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Regression Analysis of Count Data

Regression Analysis of Count Data PDF Author: A. Colin Cameron
Publisher: Cambridge University Press
ISBN: 1107014166
Category : Business & Economics
Languages : en
Pages : 597

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Book Description
This book provides the most comprehensive and up-to-date account of regression methods to explain the frequency of events.

Regression Analysis of Count Data

Regression Analysis of Count Data PDF Author: A. Colin Cameron
Publisher: Cambridge University Press
ISBN: 1107014166
Category : Business & Economics
Languages : en
Pages : 597

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Book Description
This book provides the most comprehensive and up-to-date account of regression methods to explain the frequency of events.

Regression Analysis of Count Data

Regression Analysis of Count Data PDF Author: A. Colin Cameron
Publisher: Cambridge University Press
ISBN: 9780521635677
Category : Business & Economics
Languages : en
Pages : 436

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Book Description
This analysis provides a comprehensive account of models and methods to interpret frequency data.

Econometric Analysis of Count Data

Econometric Analysis of Count Data PDF Author: Rainer Winkelmann
Publisher: Springer Science & Business Media
ISBN: 9783540404040
Category : Business & Economics
Languages : en
Pages : 324

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Book Description
Many other sections have been entirely rewritten and extended."--BOOK JACKET.

Regression Models for Categorical and Count Data

Regression Models for Categorical and Count Data PDF Author: Peter Martin
Publisher: SAGE
ISBN: 1529762693
Category : Reference
Languages : en
Pages : 273

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Book Description
In this engaging and well-illustrated volume of the SAGE Quantitative Research Kit, Peter Martin provides practical guidance on conducting regression analysis on categorical and count data. The author covers both the theory and application of statistical models, with the help of illuminating graphs.

Econometric Analysis of Count Data

Econometric Analysis of Count Data PDF Author: Rainer Winkelmann
Publisher: Springer Science & Business Media
ISBN: 3662041499
Category : Business & Economics
Languages : en
Pages : 282

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Book Description
The primary objective of this book is to provide an introduction to the econometric modeling of count data for graduate students and researchers. It should serve anyone whose interest lies either in developing the field fur ther, or in applying existing methods to empirical questions. Much of the material included in this book is not specific to economics, or to quantita tive social sciences more generally, but rather extends to disciplines such as biometrics and technometrics. Applications are as diverse as the number of congressional budget vetoes, the number of children in a household, and the number of mechanical defects in a production line. The unifying theme is a focus on regression models in which a dependent count variable is modeled as a function of independent variables which mayor may not be counts as well. The modeling of count data has come of age. Inclusion of some of the fundamental models in basic textbooks, and implementation on standard computer software programs bear witness to that. Based on the standard Poisson regression model, numerous extensions and alternatives have been developed to address the common challenges faced in empirical modeling (unobserved heterogeneity, selectivity, endogeneity, measurement error, and dependent observations in the context of panel data or multivariate data, to name but a few) as well as the challenges that are specific to count data (e. g. , over dispersion and underdispersion).

Regression Models for Categorical, Count, and Related Variables

Regression Models for Categorical, Count, and Related Variables PDF Author: John P. Hoffmann
Publisher: Univ of California Press
ISBN: 0520289293
Category : Social Science
Languages : en
Pages : 428

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Book Description
Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists counting the number of offenses people commit, health scientists studying the number of suicides across neighborhoods, and psychologists modeling mental health treatment success are all interested in outcomes that are not continuous. Instead, they must measure and analyze these events and phenomena in a discrete manner. This book provides an introduction and overview of several statistical models designed for these types of outcomes—all presented with the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis. Numerous examples from the social sciences demonstrate the practical applications of these models. The chapters address logistic and probit models, including those designed for ordinal and nominal variables, regular and zero-inflated Poisson and negative binomial models, event history models, models for longitudinal data, multilevel models, and data reduction techniques such as principal components and factor analysis. Each chapter discusses how to utilize the models and test their assumptions with the statistical software Stata, and also includes exercise sets so readers can practice using these techniques. Appendices show how to estimate the models in SAS, SPSS, and R; provide a review of regression assumptions using simulations; and discuss missing data. A companion website includes downloadable versions of all the data sets used in the book.

