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| Author | : Christos H. Skiadas |
| Publisher | : John Wiley & Sons |
| Total Pages | : 286 |
| Release | : 2019-05-21 |
| Genre | : Mathematics |
| ISBN | : 1786303825 |
Download Data Analysis and Applications 1 Book in PDF, ePub and Kindle
This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models, and techniques, along with appropriate applications. Volume 1 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into three parts: Part 1 presents clustering and regression cases; Part 2 examines grouping and decomposition, GARCH and threshold models, structural equations, and SME modeling; and Part 3 presents symbolic data analysis, time series and multiple choice models, modeling in demography, and data mining.
| Author | : Xue-Kun Song |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 356 |
| Release | : 2007-07-27 |
| Genre | : Mathematics |
| ISBN | : 0387713921 |
Download Correlated Data Analysis: Modeling, Analytics, and Applications Book in PDF, ePub and Kindle
This book covers recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to cover a broader range of data types than the traditional generalized linear models. The reader is provided with a systematic treatment for the topic of estimating functions, and both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to the discussions on marginal models and mixed-effects models, this book covers new topics on joint regression analysis based on Gaussian copulas.
| Author | : Christos H. Skiadas |
| Publisher | : John Wiley & Sons |
| Total Pages | : 252 |
| Release | : 2019-05-21 |
| Genre | : Mathematics |
| ISBN | : 1786304473 |
Download Data Analysis and Applications 2 Book in PDF, ePub and Kindle
This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications. Volume 2 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into four parts: Part 1 examines (in)dependence relationships, innovation in the Nordic countries, dentistry journals, dependence among growth rates of GDP of V4 countries, emissions mitigation, and five-star ratings; Part 2 investigates access to credit for SMEs, gender-based impacts given Southern Europe’s economic crisis, and labor market transition probabilities; Part 3 looks at recruitment at university job-placement offices and the Program for International Student Assessment; and Part 4 examines discriminants, PageRank, and the political spectrum of Germany.
| Author | : Xian Liu |
| Publisher | : Elsevier |
| Total Pages | : 530 |
| Release | : 2015-09-11 |
| Genre | : Mathematics |
| ISBN | : 0128014822 |
Download Methods and Applications of Longitudinal Data Analysis Book in PDF, ePub and Kindle
Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include: descriptive methods for delineating trends over time linear mixed regression models with both fixed and random effects covariance pattern models on correlated errors generalized estimating equations nonlinear regression models for categorical repeated measurements techniques for analyzing longitudinal data with non-ignorable missing observations Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data. Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists. From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.
| Author | : Vera Pawlowsky-Glahn |
| Publisher | : John Wiley & Sons |
| Total Pages | : 401 |
| Release | : 2011-09-19 |
| Genre | : Mathematics |
| ISBN | : 0470711353 |
Download Compositional Data Analysis Book in PDF, ePub and Kindle
It is difficult to imagine that the statistical analysis of compositional data has been a major issue of concern for more than 100 years. It is even more difficult to realize that so many statisticians and users of statistics are unaware of the particular problems affecting compositional data, as well as their solutions. The issue of ``spurious correlation'', as the situation was phrased by Karl Pearson back in 1897, affects all data that measures parts of some whole, such as percentages, proportions, ppm and ppb. Such measurements are present in all fields of science, ranging from geology, biology, environmental sciences, forensic sciences, medicine and hydrology. This book presents the history and development of compositional data analysis along with Aitchison's log-ratio approach. Compositional Data Analysis describes the state of the art both in theoretical fields as well as applications in the different fields of science. Key Features: Reflects the state-of-the-art in compositional data analysis. Gives an overview of the historical development of compositional data analysis, as well as basic concepts and procedures. Looks at advances in algebra and calculus on the simplex. Presents applications in different fields of science, including, genomics, ecology, biology, geochemistry, planetology, chemistry and economics. Explores connections to correspondence analysis and the Dirichlet distribution. Presents a summary of three available software packages for compositional data analysis. Supported by an accompanying website featuring R code. Applied scientists working on compositional data analysis in any field of science, both in academia and professionals will benefit from this book, along with graduate students in any field of science working with compositional data.
| Author | : Andreas Makrides |
| Publisher | : John Wiley & Sons |
| Total Pages | : 310 |
| Release | : 2020-03-31 |
| Genre | : Mathematics |
| ISBN | : 1119721504 |
Download Data Analysis and Applications 4 Book in PDF, ePub and Kindle
Data analysis as an area of importance has grown exponentially, especially during the past couple of decades. This can be attributed to a rapidly growing computer industry and the wide applicability of computational techniques, in conjunction with new advances of analytic tools. This being the case, the need for literature that addresses this is self-evident. New publications are appearing, covering the need for information from all fields of science and engineering, thanks to the universal relevance of data analysis and statistics packages. This book is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians who have been working at the forefront of data analysis. The chapters included in this volume represent a cross-section of current concerns and research interests in these scientific areas. The material is divided into three parts: Financial Data Analysis and Methods, Statistics and Stochastic Data Analysis and Methods, and Demographic Methods and Data Analysis- providing the reader with both theoretical and applied information on data analysis methods, models and techniques and appropriate applications.
| Author | : Christine Preisach |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 714 |
| Release | : 2008-04-13 |
| Genre | : Computers |
| ISBN | : 354078246X |
Download Data Analysis, Machine Learning and Applications Book in PDF, ePub and Kindle
Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007.
| Author | : Konstantinos N. Zafeiris |
| Publisher | : John Wiley & Sons |
| Total Pages | : 452 |
| Release | : 2022-10-25 |
| Genre | : Computers |
| ISBN | : 1786307723 |
Download Data Analysis and Related Applications, Volume 2 Book in PDF, ePub and Kindle
The scientific field of data analysis is constantly expanding due to the rapid growth of the computer industry and the wide applicability of computational and algorithmic techniques, in conjunction with new advances in statistical, stochastic and analytic tools. There is a constant need for new, high-quality publications to cover the recent advances in all fields of science and engineering. This book is a collective work by a number of leading scientists, computer experts, analysts, engineers, mathematicians, probabilists and statisticians who have been working at the forefront of data analysis and related applications. The chapters of this collaborative work represent a cross-section of current concerns, developments and research interests in the above scientific areas. The collected material has been divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with related applications.
| Author | : Edward W. Frees |
| Publisher | : Cambridge University Press |
| Total Pages | : 492 |
| Release | : 2004-08-16 |
| Genre | : Business & Economics |
| ISBN | : 9780521535380 |
Download Longitudinal and Panel Data Book in PDF, ePub and Kindle
An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.
| Author | : S. Ejaz Ahmed |
| Publisher | : Springer |
| Total Pages | : 386 |
| Release | : 2017-03-21 |
| Genre | : Mathematics |
| ISBN | : 3319415735 |
Download Big and Complex Data Analysis Book in PDF, ePub and Kindle
This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.
