Download or read book Relative Error Estimation and Efficient Estimation of Censored Linear Regression Model written by Yuanyuan Lin and published by . This book was released on 2011 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Applications of Locally Efficient Estimation to Censored Data Models written by Alan Edward Hubbard and published by . This book was released on 1998 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Efficient Estimation of Linear and Type I Censored Regression Models Under Conditional Quantile Restrictions written by Whitney K. Newey and published by . This book was released on 1987 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Asymptotically Efficient Estimation in Censored and Truncated Regression Models written by Stanford University. Department of Statistics and published by . This book was released on 1991 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Theory of Ridge Regression Estimation with Applications written by A. K. Md. Ehsanes Saleh and published by John Wiley & Sons. This book was released on 2019-01-08 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications Theory of Ridge Regression Estimation with Applications offers a comprehensive guide to the theory and methods of estimation. Ridge regression and LASSO are at the center of all penalty estimators in a range of standard models that are used in many applied statistical analyses. Written by noted experts in the field, the book contains a thorough introduction to penalty and shrinkage estimation and explores the role that ridge, LASSO, and logistic regression play in the computer intensive area of neural network and big data analysis. Designed to be accessible, the book presents detailed coverage of the basic terminology related to various models such as the location and simple linear models, normal and rank theory-based ridge, LASSO, preliminary test and Stein-type estimators. The authors also include problem sets to enhance learning. This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. This important resource: Offers theoretical coverage and computer-intensive applications of the procedures presented Contains solutions and alternate methods for prediction accuracy and selecting model procedures Presents the first book to focus on ridge regression and unifies past research with current methodology Uses R throughout the text and includes a companion website containing convenient data sets Written for graduate students, practitioners, and researchers in various fields of science, Theory of Ridge Regression Estimation with Applications is an authoritative guide to the theory and methodology of statistical estimation.
Download or read book Efficient Estimation of the Error Distribution Function in Heteroskedastic Nonparametric Regression with Missing Data written by Justin Chown and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric and Parametric Estimation with Truncated Regression Data written by Kwok-Leung Tsui and published by . This book was released on 1986 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Efficient Estimation of Linear Regression Models with Autocorrelated Errors written by Mathew James Morey and published by . This book was released on 1977 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Convenient Methods for Estimation of Linear Regression Models with MA 1 Errors written by Glenn M. MacDonald and published by Kingston, Ont. : Institute for Economic Research, Queen's University. This book was released on 1983 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Regression Models written by Richard Breen and published by SAGE. This book was released on 1996-01-09 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the regression models needed, where an outcome variable for a sample is not representative of the population from which a generalized result is sought.
Download or read book Adaptive Estimation in Time Series Regression Models written by Douglas Gardiner Steigerwald and published by . This book was released on 1989 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Semiparametric and Nonparametric Econometrics written by Aman Ullah and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last three decades much research in empirical and theoretical economics has been carried on under various assumptions. For example a parametric functional form of the regression model, the heteroskedasticity, and the autocorrelation is always as sumed, usually linear. Also, the errors are assumed to follow certain parametric distri butions, often normal. A disadvantage of parametric econometrics based on these assumptions is that it may not be robust to the slight data inconsistency with the particular parametric specification. Indeed any misspecification in the functional form may lead to erroneous conclusions. In view of these problems, recently there has been significant interest in 'the semiparametric/nonparametric approaches to econometrics. The semiparametric approach considers econometric models where one component has a parametric and the other, which is unknown, a nonparametric specification (Manski 1984 and Horowitz and Neumann 1987, among others). The purely non parametric approach, on the other hand, does not specify any component of the model a priori. The main ingredient of this approach is the data based estimation of the unknown joint density due to Rosenblatt (1956). Since then, especially in the last decade, a vast amount of literature has appeared on nonparametric estimation in statistics journals. However, this literature is mostly highly technical and this may partly be the reason why very little is known about it in econometrics, although see Bierens (1987) and Ullah (1988).
