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Modeling Count Data

This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for;

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Modeling Count Data

This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for;

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Categorical Data Analysis and Multilevel Modeling Using R

Categorical Data Analysis and Multilevel Modeling Using R provides a practical guide to regression techniques for analyzing binary, ordinal;

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Multivariate Models and Multivariate Dependence Concepts

for modeling of binary, count, ordinal, and extreme value response data. It is virtually self-contained, and includes many exercises and;

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Handbook for Applied Modeling

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data;

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Handbook for Applied Modeling

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data;

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Statistical Regression Modeling with R

regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume;

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Statistical Regression Modeling with R

regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume;

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Handbook of Discrete-Valued Time Series

series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the;

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Handbook of Discrete-Valued Time Series

series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the;

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Mixed Effects Models for the Population Approach

continuous, categorical, count, and time-to-event data. This leads to the use of generic methods, such as the stochastic approximation of the EM;

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Mixed Effects Models for the Population Approach

, categorical, count, and time-to-event data. This leads to the use of generic methods, such as the stochastic approximation of the EM algorithm (SAEM;

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Modeling the Agile Data Warehouse with Data Vault

Data Modeling for Agile Data Warehouse using Data Vault Modeling Approach. Includes Enterprise Data Warehouse Architecture. This is a;

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Generalized Latent Variable Modeling

response types, such as ordinal, nominal, count, and survival data. Joint modeling of mixed responses, such as survival and longitudinal data;

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Statistical Analysis of Panel Count Data

Panel count data occur in studies that concern recurrent events, or event history studies, when study subjects are observed only at;

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Multilevel Modeling of Categorical Outcomes Using IBM SPSS

focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes;

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Multilevel Modeling of Categorical Outcomes Using IBM SPSS

focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes;

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Regression Models for Categorical and Count Data

regression analysis on categorical and count data. The author covers both the theory and application of statistical models, with the help;

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Data Modeling for MongoDB

Read this book to learn how to do data modeling for MongoDB applications, and accomplish these five objectives: 1. Understand how data;

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Applied Categorical and Count Data Analysis

Developed from the authors' graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the;

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Regression With Social Data

response models* Poisson, negative binomial, and other regression models for event-count data* Survival analysis using multistate, multiepisode;

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Data Modeling Fundamentals

The purpose of this book is to provide a practical approach for IT professionals to acquire the necessary knowledge and expertise in data;

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Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses

science (GISci) is the new kid on the block. Its theoretical foundations are therefore still developing and data quality and uncertainty modeling;

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Principles of Modeling Uncertainties in Spatial Data and Spatial Analyses

science (GISci) is the new kid on the block. Its theoretical foundations are therefore still developing and data quality and uncertainty modeling;

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Database Modeling Step by Step

contemporary topics, the book opens with relational data modeling and ends with BigData database modeling following a road map of the evolution;

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Data Modeling of Financial Derivatives

Written in plain English and based on successful client engagements, Data Modeling of Financial Derivatives: A Conceptual Approach;

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Model and Data Engineering

This book constitutes the refereed proceedings of the 5th International Conference on Model and Data Engineering, MEDI 2015, held in Rhodes;

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