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Statistical Methods For Overdispersed Count Data

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Statistical Methods for Overdispersed Count Data

Statistical Methods for Overdispersed Count Data Book
Author : Jean-Francois Dupuy
Publisher : Elsevier
Release : 2018-11-19
ISBN : 008102374X
Language : En, Es, Fr & De

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Book Description :

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 Modeling Count Data with Overdispersion and Missing Time Varying Categorical Covariates

Statistical Methods for Modeling Count Data with Overdispersion and Missing Time Varying Categorical Covariates Book
Author : Elizabeth H. Payne
Publisher : Unknown
Release : 2016
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Statistical Methods for Modeling Count Data with Overdispersion and Missing Time Varying Categorical Covariates book written by Elizabeth H. Payne, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Statistical Methods for Rates and Proportions

Statistical Methods for Rates and Proportions Book
Author : Joseph L. Fleiss,Bruce Levin,Myunghee Cho Paik
Publisher : John Wiley & Sons
Release : 2013-06-12
ISBN : 1118625617
Language : En, Es, Fr & De

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Book Description :

* Includes a new chapter on logistic regression. * Discusses the design and analysis of random trials. * Explores the latest applications of sample size tables. * Contains a new section on binomial distribution.

Model Based Inference in the Life Sciences

Model Based Inference in the Life Sciences Book
Author : David R. Anderson
Publisher : Springer Science & Business Media
Release : 2007-12-22
ISBN : 9780387740751
Language : En, Es, Fr & De

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Book Description :

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 reasonable competence in modeling and parameter estimation.

Statistical Methods in Toxicology

Statistical Methods in Toxicology Book
Author : Ludwig Hothorn
Publisher : Springer Science & Business Media
Release : 2013-03-08
ISBN : 364248736X
Language : En, Es, Fr & De

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Book Description :

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 papers new approaches are discussed. The most important features are: - A new view on the per-litter analysis problem in em- bryotoxicity assays. - A highly sophisticated treatment of the so-called muta-tox problem in mutagenicity assays. - A detailed discussion of the multiplicity problem based on the closed testing procedure. This volume provides readers with an overview of modern biostatistical methods for several toxicological assays and is in part intended for direct, practical use.

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

Statistical Methods for Time conditional Survival Probability and Equally Spaced Count Data Book
Author : Victoria A. Gamerman
Publisher : Unknown
Release : 2016
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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 survival probabilities and additional time survived have central Chi-Square distributions under the null hypotheses. Further, we conducted simulation studies to assess the empirical probability of making a type I error for one of the hypotheses tests developed and to assess the power of the various models and statistics proposed. Additionally, we used weighted least squares techniques to fit regression models for the log time-conditional survival probabilities as a function of time survived after diagnosis to address clinically relevant questions. In Chapter 3, we derive the asymptotic distribution of time-conditional survival probability estimators from a Weibull parametric regression model and from a Logistic-Weibull cure model, adjusting for continuous covariates. We implement the weighted least squares methodology to assess relevant hypotheses. We create a statistical framework for investigating time-conditional survival probability by developing additional methodological approaches to address the relationship between estimated time-conditional survival probabilities, time survived, and patient prognostic factors. Over-dispersed count data are often encountered in longitudinal studies. In Chapter 4, we implement a maximum-likelihood based method for the analysis of equally spaced longitudinal count data with over-dispersion. The key features of this approach are first-order antedependence and linearity of the conditional expectations. We also assume a Markovian model of first order, implying that the value of an outcome on a subject at a specific measurement occasion only depends on the value at the previous measurement occasion. Our maximum likelihood approach using the Poisson model for count data benefits from a simple interpretation of regression parameters, like that in GEE analysis of count data.

Applied Categorical and Count Data Analysis

Applied Categorical and Count Data Analysis Book
Author : Wan Tang,Hua He,Xin M. Tu
Publisher : CRC Press
Release : 2012-06-04
ISBN : 143989793X
Language : En, Es, Fr & De

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Book Description :

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 include models for zero-modified count outcomes, parametric and semiparametric longitudinal data analysis, reliability analysis, and methods for dealing with missing values. R, SAS, SPSS, and Stata programming codes are provided for all the examples, enabling readers to immediately experiment with the data in the examples and even adapt or extend the codes to fit data from their own studies. Designed for a one-semester course for graduate and senior undergraduate students in biostatistics, this self-contained text is also suitable as a self-learning guide for biomedical and psychosocial researchers. It will help readers analyze data with discrete variables in a wide range of biomedical and psychosocial research fields.

