Markov chain Monte Carlo tests for designed experiments

Details Markov chain Monte Carlo tests for designed experiments Markov chain Monte Carlo tests for designed experiments

2006-11-15

arxiv.org/abs/math/0611463v1

Journal of Statistical Planning and Inference, 140 (2010), 817-830

Mathematics

Statistics Theory

Scientific paper

10.1016/j.jspi.2009.09.010

We consider conditional exact tests of factor effects in designed experiments for discrete response variables. Similarly to the analysis of contingency tables, a Markov chain Monte Carlo method can be used for performing exact tests, when large-sample approximations are poor and the enumeration of the conditional sample space is infeasible. For designed experiments with a single observation for each run, we formulate log-linear or logistic models and consider a connected Markov chain over an appropriate sample space. In particular, we investigate fractional factorial designs with $2^{p-q}$ runs, noting correspondences to the models for $2^{p-q}$ contingency tables.

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