Comparing Conditional Distributions Under Measurement Errors of Known Variances - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Comparing Conditional Distributions Under Measurement Errors of Known Variances write by Stanford University. Department of Statistics. This book was released on 1968. Comparing Conditional Distributions Under Measurement Errors of Known Variances available in PDF, EPUB and Kindle.
Measurement Error Models
Measurement Error Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Measurement Error Models write by Wayne A. Fuller. This book was released on 2009-09-25. Measurement Error Models available in PDF, EPUB and Kindle. The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "The effort of Professor Fuller is commendable . . . [the book] provides a complete treatment of an important and frequently ignored topic. Those who work with measurement error models will find it valuable. It is the fundamental book on the subject, and statisticians will benefit from adding this book to their collection or to university or departmental libraries." -Biometrics "Given the large and diverse literature on measurement error/errors-in-variables problems, Fuller's book is most welcome. Anyone with an interest in the subject should certainly have this book." -Journal of the American Statistical Association "The author is to be commended for providing a complete presentation of a very important topic. Statisticians working with measurement error problems will benefit from adding this book to their collection." -Technometrics " . . . this book is a remarkable achievement and the product of impressive top-grade scholarly work." -Journal of Applied Econometrics Measurement Error Models offers coverage of estimation for situations where the model variables are observed subject to measurement error. Regression models are included with errors in the variables, latent variable models, and factor models. Results from several areas of application are discussed, including recent results for nonlinear models and for models with unequal variances. The estimation of true values for the fixed model, prediction of true values under the random model, model checks, and the analysis of residuals are addressed, and in addition, procedures are illustrated with data drawn from nearly twenty real data sets.
A Test for Comparing Multiple Misspecified Conditional Distributions
A Test for Comparing Multiple Misspecified Conditional Distributions - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Test for Comparing Multiple Misspecified Conditional Distributions write by Norman R. Swanson. This book was released on 2003. A Test for Comparing Multiple Misspecified Conditional Distributions available in PDF, EPUB and Kindle. This paper introduces a conditional Kolmogorov test, in the spirit of Andrews (1997), that allows for comparison of multiple misspecifed conditional distribution models, for the case of dependent observations. A conditional confidence interval version of the test is also discussed. Model accuracy is measured using a distributional analog of mean square error, in which the squared (approximation) error associated with a given model, say model i, is measured in terms of the average over U of E((Fi(u|Zt,0iĴ)-F0(u|Zt0o))squared), where U is a possibly unbounded set on the real line, Zt is the conditioning information set, Fi is the distribution function of a particular candidate model, and F0 is the true (unkown) distribution function. When comparing more than two models, a "benchmark" model is specified, and the test is constructed along the lines of the "reality check" of White (2000). Valid asymptotic critical values are obtained via a version of the block bootstrap which properly captures the effect of parameter estimation error. The results of a small Monte Carlo experiment indicate that the conditional confidence interval version of the test has reasonable finite sample properties even for samples with as few as 60 observations.
Contributions to Probability and Statistics
Contributions to Probability and Statistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Contributions to Probability and Statistics write by Leon J. Gleser. This book was released on 2012-12-06. Contributions to Probability and Statistics available in PDF, EPUB and Kindle. Published in honor of the sixty-fifth birthday of Professor Ingram Olkin of Stanford University. Part I contains a brief biography of Professor Olkin and an interview with him discussing his career and his research interests. Part II contains 32 technical papers written in Professor Olkin's honor by his collaborators, colleagues, and Ph.D. students. These original papers cover a wealth of topics in mathematical and applied statistics, including probability inequalities and characterizations, multivariate analysis and association, linear and nonlinear models, ranking and selection, experimental design, and approaches to statistical inference. The volume reflects the wide range of Professor Olkin's interests in and contributions to research in statistics, and provides an overview of new developments in these areas of research.
Measurement Error in Nonlinear Models
Measurement Error in Nonlinear Models - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Measurement Error in Nonlinear Models write by Sandra Nolte. This book was released on 2010. Measurement Error in Nonlinear Models available in PDF, EPUB and Kindle. This book analyzes how the choice of a particular disclosure limitation method, namely additive and multiplicative measurement error, affects the quality of the data and limits its usefulness for empirical research. Generally, a disclosure limitation method can be regarded as a data filter that transforms the true data generating process. This book focuses explicitly on the consequences of additive and multiplicative measurement error for the properties of nonlinear econometric estimators. It investigates the extent to which appropriate econometric techniques can yield consistent and unbiased estimates of the true data generating process in the case of disclosure limitation. Sandra Nolte received her PhD in Economics at the University of Konstanz, Germany in 2008 and is a postdoctoral researcher at the Financial Econometric Research Centre at the Warwick Business School, UK since 2009. Her research areas include microeconometrics and financial econometrics.
