Asymptotic Properties of Log Odds Ratio Regression Estimators with Sparse Strata and Covariate Measurement Error PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Asymptotic Properties of Log Odds Ratio Regression Estimators with Sparse Strata and Covariate Measurement Error PDF full book. Access full book title Asymptotic Properties of Log Odds Ratio Regression Estimators with Sparse Strata and Covariate Measurement Error by Andrew Benjamin Forbes. Download full books in PDF and EPUB format.
Author: Andrew Benjamin Forbes
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 508
Get Book Here
Book Description
Author: Andrew Benjamin Forbes
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 508
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category : Electronic journals
Languages : en
Pages : 1638
Get Book Here
Book Description
A scientific and educational journal not only for professional statisticians but also for economists, business executives, research directors, government officials, university professors, and others who are seriously interested in the application of statistical methods to practical problems, in the development of more useful methods, and in the improvement of basic statistical data.
Author: Institute of Mathematical Statistics
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 646
Get Book Here
Book Description
Author: Hira Koul
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
This article derives the consistency and asymptotic distribution of the bias corrected least squares estimators (LSEs) of the regression parameters in linear regression models when covariates have measurement error (ME) and errors and covariates form mutually independent long memory moving average processes. In the structural ME linear regression model, the nature of the asymptotic distribution of suitably standardized bias corrected LSEs depends on the range of the values of where ,, and are the LM parameters of the covariate, ME and regression error processes respectively. This limiting distribution is Gaussian when and non-Gaussian in the case . In the former case some consistent estimators of the asymptotic variances of these estimators and a log()-consistent estimator of an underlying LM parameter are also provided. They are useful in the construction of the large sample confidence intervals for regression parameters. The article also discusses the asymptotic distribution of these estimators in some functional ME linear regression models, where the unobservable covariate is non-random. In these models, the limiting distribution of the bias corrected LSEs is always a Gaussian distribution determined by the range of the values of )-)
Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 796
Get Book Here
Book Description
Author:
Publisher:
ISBN:
Category : Dissertation abstracts
Languages : en
Pages : 760
Get Book Here
Book Description
Author: Charles F. Manski
Publisher: Chapman and Hall/CRC
ISBN:
Category : Business & Economics
Languages : en
Pages : 186
Get Book Here
Book Description
Presents familiar elements of estimation theory from an analog perspective discussing recent developments in the theory of analog estimation and new results that offer flexibility in empirical research. Annotation copyrighted by Book News, Inc., Portland, OR
Author: Sas Institute
Publisher:
ISBN: 9781607646341
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
The GLIMMIX procedure fits and analyzes generalized linear mixed models (GLMM), models with random effects for data that can be nonnormally distributed. This title is also available online.
Author: Alan Agresti
Publisher: John Wiley & Sons
ISBN: 1119405270
Category : Mathematics
Languages : en
Pages : 400
Get Book Here
Book Description
A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.
Author: Frank E. Harrell
Publisher: Springer Science & Business Media
ISBN: 147573462X
Category : Mathematics
Languages : en
Pages : 583
Get Book Here
Book Description
Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".