Statistical Analysis of Microbiome Data with R

Statistical Analysis of Microbiome Data with R Book
Author : Yinglin Xia,Jun Sun,Ding-Geng Chen
Publisher : Springer
Release : 2018-10-06
ISBN : 9811315345
Language : En, Es, Fr & De

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Book Description :

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 discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research.

The Generalized Monotone Incremental Forward Stagewise Method for Modeling Longitudinal Clustered and Overdispersed Count Data

The Generalized Monotone Incremental Forward Stagewise Method for Modeling Longitudinal  Clustered  and Overdispersed Count Data Book
Author : Rebecca Lehman
Publisher : Unknown
Release : 2017
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

With the influx of high-dimensional data there is an immediate need for statistical methods that are able to handle situations when the number of predictors greatly exceeds the number of samples. One such area of growth is in examining how environmental exposures to toxins impact the body long term. The cytokinesis-block micronucleus assay can measure the genotoxic effect of exposure as a count outcome. To investigate potential biomarkers, high-throughput assays that assess gene expression and methylation have been developed. It is of interest to identify biomarkers or molecular features that are associated with elevated micronuclei (MN) or nuclear bud (Nbud) frequency, measures of exposure to environmental toxins. Given our desire to model a count outcome (MN and Nbud frequency) using high-throughput genomic features as predictors, novel methods that can handle over-parameterized models need development. Overdispersion, when the variance of a count outcome is larger than its mean, is frequently observed with count response data. For situations where overdispersion is present, the negative binomial distribution is more appropriate. Furthermore, we expand the method to the longitudinal Poisson and longitudinal negative binomial settings for modeling a longitudinal or clustered outcome both when there is equidispersion and overdispersion. The method we have chosen to expand is the Generalized Monotone Incremental Forward Stagewise (GMIFS) method. We extend the GMIFS to the negative binomial distribution so it may be used to analyze a count outcome when both a high-dimensional predictor space and overdispersion are present. Our methods were compared to glmpath. We also extend the GMIFS to the longitudinal Poisson and longitudinal negative binomial distribution for analyzing a longitudinal outcome. Our methods were compared to glmmLasso and GLMMLasso. The developed methods were used to analyze two datasets, one from the Norwegian Mother and Child Cohort study and one from the breast cancer epigenomic study conducted by researchers at Virginia Commonwealth University. In both studies a count outcome measured exposure to potential genotoxins and either gene expression or high-throughput methylation data formed a high dimensional predictor space. Further, the breast cancer study was longitudinal such that outcomes and high-dimensional genomic features were collected at multiple time points during the study for each patient. Our goal is to identify biomarkers that are associated with elevated MN or NBud frequency. From the development of these methods, we hope to make available more comprehensive statistical models for analyzing count outcomes with high dimensional predictor spaces and either cross-sectional or longitudinal study designs.

Methods and Applications of Statistics in Clinical Trials Volume 2

Methods and Applications of Statistics in Clinical Trials  Volume 2 Book
Author : N. Balakrishnan
Publisher : John Wiley & Sons
Release : 2014-06-16
ISBN : 1118595963
Language : En, Es, Fr & De

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Book Description :

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 models, andmulti-armed bandit problems • Up-to-date research on the Cox proportional hazardmodel, frailty models, trial reports, intrarater reliability,conditional power, and the kappa index • Key qualitative issues including cost-effectivenessanalysis, publication bias, and regulatory issues, which arecrucial to the planning and data management of clinical trials

Statistical Methods for Hospital Monitoring with R

Statistical Methods for Hospital Monitoring with R Book
Author : Anthony Morton,Kerrie L. Mengersen,Geoffrey Playford,Michael Whitby
Publisher : John Wiley & Sons
Release : 2013-06-27
ISBN : 1118639170
Language : En, Es, Fr & De

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Book Description :

