Download or read book Robust Estimation of Location by Maximum Likelihood written by Carl Richard Jerome and published by . This book was released on 1974 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Breakthroughs in Statistics written by Samuel Kotz and published by Springer Science & Business Media. This book was released on 2013-12-01 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: Volume III includes more selections of articles that have initiated fundamental changes in statistical methodology. It contains articles published before 1980 that were overlooked in the previous two volumes plus articles from the 1980's - all of them chosen after consulting many of today's leading statisticians.
Download or read book Robust Estimates of Location written by David F. Andrews and published by Princeton University Press. This book was released on 2015-03-08 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.
Download or read book Robust Estimation of Location Via Embeddings written by Fang Dong and published by . This book was released on 1993 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Contributions to the Theory of Robust Estimation written by Frank R. Hampel and published by . This book was released on 1968 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Estimation of Location and Scale Parameters written by Loren W. Jorgenson and published by . This book was released on 1973 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Monte Carlo analysis was performed to determine the efficiency of the Harter robust estimators of the scale and location parameters of double exponential, normal, and uniform distributions relative to the maximum-likelihood estimators. Two modifications were made to the Harter estimators which increased the relative efficiency except when the underlying population was uniformly distributed. The modified estimators were then designated the Moore estimators and the Jorgenson estimators. Tables were prepared comparing the relative efficiencies of the Harter, Jorgenson, Moore, Hogg, Hodges-Lehmann, and the Switzer robust estimators of the location parameter for the double exponential, the normal, and the uniform distributions for samples of size 12 and 24. Two types of figure of merit were defined for a robust estimator. The choice of the best robust estimator is a function of sample size and the criteria used. (Author).
Download or read book Studies in Robust Estimation written by Gina Gee Chen and published by . This book was released on 1979 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Estimation and Hypothesis Testing written by Moti Lal Tiku and published by New Age International. This book was released on 2004 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Robust Estimates of Location written by Louis Alan Jaeckel and published by . This book was released on 1969 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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.
Download or read book Directionally Efficient Robust Estimators of Location Via Exponential Embedding written by Wei-Yin Loh and published by . This book was released on 1983 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: A general method is presented for constructing a location estimator which is asymptotically efficient at any two different location-scale families of symmetric distributions as well as at an appropriately defined class of distributions lying in between. The method works by embedding the two families in a comprehensive parametric model and identifying the estimator with the MLE(Maximum Likelihood Estimation). The case when the families are Normal and Double exponential is examined in detail. (Author).
Download or read book The Ubiquitous Role of F f in Efficiency Robust Estimation of Location written by Brian L. Joiner and published by . This book was released on 1980 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper is primarily expository in nature and focuses on the all pervasive importance of f'/f in efficient estimation of location, with primary emphasis on the role of f'/f in robust estimation. Connections between M estimators (maximum likelihood-like), R (rank) estimators and L estimators (linear combinations of order statistics) are discussed and an alternative heuristic explanation of f'/f is given showing why it is an intuitively reasonable quantity on which to base estimation. The asymptotic relative efficiency of each class of estimators is shown to be the square of a correlation coefficient related to f'/f and reasons are given as to why R estimators might often prove to have superior robustness properties relative to L and M estimators. (Author).
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.
Download or read book Huber Sense Robust M Estimation of a Scale Parameter with Application to the Exponential Distribution written by R. J. Serfling and published by . This book was released on 1975 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: A theory of robust M-estimation of a location parameter was developed by Huber (1964) and applied to estimation of the mean of a normal distribution. This theory is applicable to the problem of robust estimation of a scale parameter, since nonnegative data X having scale parameter theta may be transformed by y = log x into data Y having location parameter log theta. Equivalently, in the present article the authors reformulate Huber's location parameter results in the scale parameter content--that is transform the theorems instead of the data--and we the authors apply the results in connection with the problem of robust estimation of the parameter theta of the exponential distribution, 1 - exp ( -x/theta), x> 0. Whereas the maximum likelihood estimator of theta is the sample mean, the robust M-estimator, which is the solution of a minimax problem based on the asymptotic variance criterion, turns out to be a type of Winsorized mean. Numerical illustration is provided using a data set of Proschan (1963) consisting of the time intervals between successive failures of the air conditioning systems of a fleet of jet airplanes.
Download or read book Structural Modeling by Example written by Peter Cuttance and published by Cambridge University Press. This book was released on 1987 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive overview of the application of structural equation models in the social and behavioural sciences and in educational research.
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.
Download or read book Robust Minimum Distance Estimation of the Four Parameter Generalized Gamma Distribution written by Keith F. Shumaker and published by . This book was released on 1982 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: A robust estimation technique (MLDE) is developed which uses minimum distance estimation in conjunction with maximum likelihood estimation (MLE). This technique is then applied to the four-parameter generalized Gamma distribution to obtain location, scale, shape, and power parameter estimates. A Monte Carlo analysis is conducted on three members of the four-parameter generalized Gamma distribution with sample sizes of 12, 16, 20, and 24 for a total of twelve cases. For each of these twelve cases, one thousand samples are generated for the analysis. Initial estimates of the location, scale, shape, and power parameters are found using a maximum liklihood estimator. Minimum distance estimation using the Anderson-Darling statistic is then employed to obtain a new estimate of the location parameter. Finally, this new improved location parameter estimate is used to refine the scale, shape, and power parameter estimates through maximum likelihood estimation. The performance of the MLDE technique is determined through use of mean square error and relative efficiency measures. (Author).
