多元逻辑斯谛回归中正边缘纤维的连通性
On connectivity of fibers with positive marginals in multiple logistic regression
摘要：
在本文中，我们考虑精确测试的多元逻辑斯谛回归，协变量的水平是等间距，通过马尔可夫贝斯。在多元逻辑斯谛回归的通常应用中，对于协变量的每个水平组合，样本量是正的。在这种情况下，我们不需要一个完整的马尔可夫基，这保证了所有光纤的连通性。我们首先给出了多重泊松回归的显式马尔可夫基。通过Lawrence提升的基础上，在二元逻辑斯谛回归的情况下，我们显示了一个简单的马尔可夫基的子集，连接所有的纤维与一个积极的样本量的协变量水平的每个组合。
英文摘要：
In this paper we consider exact tests of a multiple logistic regression, where the levels of covariates are equally spaced, via Markov beses. In usual application of multiple logistic regression, the sample size is positive for each combination of levels of the covariates. In this case we do not need a whole Markov basis, which guarantees connectivity of all fibers. We first give an explicit Markov basis for multiple Poisson regression. By the Lawrence lifting of this basis, in the case of bivariate logistic regression, we show a simple subset of the Markov basis which connects all fibers with a positive sample size for each combination of levels of covariates.
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