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Review of inference in logistic regression model parameters

机译：逻辑回归模型参数推论的回顾

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摘要

This study attempts to review the existing literature in inference of logistic regression (LR) model parameters. The use of maximum likelihood estimators is unquestionable; however, its use is debatable in small-samples because they may be biased, although they are asymptotically unbiased. Theoretically however, for small-sample cases, bias-correction comes to remedy, though it is not easy to identify how much bias can be reduced. Hence it's use is not popular among it's users in small-sample cases. The LR model analysis is quite often used in real-life data where the data are skewed. However, literature in this area is not widely available. The use of three test statistics (Likelihood ratio, Score, Wald) is common in the LR model. Though the Wald test statistic is more popular, it does not perform as well as the other two. All these tests possess optimal asymptotic properties, but the small-sample behavior is less known.
机译：本研究试图回顾有关逻辑回归（LR）模型参数的现有文献。毫无疑问，最大似然估计器的使用。然而，尽管它们渐近无偏，但在小样本中它的使用值得商use，因为它们可能会有偏差。从理论上讲，对于小样本情况，可以使用偏差校正，尽管很难确定可以减少多少偏差。因此，在小样本情况下，它的使用在用户中并不流行。 LR模型分析经常用于实际数据中，这些数据偏斜。但是，该领域的文献并不广泛。在LR模型中，通常使用三个检验统计量（似然比，得分，Wald）。尽管Wald检验统计数据更受欢迎，但其性能却不如其他两个。所有这些测试都具有最佳渐近特性，但是小样本行为却鲜为人知。

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