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Author: Albert Satorra Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
We consider the application of normal theory methods to the estimation and testing of a general type of multivariate regression models with errors-in-variables, in the case where various data sets are merged into a single analysis and the observable variables deviate possibly from normality. The various samples to be merged can differ on the set of observable variables available. We show that there is a convenient way to parameterize the model so that, despite the possible non-normality of the data, normal-theory methods yield correct inferences for the parameters of interest and for the goodness-of-fit test. The theory described encompasses both the functional and structural model cases, and can be implemented using standard software for structural equations models, such as LISREL, EQS, LISCOMP, among others. An illustration with Monte Carlo data is presented.
Author: Albert Satorra Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
We consider the application of normal theory methods to the estimation and testing of a general type of multivariate regression models with errors-in-variables, in the case where various data sets are merged into a single analysis and the observable variables deviate possibly from normality. The various samples to be merged can differ on the set of observable variables available. We show that there is a convenient way to parameterize the model so that, despite the possible non-normality of the data, normal-theory methods yield correct inferences for the parameters of interest and for the goodness-of-fit test. The theory described encompasses both the functional and structural model cases, and can be implemented using standard software for structural equations models, such as LISREL, EQS, LISCOMP, among others. An illustration with Monte Carlo data is presented.
Author: Rüdiger Klar Publisher: Springer Science & Business Media ISBN: 3642590519 Category : Business & Economics Languages : en Pages : 693
Book Description
Large collections of data and information necessitate adequate methods for their analysis. The book presents such methods, proposes and discusses recent approaches and implementations and describes a series of practical applications.
Author: Gregory C. Reinsel Publisher: Springer Nature ISBN: 1071627937 Category : Mathematics Languages : en Pages : 420
Book Description
This book provides an account of multivariate reduced-rank regression, a tool of multivariate analysis that enjoys a broad array of applications. In addition to a historical review of the topic, its connection to other widely used statistical methods, such as multivariate analysis of variance (MANOVA), discriminant analysis, principal components, canonical correlation analysis, and errors-in-variables models, is also discussed. This new edition incorporates Big Data methodology and its applications, as well as high-dimensional reduced-rank regression, generalized reduced-rank regression with complex data, and sparse and low-rank regression methods. Each chapter contains developments of basic theoretical results, as well as details on computational procedures, illustrated with numerical examples drawn from disciplines such as biochemistry, genetics, marketing, and finance. This book is designed for advanced students, practitioners, and researchers, who may deal with moderate and high-dimensional multivariate data. Because regression is one of the most popular statistical methods, the multivariate regression analysis tools described should provide a natural way of looking at large (both cross-sectional and chronological) data sets. This book can be assigned in seminar-type courses taken by advanced graduate students in statistics, machine learning, econometrics, business, and engineering.
Author: Age K. Smilde Publisher: John Wiley & Sons ISBN: 1119600995 Category : Science Languages : en Pages : 354
Book Description
Multiblock Data Fusion in Statistics and Machine Learning Explore the advantages and shortcomings of various forms of multiblock analysis, and the relationships between them, with this expert guide Arising out of fusion problems that exist in a variety of fields in the natural and life sciences, the methods available to fuse multiple data sets have expanded dramatically in recent years. Older methods, rooted in psychometrics and chemometrics, also exist. Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences is a detailed overview of all relevant multiblock data analysis methods for fusing multiple data sets. It focuses on methods based on components and latent variables, including both well-known and lesser-known methods with potential applications in different types of problems. Many of the included methods are illustrated by practical examples and are accompanied by a freely available R-package. The distinguished authors have created an accessible and useful guide to help readers fuse data, develop new data fusion models, discover how the involved algorithms and models work, and understand the advantages and shortcomings of various approaches. This book includes: A thorough introduction to the different options available for the fusion of multiple data sets, including methods originating in psychometrics and chemometrics Practical discussions of well-known and lesser-known methods with applications in a wide variety of data problems Included, functional R-code for the application of many of the discussed methods Perfect for graduate students studying data analysis in the context of the natural and life sciences, including bioinformatics, sensometrics, and chemometrics, Multiblock Data Fusion in Statistics and Machine Learning: Applications in the Natural and Life Sciences is also an indispensable resource for developers and users of the results of multiblock methods.
