| Description |
1 online resource (393 pages) |
| Series |
Chapman and Hall/CRC Texts in Statistical Science Ser |
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Chapman and Hall/CRC Texts in Statistical Science Ser
|
| Contents |
Cover; Half title; Series Editors; Title; Copyright; Dedication; Contents; Preface; Chapter 1 Introduction; 1.1 Background; 1.2 Scope; 1.3 Notation; 1.4 Distributions related to the Normal distribution; 1.4.1 Normal distributions; 1.4.2 Chi-squared distribution; 1.4.3 t-distribution; 1.4.4 F-distribution; 1.4.5 Some relationships between distributions; 1.5 Quadratic forms; 1.6 Estimation; 1.6.1 Maximum likelihood estimation; 1.6.2 Example: Poisson distribution; 1.6.3 Least squares estimation; 1.6.4 Comments on estimation; 1.6.5 Example: Tropical cyclones; 1.7 Exercises |
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Chapter 2 Model Fitting2.1 Introduction; 2.2 Examples; 2.2.1 Chronic medical conditions; 2.2.2 Example: Birthweight and gestational age; 2.3 Some principles of statistical modelling; 2.3.1 Exploratory data analysis; 2.3.2 Model formulation; 2.3.3 Parameter estimation; 2.3.4 Residuals and model checking; 2.3.5 Inference and interpretation; 2.3.6 Further reading; 2.4 Notation and coding for explanatory variables; 2.4.1 Example: Means for two groups; 2.4.2 Example: Simple linear regression for two groups; 2.4.3 Example: Alternative formulations for comparing the means of two groups |
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2.4.4 Example: Ordinal explanatory variables2.5 Exercises; Chapter 3 Exponential Family and Generalized Linear Models; 3.1 Introduction; 3.2 Exponential family of distributions; 3.2.1 Poisson distribution; 3.2.2 Normal distribution; 3.2.3 Binomial distribution; 3.3 Properties of distributions in the exponential family; 3.4 Generalized linear models; 3.5 Examples; 3.5.1 Normal linear model; 3.5.2 Historical linguistics; 3.5.3 Mortality rates; 3.6 Exercises; Chapter 4 Estimation; 4.1 Introduction; 4.2 Example: Failure times for pressure vessels; 4.3 Maximum likelihood estimation |
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4.4 Poisson regression example4.5 Exercises; Chapter 5 Inference; 5.1 Introduction; 5.2 Sampling distribution for score statistics; 5.2.1 Example: Score statistic for the Normal distribution; 5.2.2 Example: Score statistic for the Binomial distribution; 5.3 Taylor series approximations; 5.4 Sampling distribution for maximum likelihood estimators; 5.4.1 Example: Maximum likelihood estimators for the Normal linear model; 5.5 Log-likelihood ratio statistic; 5.7 Hypothesis testing; 5.7.1 Example: Hypothesis testing for a Normal linear model; 5.6 Sampling distribution for the deviance |
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5.6.1 Example: Deviance for a Binomial model5.6.2 Example: Deviance for a Normal linear model; 5.6.3 Example: Deviance for a Poisson model; 5.8 Exercises; Chapter 6 Normal Linear Models; 6.1 Introduction; 6.2 Basic results; 6.2.1 Maximum likelihood estimation; 6.2.2 Least squares estimation; 6.2.3 Deviance; 6.2.4 Hypothesis testing; 6.2.5 Orthogonality; 6.2.6 Residuals; 6.2.7 Other diagnostics; 6.3 Multiple linear regression; 6.3.1 Example: Carbohydrate diet; 6.3.2 Coefficient of determination, R2; 6.3.3 Model selection; 6.3.4 Collinearity; 6.4 Analysis of variance |
| Notes |
6.4.1 One-factor analysis of variance |
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Print version record |
| Form |
Electronic book
|
| Author |
Barnett, Adrian G
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| ISBN |
9781351726214 |
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1351726218 |
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