title: Methods of reducing loss of efficiency due to discreteness of distributions.
abstract: All models of this paper involve R x C contingency tables in which the total frequency is fixed (full multinomial model), or in which the row totals are fixed (product multinomial model). For the most part, we assume that the column categories are ordered. For the full multinomial model the null hypothesis of interest is independence, i.e., the (ij)th cell probability is the product of the marginal probabilities of the ith row and jth column. In the product multinomial model the null hypothesis is that the R multinomial distributions have the same vector of cell probabilities. Our review includes (1) a careful listing of two-sided and one-sided alternatives, and (2) methodology to reduce the loss of efficiency of tests because of the discreteness of the model (The methodologies discussed are efficient in several senses. Tests are exact. Tests have very favorable and robust power properties. Tests make use of back-up statistics, thereby providing a finer grid of p-values. In some special cases, e.g., a 2 x C table and a one-sided alternative, conditional p-values are found, within seconds, simply by entering row frequencies into a given website. Thus, computational efficiency is exceptional.), and (3) a critique of some exact linear permutation tests (that are conditional on row and column margins) for both two-sided and some one-sided alternatives. Furthermore, recommendations as to which tests to use for specific alternatives are made.
authors.affiliation: Department of Statistics, Rutgers University, Piscataway NJ 08854-8019, USA. artcohen@rci.rutgers.edu
authors.forename: A
authors.initials: A
authors.lastname: Cohen
authors.forename: H B
authors.initials: HB
authors.lastname: Sackrowitz
mesh_terms: D003627:Data Interpretation, Statistical; D004526:Efficiency; D006801:Humans; D008432:Mathematical Computing; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D011336:Probability; D015203:Reproducibility of Results; D018401:Sample Size; D012984:Software; D016896:Treatment Outcome