- ISBN: 9780199219131 | 0199219133
- Cover: Paperback
- Copyright: 4/29/2009
Murray Aitkin is a Professorial Fellow at the Department of Mathematics and Statistics, University of Melbourne. In 1992 he was awarded an ARC Senior Research Fellowship, initially at the Australian National University and then at the University of Western Australia, where he worked on foundational issues in statistics. At the conclusion of the fellowship he was appointed to the Chair of Statistics at the University of Newcastle, UK, from which he took early retirement in 2004. In 2000-2002 he held a consulting position as Chief Statistician at the Education Statistics Services Institute, a division of the American Institutes for Research which provided consultancy to the National Center for Education Statistics of the US Department of Education. He continued to work as a consultant for NCES after 2002 at Newcastle, and this continues in Melbourne. John Hinde is Professor of Statistics at the National University of Ireland Galway having previously worked at the Universities of Exeter and Lancaster in the UK. It was while at Lancaster that he met his co-authors and wrote the original book Statistical Modelling in GLIM that was later revised and has now been translated to R. His interests are in all aspects of statistical modelling, including generalized linear models and their extensions, and statistical computing. Particular interests are in overdispersion modelling, mixture models, and random effect models. He was a joint founding editor of the journal Statistical Modelling and served as Chairman of the Statistical Modelling Society. He is currently President of the Irish Statistical Association.
Introducing R | p. 1 |
Statistical packages and statistical modelling | p. 1 |
Getting started in R | p. 1 |
Reading data into R | p. 3 |
Assignment and data generation | p. 6 |
Displaying data | p. 8 |
Data structures and the workspace | p. 9 |
Transformations and data modification | p. 11 |
Functions and suffixing | p. 12 |
Structure functions | p. 13 |
Mathematical functions | p. 13 |
Logical operators | p. 14 |
Control functions | p. 14 |
Statistical functions | p. 14 |
Random numbers | p. 15 |
Suffixes in expressions | p. 16 |
Extracting subsets of data | p. 17 |
Recoding variates and factors into new factors | p. 17 |
Graphical facilities | p. 18 |
Text functions | p. 21 |
Writing your own functions | p. 22 |
Sorting and tabulation | p. 23 |
Editing R code | p. 26 |
Installing and using packages | p. 27 |
Statistical modelling and inference | p. 28 |
Statistical models | p. 28 |
Types of variables | p. 30 |
Population models | p. 31 |
Random sampling | p. 44 |
The likelihood function | p. 44 |
Inference for single parameter models | p. 46 |
Comparing two simple hypotheses | p. 47 |
Information about a single parameter | p. 49 |
Comparing a simple null hypothesis and a composite alternative | p. 54 |
Inference with nuisance parameters | p. 58 |
Profile likelihoods | p. 59 |
Marginal likelihood for the variance | p. 63 |
Likelihood normalizing transformations | p. 66 |
Alternative test procedures | p. 68 |
Bayes inference | p. 71 |
Binomial model | p. 74 |
Hypergeometric sampling from finite populations | p. 80 |
The effect of the sample design on inference | p. 81 |
The exponential family | p. 82 |
Mean and variance | p. 83 |
Generalized linear models | p. 83 |
Maximum likelihood fitting of the GLM | p. 84 |
Model comparisons through maximized likelihoods | p. 87 |
Likelihood inference without models | p. 89 |
Likelihoods for percentiles | p. 89 |
Empirical likelihood | p. 92 |
Regression and analysis of variance | p. 97 |
An example | p. 97 |
Strategies for model simplification | p. 107 |
Stratified, weighted and clustered samples | p. 111 |
Model criticism | p. 114 |
Mis-specification of the probability distribution | p. 116 |
Mis-specification of the link function | p. 119 |
The occurrence of aberrant and influential observations | p. 119 |
Mis-specification of the systematic part of the model | p. 123 |
The Box-Cox transformation family | p. 123 |
Modelling and background information | p. 126 |
Link functions and transformations | p. 136 |
Regression models for prediction | p. 138 |
Model choice and mean square prediction error | p. 140 |
Model selection through cross-validation | p. 141 |
Reduction of complex regression models | p. 144 |
Sensitivity of the Box-Cox transformation | p. 153 |
The use of regression models for calibration | p. 156 |
Measurement error in the explanatory variables | p. 159 |
Factorial designs | p. 161 |
Unbalanced cross-classifications | p. 168 |
The Bennett hostility data | p. 168 |
ANOVA of the cross-classification | p. 170 |
Regression analysis of the cross-classification | p. 174 |
Statistical package treatments of cross-classifications | p. 176 |
Missing data | p. 178 |
Approximate methods for missing data | p. 180 |
Modelling of variance heterogeneity | p. 180 |
Poison example | p. 184 |
Tree example | p. 191 |
Binary response data | p. 195 |
Binary responses | p. 195 |
Transformations and link functions | p. 197 |
Profile likelihoods for functions of parameters | p. 202 |
Model criticism | p. 207 |
Mis-specification of the probability distribution | p. 207 |
Mis-specification of the link function | p. 207 |
The occurrence of aberrant and influential observations | p. 207 |
