An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

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Experimental Design and Data Analysis for Biologists

Many scientific phenomena are now investigated by complex computer models or codes A computer experiment is a number of runs of the code with various inputs A feature of many computer experiments is that the output is deterministic--rerunning the code with the same inputs gives identical observations Often, the codes are computationally expensive to run, and a common objective of an experiment is to fit a cheaper predictor of the output to the data Our approach is to model the deterministic output as the realization of a stochastic process, thereby providing a statistical basis for designing experiments (choosing the inputs) for efficient prediction With this model, estimates of uncertainty of predictions are also available Recent work in this area is reviewed, a number of applications are discussed, and we demonstrate our methodology with an example

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The design and analysis of computer experiments

Statistics for Spatial Data.

General Strategies.- Regression Models.- Classification Models.- Other Considerations.- Appendix.- References.- Indices.

Applied Predictive Modeling

Full four-color book.

Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R.

All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies.

Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.

Bioinformatics and Computational Biology Solutions Using R and Bioconductor

Minimax and maximin distance designs

Many of the existing parametric and nonparametric tests for homogeneity of variances, and some variations of these tests, are examined in this paper. Comparisons are made under the null hypothesis (for robustness) and under the alternative (for power). Monte Carlo simulations of various symmetric and asymmetric distributions, for various sample sizes, reveal a few tests that are robust and have good power. These tests are further compared using data from outer continental shelf bidding on oil and gas leases.

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A Comparative Study of Tests for Homogeneity of Variances, with Applications to the Outer Continental Shelf Bidding Data

Univariate Distributions and Their Generation. Multivariate Generation Techniques. Multivariate Normal and Related Distributions. Johnson's Translation System. Elliptically Contoured Distributions. Circular, Spherical and Related Distributions. Khintchine Distributions. Miscellaneous Distributions. Research Directions. References. Supplementary References. Index.

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Multivariate statistical simulation

A generalized simulated annealing method has been developed and applied to the optimization of functions (possibly constrained) having many local extrema. The method is illustrated in some difftcult pedagogical examples and used to solve a problem analyzed by Bates (Technometrics, 25, pp. 373–376, 1983) for which we identify an improved optimum. The sensitivity of the solution to changes in the constraints and in other specifications of the problem is analyzed and discussed.

https://www.jstor.org/stable/pdfplus/1269076.pdf

Generalized simulated annealing for function optimization

SUMMARY The paucity of distributions that can be used as an aid to modelling the structure of multivariate data is a recognized limitation. We present a relatively simple family of distributions for use in describing data which are not elliptically symmetric. Properties of the family are discussed and it is shown that multivariate Pareto, Burr and Logistic distributions are special cases. In addition, simulation methods for applications in Monte Carlo robustness studies are outlined and, finally, the fit to a data set on uranium exploration is compared to that obtained using the usual bivariate normal distribution.

Mark E. Johnson

Papers

Minimax and maximin distance designs

A Comparative Study of Tests for Homogeneity of Variances, with Applications to the Outer Continental Shelf Bidding Data

Multivariate statistical simulation

Generalized simulated annealing for function optimization

A Family of Distributions for Modelling Non-Elliptically Symmetric Multivariate Data