Regression methods for the analysis of count data. Generalised linear models for limited dependent variables

Regression methods for the analysis of count data. Generalised linear models for limited dependent variables PDF Author: Martin Georg Haas
Publisher: GRIN Verlag
ISBN: 3346380122
Category : Business & Economics
Languages : en
Pages : 11

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Book Description
Seminar paper from the year 2019 in the subject Business economics - Miscellaneous, grade: 1.0, Zeppelin University Friedrichshafen, course: Advanced Methods | N | Limited Dependent Variables, language: English, abstract: This paper assesses the application of regression methods to analyse count data. R-Code and Data are available from the author! While the common multiple regression method has a wide range of applicability, and can be accommodated to various different kinds of regressor variables, its application is limited to the modelling of response variables from the space of real numbers. For the analysis of other kinds of responses, such as counts, a more generalised set of tools is needed. This toolset is given by the generalised linear model framework and maximum likelihood estimation. For the specific purpose of this paper, the count data analysis methods of Poisson, Negative-Binomial, Hurdle and Zero-Inflation models are considered. This paper explains their theoretical background and applies them to a unique dataset that motivates their respective use. It is structured as follows: section 2 describes the applied dataset and section 3 the generalised linear model framework. In section 4 and section 5 the basic count data models and their results are discussed, while section 6 and section 7 explain the more advanced methods and their results. section 8 concludes.

Applied Survey Data Analysis

Applied Survey Data Analysis PDF Author: Steven G. Heeringa
Publisher: CRC Press
ISBN: 1498761615
Category : Mathematics
Languages : en
Pages : 568

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Book Description
Highly recommended by the Journal of Official Statistics, The American Statistician, and other journals, Applied Survey Data Analysis, Second Edition provides an up-to-date overview of state-of-the-art approaches to the analysis of complex sample survey data. Building on the wealth of material on practical approaches to descriptive analysis and regression modeling from the first edition, this second edition expands the topics covered and presents more step-by-step examples of modern approaches to the analysis of survey data using the newest statistical software. Designed for readers working in a wide array of disciplines who use survey data in their work, this book continues to provide a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. An example-driven guide to the applied statistical analysis and interpretation of survey data, the second edition contains many new examples and practical exercises based on recent versions of real-world survey data sets. Although the authors continue to use Stata for most examples in the text, they also continue to offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book’s updated website.

Statistical Analysis of Panel Count Data

Statistical Analysis of Panel Count Data PDF Author: Jianguo Sun
Publisher: Springer Science & Business Media
ISBN: 1461487153
Category : Medical
Languages : en
Pages : 271

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Book Description
Panel count data occur in studies that concern recurrent events, or event history studies, when study subjects are observed only at discrete time points. By recurrent events, we mean the event that can occur or happen multiple times or repeatedly. Examples of recurrent events include disease infections, hospitalizations in medical studies, warranty claims of automobiles or system break-downs in reliability studies. In fact, many other fields yield event history data too such as demographic studies, economic studies and social sciences. For the cases where the study subjects are observed continuously, the resulting data are usually referred to as recurrent event data. This book collects and unifies statistical models and methods that have been developed for analyzing panel count data. It provides the first comprehensive coverage of the topic. The main focus is on methodology, but for the benefit of the reader, the applications of the methods to real data are also discussed along with numerical calculations. There exists a great deal of literature on the analysis of recurrent event data. This book fills the void in the literature on the analysis of panel count data. This book provides an up-to-date reference for scientists who are conducting research on the analysis of panel count data. It will also be instructional for those who need to analyze panel count data to answer substantive research questions. In addition, it can be used as a text for a graduate course in statistics or biostatistics that assumes a basic knowledge of probability and statistics.

Modeling Count Data

Modeling Count Data PDF Author: Joseph M. Hilbe
Publisher: Cambridge University Press
ISBN: 1107028337
Category : Business & Economics
Languages : en
Pages : 301

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Book Description
"This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of linear regression and works up to an analysis of the Poisson and negative binomial models, and to the problem of overdispersion. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in public health, ecology, econometrics, transportation, and other related fields"--