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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;

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Statistical Analysis of Panel Count Data

unifies statistical models and methods that have been developed for analyzing panel count data. It provides the first comprehensive coverage;

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Statistical Analysis of Categorical Data

to a statistician wanting to analyze and report on categorical data, Statistical Analysis of Categorical Data is an excellent resource for;

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Biostatistical Methods

; Poisson and negative binomial models for count data; and the analysis of event-time data including the Cox proportional hazards model and its;

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Generalized Linear Models

methods of analyzing correlated binary data using latent variables. It also proposes a semiparametric method to model link functions for binary;

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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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Applied Categorical and Count Data Analysis

statistical analysis of discrete data, including categorical and count outcomes. The authors describe the basic ideas underlying each concept, model;

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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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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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Practical Statistical Methods

an extensive statistical background. In addition to nonparametric methods, it covers methods for discrete and continuous data. Omitting;

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Practical Statistical Methods

an extensive statistical background. In addition to nonparametric methods, it covers methods for discrete and continuous data. Omitting;

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Analysis of Correlated Data with SAS and R

for the analysis of hierarchical binary, count and continuous response data. It explains how to use procedures in SAS and packages in R for;

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Analysis of Correlated Data with SAS and R

for the analysis of hierarchical binary, count and continuous response data. It explains how to use procedures in SAS and packages in R for;

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Statistical Methods for Fuzzy Data

-priori information. Key Features: * Provides basic methods for the mathematical description of fuzzy data, as well as statistical methods that;

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Discrete Data Analysis with R

Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both;

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Statistical Methods for the Evaluation of University Systems

performance of the students at Italian universities, and statistical modeling for questionnaire data. Other issues are the implications of introducing;

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Statistical Methods for Reliability Data, Second Edition

Reliability Data, Second Edition (SMRD2) is an essential guide to the most widely used and recently developed statistical methods for reliability data;

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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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Advanced Statistical Methods for the Analysis of Large Data-Sets

The theme of the meeting was Statistical Methods for the Analysis of Large Data-Sets . In recent years there has been increasing interest;

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Statistical Methods for Non-Precise Data

The formal description of non-precise data before their statistical analysis is, except for error models and interval arithmetic, a;

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Statistics for Data Scientists

single-course introduction of statistical methods for data science.;

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Statistical Analysis of Spherical Data

This is the first comprehensive, yet clearly presented, account of statistical methods for analysing spherical data. The analysis of data;

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Bayesian Models for Categorical Data

The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology;

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Developing Econometrics

. * Accompanying software featuring DASC (Data Analysis and Statistical Computing). Essential reading for practitioners in any area of econometrics;

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Practical Statistics for Data Scientists

belongs to Statistical machine learning methods that learn from data Unsupervised learning methods for extracting meaning from unlabeled data;

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