Author: Advait Jayant Publisher: ISBN: Category : Languages : en Pages :
Book Description
Apply Multivariate Linear Regression (Multiple-Linear Regression) in this course within the Data Science and Machine Learning Series. Follow along with machine learning expert Advait Jayant through a combination of lecture and hands-on to practice using this powerful statistical linear model. Also here are all of Advait Jayant's highly-rated videos on O'Reilly, including the full Data Science and Machine Learning Series . The following six topics will be covered in this Data Science and Machine Learning course: Introducing Multivariate Linear Regression (Multiple-Linear Regression) . Be able to explain multivariate linear regression and its use cases in this first topic in the Data Science and Machine Learning Series. Regression analysis is a powerful statistical method that allows us to examine the relationship between two or more variables of interest. A Dependent Variable is the main factor that we are trying to understand and predict. An Independent Variable is a factor that we want to hypothesize has an impact on our dependent variable. Practice the steps of defining dependent and independent variables, and establishing a comprehensive data set. Practicing Multivariate Linear Regression using the Boston Housing Prices Dataset . Practice multivariate linear regression using the Boston Housing Prices Dataset to predict housing prices in this second topic in the Data Science and Machine Learning Series. Follow along with Advait and use the Python libraries of pandas, numpy, seaborn, and matplotlib to work with Multivariate Linear Regression. Underfitting and Overfitting in Machine Learning and while using Multivariate Linear Regression . Be able to explain underfitting and overfitting in machine learning and while using multivariate linear regression in this third topic in the Data Science and Machine Learning Series. Applying the Mini Batch and Stochastic Gradient Descent Algorithms . Apply the Mini Batch and Stochastic Gradient Descent Algorithms in this fourth topic in the Data Science and Machine Learning Series. Using Maximum Likelihood Estimation . Use maximum likelihood estimation in this fifth topic in the Data Science and Machine Learning Series. Follow along with Advait and also practice the least squares loss function. K-Fold Cross Validation . Apply K-fold cross validation in this sixth topic in the Data Science and Machine Learning Series. Follow along with Advait and use this algorithm to create training and testing data sets.
Author: Ronny Richardson Publisher: Business Expert Press ISBN: 1631570609 Category : Business & Economics Languages : en Pages : 379
Book Description
This second edition of Business Applications of Multiple Regression describes the use of the statistical procedure called multiple regression in business situations, including forecasting and understanding the relationships between variables. The book assumes a basic understanding of statistics but reviews correlation analysis and simple regression to prepare the reader to understand and use multiple regression. The techniques described in the book are illustrated using both Microsoft Excel and a professional statistical program. Along the way, several real-world data sets are analyzed in detail to better prepare the reader for working with actual data in a business environment. This book will be a useful guide to managers at all levels who need to understand and make decisions based on data analysis performed using multiple regression. It also provides the beginning analyst with the detailed understanding required to use multiple regression to analyze data sets.
Author: National Academies of Sciences, Engineering, and Medicine Publisher: National Academies Press ISBN: 0309465370 Category : Social Science Languages : en Pages : 195
Book Description
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.
Author: Li-Chun Zhang Publisher: CRC Press ISBN: 1351646729 Category : Mathematics Languages : en Pages : 215
Book Description
The advent of "Big Data" has brought with it a rapid diversification of data sources, requiring analysis that accounts for the fact that these data have often been generated and recorded for different reasons. Data integration involves combining data residing in different sources to enable statistical inference, or to generate new statistical data for purposes that cannot be served by each source on its own. This can yield significant gains for scientific as well as commercial investigations. However, valid analysis of such data should allow for the additional uncertainty due to entity ambiguity, whenever it is not possible to state with certainty that the integrated source is the target population of interest. Analysis of Integrated Data aims to provide a solid theoretical basis for this statistical analysis in three generic settings of entity ambiguity: statistical analysis of linked datasets that may contain linkage errors; datasets created by a data fusion process, where joint statistical information is simulated using the information in marginal data from non-overlapping sources; and estimation of target population size when target units are either partially or erroneously covered in each source. Covers a range of topics under an overarching perspective of data integration. Focuses on statistical uncertainty and inference issues arising from entity ambiguity. Features state of the art methods for analysis of integrated data. Identifies the important themes that will define future research and teaching in the statistical analysis of integrated data. Analysis of Integrated Data is aimed primarily at researchers and methodologists interested in statistical methods for data from multiple sources, with a focus on data analysts in the social sciences, and in the public and private sectors.
Author: Albert Satorra
Author: Albert Satorra
Author: Rüdiger Klar
Author: Gregory C. Reinsel
Author: Age K. Smilde
Author: Advait Jayant
Author: Ronny Richardson
Author: Norman Cliff
Author: National Academies of Sciences, Engineering, and Medicine
Author: 
Author: Li-Chun Zhang