Download or read book A Look at Various Estimators in Logistic Models in the Presence of Missing Values written by Winston Chow and published by . This book was released on 1979 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Logistic Regression with Missing Values in the Covariates written by Werner Vach and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many areas of science a basic task is to assess the influence of several factors on a quantity of interest. If this quantity is binary logistic, regression models provide a powerful tool for this purpose. This monograph presents an account of the use of logistic regression in the case where missing values in the variables prevent the use of standard techniques. Such situations occur frequently across a wide range of statistical applications. The emphasis of this book is on methods related to the classical maximum likelihood principle. The author reviews the essentials of logistic regression and discusses the variety of mechanisms which might cause missing values while the rest of the book covers the methods which may be used to deal with missing values and their effectiveness. Researchers across a range of disciplines and graduate students in statistics and biostatistics will find this a readable account of this.
Download or read book Estimation of Polytomous Logistic Regression Coefficients in the Presence of Missing Data written by William Augustus Reynolds and published by . This book was released on 1997 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Look at various estimators in logistic models in the presence written by Winston K. Chow and published by . This book was released on 1979 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Missing Data written by Paul D. Allison and published by SAGE Publications. This book was released on 2001-08-13 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to be included in the analysis. Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has been relying on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data.
Download or read book Proceedings of the Biometrics Section written by American Statistical Association. Biometrics Section and published by . This book was released on 1999 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Flexible Imputation of Missing Data Second Edition written by Stef van Buuren and published by CRC Press. This book was released on 2018-07-17 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.
Download or read book Statistical Analysis with Missing Data written by Roderick J. A. Little and published by John Wiley & Sons. This book was released on 2019-03-21 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: An up-to-date, comprehensive treatment of a classic text on missing data in statistics The topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism, and then they apply the theory to a wide range of important missing data problems. Statistical Analysis with Missing Data, Third Edition starts by introducing readers to the subject and approaches toward solving it. It looks at the patterns and mechanisms that create the missing data, as well as a taxonomy of missing data. It then goes on to examine missing data in experiments, before discussing complete-case and available-case analysis, including weighting methods. The new edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics. An updated “classic” written by renowned authorities on the subject Features over 150 exercises (including many new ones) Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methods Revises previous topics based on past student feedback and class experience Contains an updated and expanded bibliography The authors were awarded The Karl Pearson Prize in 2017 by the International Statistical Institute, for a research contribution that has had profound influence on statistical theory, methodology or applications. Their work "has been no less than defining and transforming." (ISI) Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry.
Download or read book The Rand Paper Series written by and published by . This book was released on with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Analysis and Statistical Inference written by Siegfried Schach and published by . This book was released on 1992 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Special Cases in Estimating Multiple Missing Values in Linear Models written by Aaron Marshall and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Missing a single observation, or more, very commonly occurs in observational and designed studies. Estimating a single missing observation and analysis of these types of data is found in literature (Montgomery, 2020). But the estimation and analysis of data becomes more complicated when the study dataset becomes imbalanced due to multiple missing. Though the case of two missing values is the simplest case of multiple missing values, the analytic estimation and analysis will not be as straight forward as in the one missing value case, because two missing can occur in various ways. This thesis will be exploring mainly the idea of multiple missing values in two-way classified data. In Azadeh et al (2008) it is stated that missing values are incredibly common. In order to continue working with the dataset, those missing values must be estimated. There are two different types of missing values: missing at random (MAR) and missing not at random (MNAR) (Efromovich, 2018). This thesis will be focusing specifically on values that are MAR. Values that are MAR are relatively convenient to deal with because each value in the data set has the same probability of being missing. Also, in Gomer and Ke-Hai (2021) it is stated that the cause of missingness being unobserved makes MNAR valuables difficult to deal with, but it is found in literature (Efromovich, 2018). With a MAR in datasets, the missing value can be in any treatment and in any block. With two missing values, each missing value can appear in any treatment and any block which gives three separate cases that will be discussed later. This thesis will also be looking at multiple missing values instead of just one. Estimating for one missing value has been researched extensively, but estimating more than one still has plenty of room for exploration. In Tang & Ishwaran (2017) and Montgomery (2020), the authors mentioned that their method for estimating missing values is iterative because the estimation of one missing value takes into account the value of the other missing value. This means for the first estimation one of the missing values is given a random number and that is used to compute the second missing value. That second missing value is then plugged in to find the estimation for the first missing value. This process is done over and over until the estimates have stabilized. The method proposed is an analytic method which will provide closed form solution for missing values and will not require the iterative process. Having the analytic solution will allow us to explore the inferential, statistical properties of the estimators"--Provided by author.
Download or read book Proceedings of the Business and Economic Statistics Section written by American Statistical Association. Business and Economic Statistics Section and published by . This book was released on 1979 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Journal of the American Statistical Association written by and published by . This book was released on 2005 with total page 1526 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Practical Guide to Logistic Regression written by Joseph M. Hilbe and published by CRC Press. This book was released on 2016-04-05 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. This powerful methodology can be used to analyze data from various fields, including medical and health outcomes research, business analytics and data science, ecology, fishe
Download or read book Multiple Imputation for Nonresponse in Surveys written by Donald B. Rubin and published by John Wiley & Sons. This book was released on 2009-09-25 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demonstrates how nonresponse in sample surveys and censuses can be handled by replacing each missing value with two or more multiple imputations. Clearly illustrates the advantages of modern computing to such handle surveys, and demonstrates the benefit of this statistical technique for researchers who must analyze them. Also presents the background for Bayesian and frequentist theory. After establishing that only standard complete-data methods are needed to analyze a multiply-imputed set, the text evaluates procedures in general circumstances, outlining specific procedures for creating imputations in both the ignorable and nonignorable cases. Examples and exercises reinforce ideas, and the interplay of Bayesian and frequentist ideas presents a unified picture of modern statistics.
Download or read book Analysis of Incomplete Multivariate Data written by J.L. Schafer and published by CRC Press. This book was released on 1997-08-01 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last two decades have seen enormous developments in statistical methods for incomplete data. The EM algorithm and its extensions, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data problems. Yet, in practical terms, those developments have had surprisingly little impact on the way most data analysts handle missing values on a routine basis. Analysis of Incomplete Multivariate Data helps bridge the gap between theory and practice, making these missing-data tools accessible to a broad audience. It presents a unified, Bayesian approach to the analysis of incomplete multivariate data, covering datasets in which the variables are continuous, categorical, or both. The focus is applied, where necessary, to help readers thoroughly understand the statistical properties of those methods, and the behavior of the accompanying algorithms. All techniques are illustrated with real data examples, with extended discussion and practical advice. All of the algorithms described in this book have been implemented by the author for general use in the statistical languages S and S Plus. The software is available free of charge on the Internet.
Download or read book Feature Engineering and Selection written by Max Kuhn and published by CRC Press. This book was released on 2019-07-25 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