Hospitals monitoring is becoming more complex and is increasingboth because staff want their data analysed and because ofincreasing mandated surveillance. This book provides a suiteof functions in R, enabling scientists and data analysts working ininfection management and quality improvement departments inhospitals, to analyse their often non-independent data which isfrequently in the form of trended, over-dispersed and sometimesauto-correlated time series; this is often difficult to analyseusing standard office software. This book provides much-needed guidance on data analysis using Rfor the growing number of scientists in hospital departments whoare responsible for producing reports, and who may have limitedstatistical expertise. This book explores data analysis using R and is aimed atscientists in hospital departments who are responsible forproducing reports, and who are involved in improving safety.Professionals working in the healthcare quality and safetycommunity will also find this book of interest Statistical Methods for Hospital Monitoring with R: Provides functions to perform quality improvement and infectionmanagement data analysis. Explores the characteristics of complex systems, such asself-organisation and emergent behaviour, along with theirimplications for such activities as root-cause analysis and thePareto principle that seek few key causes of adverse events. Provides a summary of key non-statistical aspects of hospitalsafety and easy to use functions. Provides R scripts in an accompanying web site enablinganalyses to be performed by the reader ahref="http://www.wiley.com/go/hospital_monitoring"http://www.wiley.com/go/hospital_monitoring/a Covers issues that will be of increasing importance in thefuture, such as, generalised additive models, and complex systems,networks and power laws.

Statistical Theory and Method Abstracts

Statistical Theory and Method Abstracts Book
Author : Anonim
Publisher : Unknown
Release : 2000
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Statistical Theory and Method Abstracts book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Essential Statistical Methods for Medical Statistics

Essential Statistical Methods for Medical Statistics Book
Author : J. Philip Miller
Publisher : Elsevier
Release : 2010-11-08
ISBN : 9780444537386
Language : En, Es, Fr & De

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Book Description :

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 are internationally renowned experts in their respective areas. · Contributors are internationally renowned experts in their respective areas · Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research · Methods for assessing Biomarkers, analysis of competing risks · Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs · Structural equations modelling and longitudinal data analysis

Introduction to Statistical Methods for Biosurveillance

Introduction to Statistical Methods for Biosurveillance Book
Author : Ronald D. Fricker
Publisher : Cambridge University Press
Release : 2013-02-25
ISBN : 1107328063
Language : En, Es, Fr & De

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Book Description :

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, quantification, localization and outbreak management. This book discusses the design and evaluation of statistical methods for effective biosurveillance for readers with minimal statistical training. Weaving public health and statistics together, it presents basic and more advanced methods, with a focus on empirically demonstrating added value. Although the emphasis is on epidemiologic and syndromic surveillance, the statistical methods can be applied to a broad class of public health surveillance problems.

Fundamental Statistical Methods for Analysis of Alzheimer s and Other Neurodegenerative Diseases

Fundamental Statistical Methods for Analysis of Alzheimer s and Other Neurodegenerative Diseases Book
Author : Katherine E. Irimata,Brittany N. Dugger,Jeffrey R. Wilson
Publisher : Johns Hopkins University Press
Release : 2020-05-05
ISBN : 142143671X
Language : En, Es, Fr & De

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Book Description :

Allowing more people to aid in analyzing data—while promoting constructive dialogues with statisticians—this book will hopefully play an important part in unlocking the secrets of these confounding diseases.

Statistical and Econometric Methods for Transportation Data Analysis

Statistical and Econometric Methods for Transportation Data Analysis Book
Author : Simon Washington,Matthew G. Karlaftis,Fred Mannering,Panagiotis Anastasopoulos
Publisher : CRC Press
Release : 2020-01-30
ISBN : 0429534221
Language : En, Es, Fr & De

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Book Description :

Praise for the Second Edition: The second edition introduces an especially broad set of statistical methods ... As a lecturer in both transportation and marketing research, I find this book an excellent textbook for advanced undergraduate, Master’s and Ph.D. students, covering topics from simple descriptive statistics to complex Bayesian models. ... It is one of the few books that cover an extensive set of statistical methods needed for data analysis in transportation. The book offers a wealth of examples from the transportation field. —The American Statistician Statistical and Econometric Methods for Transportation Data Analysis, Third Edition offers an expansion over the first and second editions in response to the recent methodological advancements in the fields of econometrics and statistics and to provide an increasing range of examples and corresponding data sets. It describes and illustrates some of the statistical and econometric tools commonly used in transportation data analysis. It provides a wide breadth of examples and case studies, covering applications in various aspects of transportation planning, engineering, safety, and economics. Ample analytical rigor is provided in each chapter so that fundamental concepts and principles are clear and numerous references are provided for those seeking additional technical details and applications. New to the Third Edition Updated references and improved examples throughout. New sections on random parameters linear regression and ordered probability models including the hierarchical ordered probit model. A new section on random parameters models with heterogeneity in the means and variances of parameter estimates. Multiple new sections on correlated random parameters and correlated grouped random parameters in probit, logit and hazard-based models. A new section discussing the practical aspects of random parameters model estimation. A new chapter on Latent Class Models. A new chapter on Bivariate and Multivariate Dependent Variable Models. Statistical and Econometric Methods for Transportation Data Analysis, Third Edition can serve as a textbook for advanced undergraduate, Masters, and Ph.D. students in transportation-related disciplines including engineering, economics, urban and regional planning, and sociology. The book also serves as a technical reference for researchers and practitioners wishing to examine and understand a broad range of statistical and econometric tools required to study transportation problems.

