Exact Inference for Contingency Tables with Ordered Categories

摘要: Abstract This article proposes an efficient numerical algorithm for small-sample exact inferences in contingency tables having ordinal classifications. The inferences, which apply conditional on the observed marginal totals, also provide analysis log-linear model of linear-by-linear association cell probabilities. An test independence has a one-sided P value equal to null probability that model-based maximum likelihood estimates odds ratios are at least as large estimates. nonnull distribution yields confidence intervals structure. computations utilize extension network proposed by Mehta and Patel (1983).