Statistical Methods for Overdispersed Count Data

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  • Author : Jean-Francois Dupuy
  • Publisher : Elsevier
  • Pages : 192 pages
  • ISBN : 008102374X
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Statistical Methods for Overdispersed Count Data

Download or Read online Statistical Methods for Overdispersed Count Data full in PDF, ePub and kindle. this book written by Jean-Francois Dupuy and published by Elsevier which was released on 19 November 2018 with total page 192 pages. We cannot guarantee that Statistical Methods for Overdispersed Count Data book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. Statistical Methods for Overdispersed Count Data provides a review of the most recent methods and models for such data, including a description of R functions and packages that allow their implementation. All methods are illustrated on datasets arising in the field of health economics. As several tools have been developed to tackle over-dispersed and zero-inflated data (such as adjustment methods and zero-inflated models), this book covers the topic in a comprehensive and interesting manner. Includes reading on several levels, including methodology and applications Presents the state-of-the-art on the most recent zero-inflated regression models Contains a single dataset that is used as a common thread for illustrating all methodologies Includes R code that allows the reader to apply methodologies

Statistical Methods for Overdispersed Count Data

Statistical Methods for Overdispersed Count Data
  • Author : Jean-Francois Dupuy
  • Publisher : Elsevier
  • Release : 19 November 2018
GET THIS BOOK Statistical Methods for Overdispersed Count Data

Statistical Methods for Overdispersed Count Data provides a review of the most recent methods and models for such data, including a description of R functions and packages that allow their implementation. All methods are illustrated on datasets arising in the field of health economics. As several tools have been developed to tackle over-dispersed and zero-inflated data (such as adjustment methods and zero-inflated models), this book covers the topic in a comprehensive and interesting manner. Includes reading on several levels, including

A Comparison of Statistical Models for Correlated Over dispersed Count Data

A Comparison of Statistical Models for Correlated Over dispersed Count Data
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  • Publisher : Unknown
  • Release : 08 December 2021
GET THIS BOOK A Comparison of Statistical Models for Correlated Over dispersed Count Data

As the cost of RNA-sequencing (RNA-Seq) decreases, it becomes increasingly feasible to collect RNA-Seq data under complex study designs, including paired, longitudinal, and other correlated designs. Commonly used RNA-Seq analysis tools do not allow for correlation between observations, which is common in these types of studies. When applying statistical methods with mechanisms to account for correlated data to RNA-Seq experiments, extra considerations must be made because RNA-Seq experiments include data on 10,000 to 20,000 genes, resulting in a large number of statistical

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  • Publisher : CRC Press
  • Release : 04 June 2012
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Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors describe the basic ideas underlying each concept, model, and approach to give readers a good grasp of the fundamentals of the methodology without using rigorous mathematical arguments. The text covers classic concepts and popular topics, such as contingency tables, logistic models, and Poisson regression models, along with modern areas that

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  • Publisher : Springer Science & Business Media
  • Release : 22 December 2007
GET THIS BOOK Model Based Inference in the Life Sciences

This textbook introduces a science philosophy called "information theoretic" based on Kullback-Leibler information theory. It focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The text is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals. Readers are however expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes

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  • Publisher : CRC Press
  • Release : 23 August 2021
GET THIS BOOK Handbook of Statistical Methods for Randomized Controlled Trials

Statistical concepts provide scientific framework in experimental studies, including randomized controlled trials. In order to design, monitor, analyze and draw conclusions scientifically from such clinical trials, clinical investigators and statisticians should have a firm grasp of the requisite statistical concepts. The Handbook of Statistical Methods for Randomized Controlled Trials presents these statistical concepts in a logical sequence from beginning to end and can be used as a textbook in a course or as a reference on statistical methods for randomized

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  • Publisher : Springer Science & Business Media
  • Release : 08 March 2013
GET THIS BOOK Statistical Methods in Toxicology