Statistical and Econometric Methods for Transportation Data Analysis Second Edition

Statistical and Econometric Methods for Transportation Data Analysis  Second Edition Book
Author : Simon P. Washington,Matthew G. Karlaftis,Fred Mannering
Publisher : CRC Press
Release : 2010-12-02
ISBN : 1420082868
Language : En, Es, Fr & De

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Book Description :

The complexity, diversity, and random nature of transportation problems necessitates a broad analytical toolbox. Describing tools commonly used in the field, Statistical and Econometric Methods for Transportation Data Analysis, Second Edition provides an understanding of a broad range of analytical tools required to solve transportation problems. It includes a wide breadth of examples and case studies covering applications in various aspects of transportation planning, engineering, safety, and economics. After a solid refresher on statistical fundamentals, the book focuses on continuous dependent variable models and count and discrete dependent variable models. Along with an entirely new section on other statistical methods, this edition offers a wealth of new material. New to the Second Edition A subsection on Tobit and censored regressions An explicit treatment of frequency domain time series analysis, including Fourier and wavelets analysis methods New chapter that presents logistic regression commonly used to model binary outcomes New chapter on ordered probability models New chapters on random-parameter models and Bayesian statistical modeling New examples and data sets Each chapter clearly presents fundamental concepts and principles and includes numerous references for those seeking additional technical details and applications. To reinforce a practical understanding of the modeling techniques, the data sets used in the text are offered on the book’s CRC Press web page. PowerPoint and Word presentations for each chapter are also available for download.

Statistical Data Analysis Based on the L1 Norm and Related Methods

Statistical Data Analysis Based on the L1 Norm and Related Methods Book
Author : Yadolah Dodge
Publisher : Birkhäuser
Release : 2012-12-06
ISBN : 3034882017
Language : En, Es, Fr & De

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Book Description :

This volume contains a selection of invited papers, presented to the fourth International Conference on Statistical Data Analysis Based on the L1-Norm and Related Methods, held in Neuchâtel, Switzerland, from August 4–9, 2002. The contributions represent clear evidence to the importance of the development of theory, methods and applications related to the statistical data analysis based on the L1-norm.

Cancer Clinical Trials

Cancer Clinical Trials Book
Author : Stephen L. George,Xiaofei Wang,Herbert Pang
Publisher : CRC Press
Release : 2016-08-03
ISBN : 1315354330
Language : En, Es, Fr & De

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Book Description :

Cancer Clinical Trials: Current and Controversial Issues in Design and Analysis provides statisticians with an understanding of the critical challenges currently encountered in oncology trials. Well-known statisticians from academic institutions, regulatory and government agencies (such as the U.S. FDA and National Cancer Institute), and the pharmaceutical industry share their extensive experiences in cancer clinical trials and present examples taken from actual trials. The book covers topics that are often perplexing and sometimes controversial in cancer clinical trials. Most of the issues addressed are also important for clinical trials in other settings. After discussing general topics, the book focuses on aspects of early and late phase clinical trials. It also explores personalized medicine, including biomarker-based clinical trials, adaptive clinical trial designs, and dynamic treatment regimes.

Effective Statistical Learning Methods for Actuaries III

Effective Statistical Learning Methods for Actuaries III Book
Author : Michel Denuit,Donatien Hainaut,Julien Trufin
Publisher : Springer Nature
Release : 2019-11-16
ISBN : 3030258270
Language : En, Es, Fr & De

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Book Description :

This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. It simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous yet accessible. Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting. Requiring only a basic knowledge of statistics, this book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning. This is the third of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.