Modeling Count Data
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Modeling Count Data
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Author : Joseph M. Hilbe
language : en
Publisher: Cambridge University Press
Release Date : 2014-07-21
Modeling Count Data written by Joseph M. Hilbe and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-21 with Business & Economics categories.
This book provides guidelines and fully worked examples of how to select, construct, interpret and evaluate the full range of count models.
Modeling Count Data Using Poisson And Negative Binomial Models
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Author : Najib A. Mozahem
language : en
Publisher:
Release Date : 2021
Modeling Count Data Using Poisson And Negative Binomial Models written by Najib A. Mozahem and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with College students categories.
I teach management at the university level, and one of the most important topics covered in the curriculum is human behavior in organizations. As an instructor, I have always been fascinated by the behavior of university students. After all, the university is an organization, and students are the largest group in it. One particular behavior that interested me was student withdrawal from courses throughout the semester. This case illustrates how I used count models to study group differences in course withdrawal. As the total number of courses from which a student withdraws during his or her time in university must be a whole number, the analysis was performed using count models. The case study will explain why such models were used and how the analysis was performed.
Modeling Count Data Using Stata
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Author : Najib Mozahem
language : en
Publisher:
Release Date : 2020
Modeling Count Data Using Stata written by Najib Mozahem and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Learn Poisson and negative binomial regression techniques About This Video Work with single and multiple independent variables Learn about model visualization and probability prediction Get acquainted with the dataset and explore continuous and binary variables In Detail The course is divided into two parts. In the first part, you'll be introduced to the theory behind count models in an intuitive way while keeping the math at a minimum. The course starts with an overview of count tables, where you'll learn how to calculate the incidence rate ratio. You'll get to grips with Poisson regression and understand how to work with continuous, binary, and categorical variables. As you advance, you'll explore the concept of overdispersion and how to address this issue using negative binomial models. The course also covers other count models such as truncated models and zero-inflated models. In the second part of the course, you'll be able to apply what you have learned using Stata. You'll be taken through a large project where you'll fit the Poisson, negative binomial, and zero-inflated models. Additionally, you'll discover the tools used to compare these models.
Count Data Models
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Author : Rainer Winkelmann
language : en
Publisher: Springer
Release Date : 1994
Count Data Models written by Rainer Winkelmann and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994 with Business & Economics categories.
Sas For Mixed Models
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Author : Walter W. Stroup
language : en
Publisher: SAS Institute
Release Date : 2018-12-12
Sas For Mixed Models written by Walter W. Stroup and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-12 with Computers categories.
Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. In a single volume, this book updates both SAS® for Linear Models, Fourth Edition, and SAS® for Mixed Models, Second Edition, covering the latest capabilities for a variety of applications featuring the SAS GLIMMIX and MIXED procedures. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS® for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS. This book expands coverage of mixed models for non-normal data and mixed-model-based precision and power analysis, including the following topics: Random-effect-only and random-coefficients models Multilevel, split-plot, multilocation, and repeated measures models Hierarchical models with nested random effects Analysis of covariance models Generalized linear mixed models This book is part of the SAS Press program.
Econometric Analysis Of Count Data
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Author : Rainer Winkelmann
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-03-07
Econometric Analysis Of Count Data written by Rainer Winkelmann and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-03-07 with Business & Economics categories.
The “count data” ?eld has further ?ourished since the previous edition of this book was published in 2003. The development of new methods has not slowed down by any means, and the application of existing ones in applied work has expanded in many areas of social science research. This, in itself, would be reason enough for updating the material in this book, to ensure that it continues to provide a fair representation of the current state of research. In addition, however, I have seized the opportunity to undertake some major changes to the organization of the book itself. The core material on cross-section models for count data is now presented in four chapters, rather than in two as previously. The ?rst of these four chapters introduces the Poissonregressionmodel,anditsestimationbymaximumlikelihoodorpseudo maximum likelihood. The second focuses on unobserved heterogeneity, the third on endogeneity and non-random sample selection. The fourth chapter provides an extended and uni?ed discussion of zeros in count data models. This topic deserves, in my view, special emphasis, as it relates to aspects of modeling and estimation that are speci?c to counts, as opposed to general exponential regression models for non-negative dependent variables. Count distributions put positive probability mass on single o- comes, and thus o?er a richer set of interesting inferences.
Foundations Of Linear And Generalized Linear Models
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Author : Alan Agresti
language : en
Publisher: John Wiley & Sons
Release Date : 2015-01-15
Foundations Of Linear And Generalized Linear Models written by Alan Agresti and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-15 with Mathematics categories.
A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.
International Encyclopedia Of Statistical Science
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Author : Miodrag Lovric
language : en
Publisher: Springer Nature
Release Date : 2025-06-19
International Encyclopedia Of Statistical Science written by Miodrag Lovric and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-19 with Mathematics categories.