Binary data with continuous covariates | p. 208 |
Contingency table construction from binary data | p. 223 |
The prediction of binary outcomes | p. 235 |
Profile and conditional likelihoods in 2 × 2 tables | p. 242 |
Three-dimensional contingency tables with a binary response | p. 246 |
Prenatal care and infant mortality | p. 246 |
Coronary heart disease | p. 248 |
Multidimensional contingency tables with a binary response | p. 255 |
Multinomial and Poisson response data | p. 269 |
The Poisson distribution | p. 269 |
Cross-classified counts | p. 271 |
Multicategory responses | p. 279 |
Multinomial logit model | p. 285 |
The Poisson-multinomial relation | p. 287 |
Fitting the multinomial logit model | p. 293 |
Ordered response categories | p. 298 |
Common slopes for the regressions | p. 299 |
Linear trend over response categories | p. 301 |
Proportional slopes | p. 304 |
The continuation ratio model | p. 304 |
Other models | p. 308 |
An Example | p. 310 |
Multinomial logit model | p. 313 |
Continuation ratio model | p. 320 |
Structured multinomial responses | p. 330 |
Independent outcomes | p. 331 |
Correlated outcomes | p. 339 |
Survival data | p. 347 |
Introduction | p. 347 |
The exponential distribution | p. 347 |
Fitting the exponential distribution | p. 349 |
Model criticism | p. 354 |
Comparison with the normal family | p. 361 |
Censoring | p. 364 |
Likelihood function for censored observations | p. 365 |
Probability plotting with censored data: the Kaplan-Meier estimator | p. 368 |
The gamma distribution | p. 377 |
Maximum likelihood with uncensored data | p. 379 |
Maximum likelihood with censored data | p. 382 |
Double modelling | p. 384 |
The Weibull distribution | p. 388 |
Maximum likelihood fitting of the Weibull distribution | p. 390 |
The extreme value distribution | p. 394 |
The reversed extreme value distribution | p. 397 |
Survivor function plotting for the Weibull and extreme value distributions | p. 398 |
The Cox proportional hazards model and the piecewise exponential distribution | p. 400 |
Maximum likelihood fitting of the piecewise exponential distribution | p. 403 |
Examples | p. 404 |
The logistic and log-logistic distributions | p. 407 |
The normal and lognormal distributions | p. 411 |
Evaluating the proportional hazard assumption | p. 414 |
Competing risks | p. 420 |
Time-dependent explanatory variables | p. 427 |
Discrete time models | p. 427 |
Finite mixture models | p. 433 |
Introduction | p. 433 |
Example - girl birthweights | p. 434 |
Finite mixtures of distributions | p. 434 |
Maximum likelihood in finite mixtures | p. 435 |
Standard errors | p. 437 |
Testing for the number of components | p. 440 |
Example | p. 443 |
Likelihood 'spikes' | p. 448 |
Galaxy data | p. 450 |
Kernel density estimates | p. 458 |
Random effect models | p. 461 |
Overdispersion | p. 461 |
Testing for overdispersion | p. 464 |
Conjugate random effects | p. 466 |
Normal kernel: the t-distribution | p. 466 |
Poisson kernel: the negative binomial distribution | p. 472 |
Binomial kernel: beta-binomial distribution | p. 477 |
Gamma kernel | p. 478 |
Difficulties with the conjugate approach | p. 478 |
Normal random effects | p. 479 |
Predicting from the normal random effect model | p. 481 |
Gaussian quadrature examples | p. 481 |
Overdispersion model fitting | p. 481 |
Poisson - the fabric fault data | p. 482 |
Binomial - the toxoplasmosis data | p. 484 |
Other specified random effect distributions | p. 487 |
Arbitrary random effects | p. 487 |
Examples | p. 489 |
The fabric fault data | p. 489 |
The toxoplasmosis data | p. 492 |
Leukaemia remission data | p. 493 |
The Brownlee stack-loss data | p. 493 |
Random coefficient regression models | p. 496 |
Example - the fabric fault data | p. 498 |
Algorithms for mixture fitting | p. 499 |
The trypanosome data | p. 499 |
Modelling the mixing probabilities | p. 503 |
Mixtures of mixtures | p. 504 |
Variance component models | p. 508 |
Models with shared random effects | p. 508 |
The normal/normal model | p. 508 |
Exponential family two-level models | p. 511 |
Other approaches | p. 513 |
NPML estimation of the masses and mass-points | p. 514 |
Random coefficient models | p. 514 |
Variance component model fitting | p. 515 |
Children's height development | p. 516 |
Multi-centre trial of beta-blockers | p. 524 |
Longitudinal study of obesity | p. 530 |
Autoregressive random effect models | p. 537 |
Latent variable models | p. 543 |
The normal factor model | p. 543 |
IRT models | p. 544 |
The Rasch model | p. 544 |
The two-parameter model | p. 545 |
The three-parameter logit (3PL) model | p. 547 |
Example - The Law School Aptitude Test (LSAT) | p. 547 |
Spatial dependence | p. 551 |
Multivariate correlated responses | p. 552 |
Discreteness of the NPML estimate | p. 552 |
Bibliography | p. 554 |
R function and constant index | p. 567 |
Dataset index | p. 570 |
Subject index | p. 571 |
Table of Contents provided by Ingram. All Rights Reserved. |
The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.
Digital License
You are licensing a digital product for a set duration. Durations are set forth in the product description, with "Lifetime" typically meaning five (5) years of online access and permanent download to a supported device. All licenses are non-transferable.
More details can be found here.