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Asymptotically Optimal Estimation In The Semiparametric Heteroscedastic Linear Model
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Book Synopsis Asymptotically Optimal Estimation in the Semiparametric Heteroscedastic Linear Model by : Beong-Soo So
Download or read book Asymptotically Optimal Estimation in the Semiparametric Heteroscedastic Linear Model written by Beong-Soo So and published by . This book was released on 1989 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Estimation in Semiparametric Models by : Johann Pfanzagl
Download or read book Estimation in Semiparametric Models written by Johann Pfanzagl and published by . This book was released on 1990-04-06 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotically Optimal Estimation in Misspecified Time Series Models by : Rainer Dahlhaus
Download or read book Asymptotically Optimal Estimation in Misspecified Time Series Models written by Rainer Dahlhaus and published by . This book was released on 1996 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Introduction to Optimal Estimation by : Edward W. Kamen
Download or read book Introduction to Optimal Estimation written by Edward W. Kamen and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: A handy technical introduction to the latest theories and techniques of optimal estimation. It provides readers with extensive coverage of Wiener and Kalman filtering along with a development of least squares estimation, maximum likelihood and maximum a posteriori estimation based on discrete-time measurements. Much emphasis is placed on how they interrelate and fit together to form a systematic development of optimal estimation. Examples and exercises refer to MATLAB software.
Book Synopsis Big and Complex Data Analysis by : S. Ejaz Ahmed
Download or read book Big and Complex Data Analysis written by S. Ejaz Ahmed and published by Springer. This book was released on 2017-03-21 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.
Book Synopsis Bulletin - Institute of Mathematical Statistics by : Institute of Mathematical Statistics
Download or read book Bulletin - Institute of Mathematical Statistics written by Institute of Mathematical Statistics and published by . This book was released on 1993 with total page 674 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Estimation in Semiparametric Models by : Zaiqian Shen
Download or read book Robust Estimation in Semiparametric Models written by Zaiqian Shen and published by . This book was released on 1992 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Semiparametric Inference in a Partial Linear Model by : Pengliang Zhao
Download or read book Semiparametric Inference in a Partial Linear Model written by Pengliang Zhao and published by . This book was released on 1992 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Estimation in Heteroscedastic Linear Models by : Raymond J. Carroll
Download or read book Robust Estimation in Heteroscedastic Linear Models written by Raymond J. Carroll and published by . This book was released on 1981 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a heteroscedastic linear model in which the variances are a parametric function of the mean responses and a parameter theta. We propose robust estimates for the regression parameter beta and show that, as long as a reasonable starting estimate of theta is available, our estimates of beta are asymptotically equivalent to the natural estimate obtained with known variances. A particular method for estimating theta is proposed and shown by Monte-Carlo to work quite well, especially in power and exponential models for the variances. We also briefly discuss a 'feedback' estimate of beta. (Author).
Book Synopsis Optimal and Robust Estimation by : Frank L. Lewis
Download or read book Optimal and Robust Estimation written by Frank L. Lewis and published by CRC Press. This book was released on 2017-12-19 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: More than a decade ago, world-renowned control systems authority Frank L. Lewis introduced what would become a standard textbook on estimation, under the title Optimal Estimation, used in top universities throughout the world. The time has come for a new edition of this classic text, and Lewis enlisted the aid of two accomplished experts to bring the book completely up to date with the estimation methods driving today's high-performance systems. A Classic Revisited 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 the robust Kalman filter, H-infinity filtering, and H-infinity filtering of discrete-time systems. Modern Tools for Tomorrow's Engineers This text overflows with examples that highlight practical applications of the theory and concepts. Design algorithms appear conveniently in tables, allowing students quick reference, easy implementation into software, and intuitive comparisons for selecting the best algorithm for a given application. In addition, downloadable MATLABĀ® code allows students to gain hands-on experience with industry-standard software tools for a wide variety of applications. This cutting-edge and highly interactive text makes teaching, and learning, estimation methods easier and more modern than ever.
Book Synopsis Asymptotic Efficient Robust Estimates in Some Semiparametric Models by : Colin Ou Wu
Download or read book Asymptotic Efficient Robust Estimates in Some Semiparametric Models written by Colin Ou Wu and published by . This book was released on 1990 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Selected Proceedings of the Symposium on Inference for Stochastic Processes by : Ishwar V. Basawa
Download or read book Selected Proceedings of the Symposium on Inference for Stochastic Processes written by Ishwar V. Basawa and published by IMS. This book was released on 2001 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Semiparametric Quasilikelihood and Variance Function Estimation in Measurement Error Models by : Jungsywan H. Sepanski
Download or read book Semiparametric Quasilikelihood and Variance Function Estimation in Measurement Error Models written by Jungsywan H. Sepanski and published by . This book was released on 1991 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Efficient and Adaptive Estimation for Semiparametric Models by : Peter J. Bickel
Download or read book Efficient and Adaptive Estimation for Semiparametric Models written by Peter J. Bickel and published by Springer. This book was released on 1998-06-01 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with estimation in situations in which there is believed to be enough information to model parametrically some, but not all of the features of a data set. Such models have arisen in a wide context in recent years, and involve new nonlinear estimation procedures. Statistical models of this type are directly applicable to fields such as economics, epidemiology, and astronomy.
Book Synopsis Asymptotically Optimal Selection of a Piecewise Polynomial Estimator of a Regression Function by : Keh-Wei Chen
Download or read book Asymptotically Optimal Selection of a Piecewise Polynomial Estimator of a Regression Function written by Keh-Wei Chen and published by . This book was released on 1983 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Dissertation Abstracts International by :
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2006 with total page 846 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Heteroscedastic Linear Model Estimation Based on Ranks by : Themba Louis Nyirenda
Download or read book Heteroscedastic Linear Model Estimation Based on Ranks written by Themba Louis Nyirenda and published by . This book was released on 2009 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: For standard estimators, data that are heteroscedastic in nature contain outlying values which can lead to poor performance. In this study, we present a robust interactive method for estimating the location and scale parameters in the general linear model, using a rank based method. It is assumed that the errors are symmetric about 0 and the variance function model is nonlinear with respect to the scale coefficients and the design. The function is known up to a scale constant. We propose taking the logarithm of the absolute values of the variance function to linearize it. The rank estimation of the scale coefficients amounts to regressing logs of absolute residuals from an initial rank based fit on to the design. The resulting scale coefficient estimates are used to form scale constants in a weighted signed-rank method. Thus, iterating between these two rank based methods leads to the desired estimates that are obtained from linear model fits for both types of coefficients. For the heteroscedastic linear model under consideration, this study has made the following contributions: (1) the asymptotic normality results that are established here show that the estimators are both consistent and highly efficient; (2) in each estimation problem, the Iterated Reweighted Least Squares (IRWLS) formulation for rank methods of Sievers and Abebe (2004) is employed with the other parameter substituted by their corresponding estimates from an appropriate iteration; (3) the high efficiency and good robustness qualities of the proposed method are confirmed by simulation trials that were conducted in two-sample problem, several groups and general linear models; (4) the inlier issue that is a consequence of employing the log transformation is also investigated and shown to be well curtailed by the proposed method and (5) finally, the method is shown to outperform other methods when applied to real life data from a Psychiatric Clinical Trial containing two treatments, one covariate, and one confounding variable. Thus, for samples larger than 20, the proposed method is highly robust and efficient under non-normal distributions.