The International Encyclopedia of Statistical Science stands as a monumental effort to enrich statistics education globally, particularly in regions facing educational challenges. By amalgamating the expertise of over 700 authors from 110 countries, including Nobel Laureates and presidents of statistical societies, it offers an unparalleled resource for readers worldwide. This encyclopedia is not just a collection of entries; it is a concerted effort to revive statistics as a vibrant, critical field of study and application. Providing a comprehensive and accessible account of statistical terms, methods, and applications, it enables readers to gain a quick insight into the subject, regardless of their background. This work serves to refresh and expand the knowledge of researchers, managers, and practitioners, highlighting the relevance and applicability of statistics across various fields, from economics and business to healthcare and public policy. Furthermore, it aims to inspire students by demonstrating the significance of statistics in solving real-world problems, thus encouraging a new generation to explore and contribute to the field.
Probabilistic Deep Learning
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Author : Oliver Duerr
language : en
Publisher: Manning
Release Date : 2020-11-10
Probabilistic Deep Learning written by Oliver Duerr and has been published by Manning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-10 with Computers categories.
Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. Summary Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability teaches the increasingly popular probabilistic approach to deep learning that allows you to refine your results more quickly and accurately without much trial-and-error testing. Emphasizing practical techniques that use the Python-based Tensorflow Probability Framework, you’ll learn to build highly-performant deep learning applications that can reliably handle the noise and uncertainty of real-world data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology The world is a noisy and uncertain place. Probabilistic deep learning models capture that noise and uncertainty, pulling it into real-world scenarios. Crucial for self-driving cars and scientific testing, these techniques help deep learning engineers assess the accuracy of their results, spot errors, and improve their understanding of how algorithms work. About the book Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications. What's inside Explore maximum likelihood and the statistical basis of deep learning Discover probabilistic models that can indicate possible outcomes Learn to use normalizing flows for modeling and generating complex distributions Use Bayesian neural networks to access the uncertainty in the model About the reader For experienced machine learning developers. About the author Oliver Dürr is a professor at the University of Applied Sciences in Konstanz, Germany. Beate Sick holds a chair for applied statistics at ZHAW and works as a researcher and lecturer at the University of Zurich. Elvis Murina is a data scientist. Table of Contents PART 1 - BASICS OF DEEP LEARNING 1 Introduction to probabilistic deep learning 2 Neural network architectures 3 Principles of curve fitting PART 2 - MAXIMUM LIKELIHOOD APPROACHES FOR PROBABILISTIC DL MODELS 4 Building loss functions with the likelihood approach 5 Probabilistic deep learning models with TensorFlow Probability 6 Probabilistic deep learning models in the wild PART 3 - BAYESIAN APPROACHES FOR PROBABILISTIC DL MODELS 7 Bayesian learning 8 Bayesian neural networks
Human Factors In Intelligent Transportation Systems
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Author : Woodrow Barfield
language : en
Publisher: Psychology Press
Release Date : 2014-01-14
Human Factors In Intelligent Transportation Systems written by Woodrow Barfield and has been published by Psychology Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-14 with Computers categories.
The Intelligent Transportation System (ITS) Program is a cooperative effort by government, private industry, and academia to apply advanced technology to the task of resolving the problems of surface transportation. The objective is to improve travel efficiency and mobility, enhance safety, conserve energy, provide economic benefits, and protect the environment. The current demand for mobility has exceeded the available capacity of the roadway system. Because the highway system cannot be expanded, except in minor ways, the available capacity must be used more efficiently to handle the increased demand. ITS applies advanced information processing, communication, sensing, and computer control technologies to the problems of surface transportation. Considerable research and development efforts will be required to produce these new technologies and to convert technologies developed in the defense and space programs to solve surface transportation problems. ITS has been subdivided into six interlocking technology areas. This book addresses human factors concerns for four of these areas: * Advanced Traveler Information Systems are a variety of systems that provide real time, in-vehicle information to drivers regarding navigation and route guidance, motorist services, roadway signing, and hazard warnings. * Advanced Vehicle Control Systems refer to systems that aid drivers in controlling their vehicle particularly in emergency situations and ultimately taking over some or all of the driving tasks. * Commercial Vehicle Operations address the application of ITS technologies to the special needs of commercial roadway vehicles including automated vehicle identification, location, weigh-in-motion, clearance sensing, and record keeping. * Advanced Traffic Management Systems monitor, control and manage traffic on streets and highways to reduce congestion using vehicle route diversion, automated signal timing, changeable message signs, and priority control systems. Two technical areas are not specifically addressed in individual chapters, but many aspects of them are covered in associated chapters: * Advanced Rural Transportation Systems include systems that apply ITS technologies to the special needs of rural systems and include emergency notification and response, vehicle location, and traveler information. * Advanced Public Transportation Systems enhance the effectiveness, attractiveness and economics of public transportation and include fleet management, automated fare collection, and real-time information systems.