# The Exact Agreement

If the use of the estimated p value does not result in a specific method, a specific method can be determined by combining the estimated p value and a maximization step [11]. The estimated p value in this test method is considered an alternative to the order of the data sets. The corresponding tail range for the E-M p value of the proposed test is and the corresponding p value is given as (6) Darroch JN, McCloud PI (1986) Category distinction and observer agreement. Australian Journal of Statistics 28 (3): 371-388 In recent years, Diaconis and Sturmfels have presented a sampling method for conditional distributions of category data. Your algorithm is based on the algebraic theory of toric ideals that are used to create Markov`s bases. The Diaconis Sturmfels algorithm leads to a Monte-Carlo Markov non-asymptomatic chain algorithm to obtain precise conclusions on certain classes of models, such as log-linear models. In this article, we apply the Diaconis-Sturmfels algorithm to a number of models arising from the rat agreement problem, with particular attention to the Multi-Rater case. The relevant Markov bases are explicitly calculated and some results are presented to simplify the calculation. An expanded example of a real data set shows the widespread applicability of this method. Asymptomatic and accurate approaches have often been used to test the match between two advisors with binary results. The exact conditional approach ensures compliance with the size of the tests compared to the asymptomatic approach traditionally used on the basis of the standardized Cohen-Kappa coefficient. An alternative to the conditional approach is an unconditional strategy that relaxes the limitation of fixed limits, as is the case in the under-stressed approach. This paper examines three methods of testing specific hypotheses: an approach based on maximization, an approach based on conditional p value and maximization, and an approach based on estimation and maximization.

We compared these test methods on the basis of the Cohen-Kappa commonly used in terms of size and performance. We recommend two specific approaches for practical use based on performance benefits: the conditional p value-based approach and maximization, and the estimation-based and maximizing approach. In the problem of random convergence review, one might be interested in hypotheses, since a number of specific unconditional procedures have been proposed [8], [9] to reduce the conservative approach C. One of them, as described by Basu [9], which considers the unconditional approach accurate by maximizing the probability of the tail over the annoying parameter space (called approach M).