Design of Experiments for Generalized Linear Models Chapman & Hall/CRC Interdisciplinary Statistics Series
Generalized Linear Models (GLMs) allow many statistical analyses to be extended to important statistical distributions other than the Normal distribution. While numerous books exist on how to analyse data using a GLM, little information is available on how to collect the data that are to be analysed in this way.
This is the first book focusing specifically on the design of experiments for GLMs. Much of the research literature on this topic is at a high mathematical level, and without any information on computation. This book explains the motivation behind various techniques, reduces the difficulty of the mathematics, or moves it to one side if it cannot be avoided, and gives examples of how to write and run computer programs using R.
Features
- The generalisation of the linear model to GLMs
- Background mathematics, and the use of constrained optimisation in R
- Coverage of the theory behind the optimality of a design
- Individual chapters on designs for data that have Binomial or Poisson distributions
- Bayesian experimental design
- An online resource contains R programs used in the book
This book is aimed at readers who have done elementary differentiation and understand minimal matrix algebra, and have familiarity with R. It equips professional statisticians to read the research literature. Nonstatisticians will be able to design their own experiments by following the examples and using the programs provided.
1. Generalized Linear Models. 2. Background Material. 3. The Theory Underlying Design. 4. The Binomial Distribution. 5. The Poisson Distribution. 6. Several Other Distributions. 7. Bayesian experimental design
K. G. Russell is at the National Institute for Applied Statistical Research Australia, University of Wollongong.
Date de parution : 06-2021
15.2x22.9 cm
Date de parution : 12-2018
15.2x22.9 cm
Mots-clés :
General Equivalence Theorem; Complementary Log Log Link Functions; binomial distribution; Min 1Q Median 3Q Max; Poisson distribution; Bayesian Experimental Design; linear models; GLM Analysis; mixture experiments; Support Points; linear mixed models; Ml Estimate; R software; Complementary Log Log Link; Linear Predictor; Canonical Variable; Link Function; MPL Estimate; MPL Estimator; Randomised Complete Block Design; Design Weights; Freedom AIC; Logit Link; ANCOVA Model; GLMs; Complementary Log Log; Probit Link; Prior Distribution; Multinomial Distribution; Exponential Family; Freedom Residual Deviance