Download or read book Interval Censored Time to Event Data written by Ding-Geng (Din) Chen and published by CRC Press. This book was released on 2012-07-19 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interval-Censored Time-to-Event Data: Methods and Applications collects the most recent techniques, models, and computational tools for interval-censored time-to-event data. Top biostatisticians from academia, biopharmaceutical industries, and government agencies discuss how these advances are impacting clinical trials and biomedical research. Divided into three parts, the book begins with an overview of interval-censored data modeling, including nonparametric estimation, survival functions, regression analysis, multivariate data analysis, competing risks analysis, and other models for interval-censored data. The next part presents interval-censored methods for current status data, Bayesian semiparametric regression analysis of interval-censored data with monotone splines, Bayesian inferential models for interval-censored data, an estimator for identifying causal effect of treatment, and consistent variance estimation for interval-censored data. In the final part, the contributors use Monte Carlo simulation to assess biases in progression-free survival analysis as well as correct bias in interval-censored time-to-event applications. They also present adaptive decision making methods to optimize the rapid treatment of stroke, explore practical issues in using weighted logrank tests, and describe how to use two R packages. A practical guide for biomedical researchers, clinicians, biostatisticians, and graduate students in biostatistics, this volume covers the latest developments in the analysis and modeling of interval-censored time-to-event data. It shows how up-to-date statistical methods are used in biopharmaceutical and public health applications.
Download or read book Least Absolute Deviations Estimation for Censored and Truncated Regression Models written by James Leo Powell and published by . This book was released on 1981 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mean Squared Prediction Error Reduction with Instrumental Variables written by Antonis Michis and published by . This book was released on 2016 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt: The mean squared prediction error of the linear regression model is examined when estimation is performed with instrumental variables. It is shown that increasing the number of instruments in the estimation procedure, can reduce the mean squared prediction error of the model through more efficient estimation of the coefficient vector.
Download or read book Statistical Approaches to Analyze Censored Data with Multiple Detection Limits written by and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Censored data with multiple detection limits frequently arise in environmental health studies, where data are collected by different sampling and measured by different analytical procedures or when data are combined from multiple laboratories. The substitution method is the most common approach to handle censored environmental data; however, this method lacks a theoretical basis and results can differ substantially depending on the substituted value. Maximum likelihood estimation (MLE) with the Expectation-Maximization (EM) algorithm integrated method and the meta-analysis method were studied to determine if they can overcome these problems and to incorporate the sample collection process into the estimation of summary statistics. A new likelihood-based Z-score test and a resampling-based permutation test were introduced as well to compare the means of two censored data groups. They were expected to provide higher power and closer type I error rates to the nominal level than the usual two-sample t-test. The proposed methods were evaluated through a series of simulation studies and their performances were compared to those of the conventional methods. Simulation results consistently showed that the proposed MLE with the EM algorithm integrated method and the meta-analysis method provided the most accurate and efficient estimation of summary statistics for censored data with multiple detection limits. The simulation also suggested that the amount of censoring, magnitude of variance and disparities of sample size influenced the statistical estimation. The proposed Z-score test and permutation test were superior to the usual two-sample t-test. They provided better power and type I error rates in the simulation studies, and thus should be recommended for the comparison of means between two censored data groups. The proposed methods were successfully applied to two data sets collected by environmental health studies; the obtained summary statistics and significant test results were found to be similar to the published findings.
Download or read book Penalty Shrinkage and Pretest Strategies written by S. Ejaz Ahmed and published by Springer Science & Business Media. This book was released on 2013-12-11 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this book is to compare the statistical properties of penalty and non-penalty estimation strategies for some popular models. Specifically, it considers the full model, submodel, penalty, pretest and shrinkage estimation techniques for three regression models before presenting the asymptotic properties of the non-penalty estimators and their asymptotic distributional efficiency comparisons. Further, the risk properties of the non-penalty estimators and penalty estimators are explored through a Monte Carlo simulation study. Showcasing examples based on real datasets, the book will be useful for students and applied researchers in a host of applied fields. The book’s level of presentation and style make it accessible to a broad audience. It offers clear, succinct expositions of each estimation strategy. More importantly, it clearly describes how to use each estimation strategy for the problem at hand. The book is largely self-contained, as are the individual chapters, so that anyone interested in a particular topic or area of application may read only that specific chapter. The book is specially designed for graduate students who want to understand the foundations and concepts underlying penalty and non-penalty estimation and its applications. It is well-suited as a textbook for senior undergraduate and graduate courses surveying penalty and non-penalty estimation strategies, and can also be used as a reference book for a host of related subjects, including courses on meta-analysis. Professional statisticians will find this book to be a valuable reference work, since nearly all chapters are self-contained.