This book contains selected papers from a workshop on modern statistical methods in toxicology held during the EUROTOX '90 conference in Leipzig. The papers deal with the biostatistical evaluation of the commonly used toxicological assays, i.e. mutagenicity, long-term carcinogenicity, embryotoxicity and chronic toxicity assays. The biological background is considered in detail, and most of the related statistical approaches described. In five overview papers, the present state of the art of the related topics is given, while in several contributed

Statistical Methods for Time conditional Survival Probability and Equally Spaced Count Data

Statistical Methods for Time conditional Survival Probability and Equally Spaced Count Data
  • Author : Victoria A. Gamerman
  • Publisher : Unknown
  • Release : 08 December 2021
GET THIS BOOK Statistical Methods for Time conditional Survival Probability and Equally Spaced Count Data

This dissertation develops statistical methods for time-conditional survival probability and for equally spaced count data. Time-conditional survival probabilities are an alternative measure of future survival by accounting for time elapsed from diagnosis and are estimated as a ratio of survival probabilities. In Chapter 2, we derive the asymptotic distribution of a vector of nonparametric estimators and use weighted least squares methodology for the analysis of time-conditional survival probabilities. We show that the proposed test statistics for evaluating the relationship between time-conditional

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  • Author : N. Balakrishnan
  • Publisher : John Wiley & Sons
  • Release : 16 June 2014
GET THIS BOOK Methods and Applications of Statistics in Clinical Trials Volume 2

Methods and Applications of Statistics in Clinical Trials,Volume 2: Planning, Analysis, and Inferential Methods includesupdates of established literature from the Wiley Encyclopedia ofClinical Trials as well as original material based on the latestdevelopments in clinical trials. Prepared by a leading expert, thesecond volume includes numerous contributions from currentprominent experts in the field of medical research. In addition,the volume features: • Multiple new articles exploring emerging topics, such asevaluation methods with threshold, empirical likelihood methods,nonparametric ROC analysis, over- and under-dispersed

Statistical Analysis of Microbiome Data with R

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  • Author : Yinglin Xia,Jun Sun,Ding-Geng Chen
  • Publisher : Springer
  • Release : 06 October 2018
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This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of R for data analysis step by step. The data and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, so that these new methods can be readily applied in their own research. The book also

Econometric Analysis of Count Data

Econometric Analysis of Count Data
  • Author : Rainer Winkelmann
  • Publisher : Springer Science & Business Media
  • Release : 11 November 2013
GET THIS BOOK Econometric Analysis of Count Data

Graduate students and researchers are provided with an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. Proper count data probability models allow for rich inferences, both with respect to the stochastic count process that generated the data, and with respect to predicting the distribution of outcomes. The book starts with a presentation of the benchmark Poisson regression model. Alternative models address unobserved heterogeneity, state dependence, selectivity, endogeneity, underreporting,

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  • Author : J. Philip Miller
  • Publisher : Elsevier
  • Release : 08 November 2010
GET THIS BOOK Essential Statistical Methods for Medical Statistics

Essential Statistical Methods for Medical Statistics presents only key contributions which have been selected from the volume in the Handbook of Statistics: Medical Statistics, Volume 27 (2009). While the use of statistics in these fields has a long and rich history, the explosive growth of science in general, and of clinical and epidemiological sciences in particular, has led to the development of new methods and innovative adaptations of standard methods. This volume is appropriately focused for individuals working in these fields. Contributors

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Introduction to Statistical Methods for Biosurveillance
  • Author : Ronald D. Fricker
  • Publisher : Cambridge University Press
  • Release : 25 February 2013
GET THIS BOOK Introduction to Statistical Methods for Biosurveillance

Bioterrorism is not a new threat, but in an increasingly interconnected world, the potential for catastrophic outcomes is greater today than ever. The medical and public health communities are establishing biosurveillance systems designed to proactively monitor populations for possible disease outbreaks as a first line of defense. The ideal biosurveillance system should identify trends not visible to individual physicians and clinicians in near-real time. Many of these systems use statistical algorithms to look for anomalies and to trigger epidemiologic investigation,