 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Estimates of Location by : David F. Andrews
Download or read book Robust Estimates of Location written by David F. Andrews and published by . This book was released on 2016-04-03 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Because estimation involves inferring information about an unknown quantity on the basis of available data, the selection of an estimator is influenced by its ability to perform well under the conditions that are assumed to underlie the data. Since these conditions are never known exactly, the estimators chosen must be robust; i.e., they must be able to perform well under a variety of underlying conditions. 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 interaction of these three components over as large a range as possible. Originally published in 1972. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.
Book Synopsis Robust Estimation for AR(1) Process with Missing Data by : Jianqing Jin
Download or read book Robust Estimation for AR(1) Process with Missing Data written by Jianqing Jin and published by . This book was released on 1994 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis JOURNAL OF ECONOMETRICS: ANNALS 1991-3, THE MEASUREMENT AND ANALYSIS OF WELFARE -- VOLUME 50 (1991) by :
Download or read book JOURNAL OF ECONOMETRICS: ANNALS 1991-3, THE MEASUREMENT AND ANALYSIS OF WELFARE -- VOLUME 50 (1991) written by and published by . This book was released on 1991 with total page 824 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Biostatistical Methods by : John M. Lachin
Download or read book Biostatistical Methods written by John M. Lachin and published by John Wiley & Sons. This book was released on 2009-09-25 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive coverage of classical and modern methods of biostatistics Biostatistical Methods focuses on the assessment of risks and relative risks on the basis of clinical investigations. It develops basic concepts and derives biostatistical methods through both the application of classical mathematical statistical tools and more modern likelihood-based theories. The first half of the book presents methods for the analysis of single and multiple 2x2 tables for cross-sectional, prospective, and retrospective (case-control) sampling, with and without matching using fixed and two-stage random effects models. The text then moves on to present a more modern likelihood- or model-based approach, which includes unconditional and conditional logistic regression; the analysis of count data and the Poisson regression model; and the analysis of event time data, including the proportional hazards and multiplicative intensity models. The book contains a technical appendix that presents the core mathematical statistical theory used for the development of classical and modern statistical methods. Biostatistical Methods: The Assessment of Relative Risks: * Presents modern biostatistical methods that are generalizations of the classical methods discussed * Emphasizes derivations, not just cookbook methods * Provides copious reference citations for further reading * Includes extensive problem sets * Employs case studies to illustrate application of methods * Illustrates all methods using the Statistical Analysis System(r) (SAS) Supplemented with numerous graphs, charts, and tables as well as a Web site for larger data sets and exercises, Biostatistical Methods: The Assessment of Relative Risks is an excellent guide for graduate-level students in biostatistics and an invaluable reference for biostatisticians, applied statisticians, and epidemiologists.
Book Synopsis Semiparametric Estimation of Binary Choice Models Based on Medians of the Grouped Data by : Kazumitsu Nawata
Download or read book Semiparametric Estimation of Binary Choice Models Based on Medians of the Grouped Data written by Kazumitsu Nawata and published by . This book was released on 1989 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt:
