This textbook is an introduction to both the theory of statistical models with general linear models and the practical implementation of these analytical techniques using a widely available computer package, GLIM (Generalized Linear Interactive Modelling). The authors fully integrate the theoretical and practical aspects, illustrating statistical principles by worked examples using GLIM's extensive interactive facilities. Statisticians working in a wide range of fields including biomedical research and the social sciences will find this an invaluable desktop companion to their statistical modelling. It also provides a hands-on guide for advanced students in these areas who may be using the techniques for the first time.

Statistical Modelling in GLIM (Oxford Statistical Science Series)

Science & Math

Francis, Brian

Hinde, John

Anderson, Dorothy

Aitkin, Murray

Biological Sciences

Oxford University Press

Mathematics

This new edition of the successful multi-disciplinary text Statistical Modelling in GLIM takes into account new developments in both statistical software and statistical modeling. Including three new chapters on mixture and random effects models, it provides a comprehensive treatment of the theory of statistical modeling with generalized linear models with an emphasis on applications to practical problems and an expanded discussion of statistical theory. A wide range of case studies is also provided, using the normal, binomial Poisson, multinominal, gamma, exponential and Weibull distributions. This book is ideal for graduates and research students in applied statistics and a wide range of quantitative disciplines, including biology, medicine, and the social sciences. Professional statisticians at all levels will also find it an invaluable desktop companion.

Statistical Modelling in GLIM4 (Oxford Statistical Science Series)

Statistical Modelling in R (Oxford Statistical Science Series)

R is now the most widely used statistical package/language in university statistics departments and many research organizations. Its great advantages are that for many years, it has been the leading statistical package/language and that it can be freely downloaded from the R website. This text provides a comprehensive treatment of the theory of statistical modelling in R with an emphasis on applications to practical problems and an expanded discussion of statistical theory. A wide range of case studies is provided, using the normal, binomial, Poisson, multinomial, gamma, exponential and Weibull distributions, making this book ideal for graduates and research students in applied statistics and a wide range of qualitative disciplines.

Arts & Photography

History & Criticism

R is now the most widely used statistical package/language in university statistics departments and many research organizations. Its great advantages are that for many years, it has been the leading statistical package/language and that it can be freely downloaded from the R website. This text provides a comprehensive treatment of the theory of statistical modelling in R with an emphasis on applications to practical problems and an expanded discussion of statistical theory. A wide range of case studies is provided, using the normal, binomial, Poisson, multinomial, gamma, exponential and Weibull distributions, making this book ideal for graduates and research students in applied statistics and a wide range of qualitative disciplines.

Darnell, Ross

Statistical Inference: An Integrated Bayesian/Likelihood Approach (CRC Monographs on Statistics & Applied Probability (Hardcover))

Chapman and Hall/CRC

The complexity of large-scale data sets (“Big Data”) has stimulated the development of advanced 
computational methods for analysing them. There are two different kinds of methods to aid this. The 
model-based method uses probability models and likelihood and Bayesian theory, while the model-free 
method does not require a probability model, likelihood or Bayesian theory. These two approaches 
are based on different philosophical principles of probability theory, espoused by the famous 
statisticians Ronald Fisher and Jerzy Neyman. 
Introduction to Statistical Modelling and Inference covers simple experimental and survey designs, 
and probability models up to and including generalised linear (regression) models and some 
extensions of these, including finite mixtures. A wide range of examples from different application 
fields are also discussed and analysed. No special software is used, beyond that needed for maximum 
likelihood analysis of generalised linear models. Students are expected to have a basic 
mathematical background in algebra, coordinate geometry and calculus. 
Features 
• Probability models are developed from the shape of the sample empirical cumulative distribution 
function (cdf) or a transformation of it. 
• Bounds for the value of the population cumulative distribution function are obtained from the 
Beta distribution at each point of the empirical cdf. 
• Bayes’s theorem is developed from the properties of the screening test for a rare condition. 
• The multinomial distribution provides an always-true model for any randomly sampled data. 
• The model-free bootstrap method for finding the precision of a sample estimate has a model-based 
parallel – the Bayesian bootstrap – based on the always-true multinomial distribution. 
• The Bayesian posterior distributions of model parameters can be obtained from the maximum 
likelihood analysis of the model. 
This book is aimed at students in a wide range of disciplines including Data Science. The book is 
based on the model-based theory, used widely by scientists in many fields, and compares it, in less 
detail, with the model-free theory, popular in computer science, machine learning and official 
survey analysis. The development of the model-based theory is accelerated by recent developments 
in Bayesian analysis.

Introduction to Statistical Modelling and Inference

Business & Money

Education & Reference

Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing.  After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout. It presents Bayesian versions of one- and two-sample t-tests, along with the corresponding normal variance tests. The author then thoroughly discusses the use of the multinomial model and noninformative Dirichlet priors in "model-free" or nonparametric Bayesian survey analysis, before covering normal regression and analysis of variance. In the chapter on binomial and multinomial data, he gives alternatives, based on Bayesian analyses, to current frequentist nonparametric methods. The text concludes with new goodness-of-fit methods for assessing parametric models and a discussion of two-level variance component models and finite mixtures.  Emphasizing the principles of Bayesian inference and Bayesian model comparison, this book develops a unique methodology for solving challenging inference problems. It also includes a concise review of the various approaches to inference.

The purpose of this book is to evaluate a new approach to the analysis and reporting of the large-scale surveys for the National Assessment of Educational Progress carried out for the National Center for Education Statistics. The need for a new approach was driven by the demands for secondary analysisof the survey data by researchers who needed analyses more detailed than those published by NCES, and the need to accelerate the processing and publication of results from the surveys.
This new approach is based on a full multilevel statistical and psychometric model for students’ responses to the test items, taking into account the design of the survey, the backgrounds of the students, and the classes, schools and communities in which the students were located. The authors detail a fully integrated single model that incorporates both the survey design and the psychometric model by extending the traditional form of the psychometric model to accommodate the design structure while allowing for student, teacher, and school covariates.

Statistical Modeling of the National Assessment of Educational Progress (Statistics for Social and Behavioral Sciences)

Health, Fitness & Dieting

Aitkin, Irit

Psychology & Counseling

Springer

See the best price to sell, buy, or rent books by Aitkin, Murray. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Aitkin, Murray

See the best price to sell, buy, or rent books by Aitkin, Murray. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Statistical Modelling in GLIM (Oxford Statistical Science Series)

Statistical Modelling in GLIM (Oxford Statistical Science Series)

Statistical Modelling in GLIM4 (Oxford Statistical Science Series)

Statistical Modelling in R (Oxford Statistical Science Series)

Statistical Modelling in R (Oxford Statistical Science Series)

Statistical Inference: An Integrated Bayesian/Likelihood Approach (CRC Monographs on Statistics & Applied Probability (Hardcover))

Introduction to Statistical Modelling and Inference

Statistical Inference: An Integrated Bayesian/Likelihood Approach (CRC Monographs on Statistics & Applied Probability (Hardcover))

Statistical Modeling of the National Assessment of Educational Progress (Statistics for Social and Behavioral Sciences)

Statistical Modeling of the National Assessment of Educational Progress (Statistics for Social and Behavioral Sciences)