Combining Information

Download or read book Combining Information written by and published by National Academies. This book was released on 1992-01-01 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Meta-analysis and Combining Information in Genetics and Genomics written by Rudy Guerra and published by CRC Press. This book was released on 2016-04-19 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Novel Techniques for Analyzing and Combining Data from Modern Biological StudiesBroadens the Traditional Definition of Meta-AnalysisWith the diversity of data and meta-data now available, there is increased interest in analyzing multiple studies beyond statistical approaches of formal meta-analysis. Covering an extensive range of quantitative infor

Download or read book Improved Operational Testing and Evaluation and Methods of Combining Test Information for the Stryker Family of Vehicles and Related Army Systems written by National Research Council and published by National Academies Press. This book was released on 2004-01-29 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: The U.S. Army Test and Evaluation Command (ATEC) is responsible for the operational testing and evaluation of Army systems in development. ATEC requested that the National Research Council form the Panel on Operational Test Design and Evaluation of the Interim Armored Vehicle (Stryker). The charge to this panel was to explore three issues concerning the IOT plans for the Stryker/SBCT. First, the panel was asked to examine the measures selected to assess the performance and effectiveness of the Stryker/SBCT in comparison both to requirements and to the baseline system. Second, the panel was asked to review the test design for the Stryker/SBCT initial operational test to see whether it is consistent with best practices. Third, the panel was asked to identify the advantages and disadvantages of techniques for combining operational test data with data from other sources and types of use. In a previous report (appended to the current report) the panel presented findings, conclusions, and recommendations pertaining to the first two issues: measures of performance and effectiveness, and test design. In the current report, the panel discusses techniques for combining information.

Download or read book Federal Statistics, Multiple Data Sources, and Privacy Protection written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2018-01-27 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: The environment for obtaining information and providing statistical data for policy makers and the public has changed significantly in the past decade, raising questions about the fundamental survey paradigm that underlies federal statistics. New data sources provide opportunities to develop a new paradigm that can improve timeliness, geographic or subpopulation detail, and statistical efficiency. It also has the potential to reduce the costs of producing federal statistics. The panel's first report described federal statistical agencies' current paradigm, which relies heavily on sample surveys for producing national statistics, and challenges agencies are facing; the legal frameworks and mechanisms for protecting the privacy and confidentiality of statistical data and for providing researchers access to data, and challenges to those frameworks and mechanisms; and statistical agencies access to alternative sources of data. The panel recommended a new approach for federal statistical programs that would combine diverse data sources from government and private sector sources and the creation of a new entity that would provide the foundational elements needed for this new approach, including legal authority to access data and protect privacy. This second of the panel's two reports builds on the analysis, conclusions, and recommendations in the first one. This report assesses alternative methods for implementing a new approach that would combine diverse data sources from government and private sector sources, including describing statistical models for combining data from multiple sources; examining statistical and computer science approaches that foster privacy protections; evaluating frameworks for assessing the quality and utility of alternative data sources; and various models for implementing the recommended new entity. Together, the two reports offer ideas and recommendations to help federal statistical agencies examine and evaluate data from alternative sources and then combine them as appropriate to provide the country with more timely, actionable, and useful information for policy makers, businesses, and individuals.

Download or read book Improved Operational Testing and Evaluation and Methods of Combining Test Information for the Stryker Family of Vehicles and Related Army Systems written by National Research Council and published by National Academies Press. This book was released on 2003-12-29 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: The U.S. Army Test and Evaluation Command (ATEC) is responsible for the operational testing and evaluation of Army systems in development. ATEC requested that the National Research Council form the Panel on Operational Test Design and Evaluation of the Interim Armored Vehicle (Stryker). The charge to this panel was to explore three issues concerning the IOT plans for the Stryker/SBCT. First, the panel was asked to examine the measures selected to assess the performance and effectiveness of the Stryker/SBCT in comparison both to requirements and to the baseline system. Second, the panel was asked to review the test design for the Stryker/SBCT initial operational test to see whether it is consistent with best practices. Third, the panel was asked to identify the advantages and disadvantages of techniques for combining operational test data with data from other sources and types of use. In a previous report (appended to the current report) the panel presented findings, conclusions, and recommendations pertaining to the first two issues: measures of performance and effectiveness, and test design. In the current report, the panel discusses techniques for combining information.

Download or read book Innovations in Federal Statistics written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2017-04-21 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Federal government statistics provide critical information to the country and serve a key role in a democracy. For decades, sample surveys with instruments carefully designed for particular data needs have been one of the primary methods for collecting data for federal statistics. However, the costs of conducting such surveys have been increasing while response rates have been declining, and many surveys are not able to fulfill growing demands for more timely information and for more detailed information at state and local levels. Innovations in Federal Statistics examines the opportunities and risks of using government administrative and private sector data sources to foster a paradigm shift in federal statistical programs that would combine diverse data sources in a secure manner to enhance federal statistics. This first publication of a two-part series discusses the challenges faced by the federal statistical system and the foundational elements needed for a new paradigm.

Download or read book Multiple Imputation of Missing Data in Practice written by Yulei He and published by CRC Press. This book was released on 2021-11-20 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis. Over the past 40 years or so, multiple imputation has gone through rapid development in both theories and applications. It is nowadays the most versatile, popular, and effective missing-data strategy that is used by researchers and practitioners across different fields. There is a strong need to better understand and learn about multiple imputation in the research and practical community. Accessible to a broad audience, this book explains statistical concepts of missing data problems and the associated terminology. It focuses on how to address missing data problems using multiple imputation. It describes the basic theory behind multiple imputation and many commonly-used models and methods. These ideas are illustrated by examples from a wide variety of missing data problems. Real data from studies with different designs and features (e.g., cross-sectional data, longitudinal data, complex surveys, survival data, studies subject to measurement error, etc.) are used to demonstrate the methods. In order for readers not only to know how to use the methods, but understand why multiple imputation works and how to choose appropriate methods, simulation studies are used to assess the performance of the multiple imputation methods. Example datasets and sample programming code are either included in the book or available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book). Key Features Provides an overview of statistical concepts that are useful for better understanding missing data problems and multiple imputation analysis Provides a detailed discussion on multiple imputation models and methods targeted to different types of missing data problems (e.g., univariate and multivariate missing data problems, missing data in survival analysis, longitudinal data, complex surveys, etc.) Explores measurement error problems with multiple imputation Discusses analysis strategies for multiple imputation diagnostics Discusses data production issues when the goal of multiple imputation is to release datasets for public use, as done by organizations that process and manage large-scale surveys with nonresponse problems For some examples, illustrative datasets and sample programming code from popular statistical packages (e.g., SAS, R, WinBUGS) are included in the book. For others, they are available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book)