Abstract
We develop a set of sequential importance sampling (SIS) strategies for sampling nearly uniformly from two-way zero-one or contingency tables with fixed marginal sums and a given set of structural zeros. The SIS procedure samples tables column by column or cell by cell by using appropriate proposal distributions, and enables us to approximate closely the null distributions of a number of test statistics involved in such tables. When structural zeros are on the diagonal or follow certain patterns, more efficient SIS algorithms are developed which guarantee that every generated table is valid. Examples show that our methods can be applied to make conditional inference on zero-one and contingency tables, and are more efficient than other existing Monte Carlo algorithms.
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