Several exact inference procedures for logistic regression require the simulation of a 0-1 dependent vector according to its conditional distribution, given the sufficient statistics for some nuisance parameters. This is viewed, in this work, as a sampling problem involving a population of $n$ units, unequal selection probabilities and balancing constraints. The basis for this reformulation of exact inference is a proposition deriving the limit, as $n$ goes to infinity, of the conditional distribution of the dependent vector given the logistic regression sufficient statistics. It is proposed to sample from this distribution using the cube sampling algorithm. The interest of this approach to exact inference is illustrated by tackling new problems. First it allows to carry out exact inference with continuous covariates. It is also useful for the investigation of a partial correlation between several 0-1 vectors. This is illustrated in an example dealing with presence-absence data in ecology.

中文翻译：

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在逻辑回归中使用调查抽样算法进行精确推理

逻辑回归的几个精确推理过程需要根据条件分布模拟 0-1 相关向量，给定一些有害参数的足够统计量。在这项工作中，这被视为涉及人口的抽样问题$n$单位、不平等的选择概率和平衡约束。这种精确推理的重新表述的基础是推导极限的命题，如$n$在给定逻辑回归足够统计量的情况下，从属向量的条件分布趋于无穷大。建议使用立方体采样算法从该分布中采样。这种方法对精确推理的兴趣通过解决新问题来说明。首先，它允许使用连续协变量进行精确推理。它对于研究几个 0-1 向量之间的偏相关也很有用。这在一个处理生态学中存在-不存在数据的例子中得到了说明。