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检验线性模型中的独立性假设AI 解读

We propose using an existing set of statistical tools in a new way that allows one to test the independence assumption in standard normal theory linear models. The set of tools is near-replicate lack-of-fit tests. The classical lack-of-fit test requires a linear model in which some rows of the model matrix are repeated. Near-replicate lack-of-fit tests were developed to mimic the behavior of the classical test by identifying clusters of rows in the design matrix that are…

线性模型中独立性假设的检验AI 解读

We propose using an existing set of statistical tools in a new way that allows one to test the independence assumption in standard normal theory linear models. The set of tools is near-replicate lack-of-fit tests. The classical lack-of-fit test requires a linear model in which some rows of the model matrix are repeated. Near-replicate lack-of-fit tests were developed to mimic the behavior of the classical test by identifying clusters of rows in the design matrix that are…

逻辑回归异常值检验：条件方法AI 解读

We consider exact conditional methods for identifying outliers in logistic regression data. Tests for a single outlier and multiple outliers are developed assuming a logistic slippage model. The p-values for these tests are determined using an explicit enumeration of all possible responses consistent with the observed value of the sufficient statistic. Justifications are given for preferring this computationally intensive approach to standard methods based on asymptotic…

逻辑回归离群值检验的条件方法AI 解读

We consider exact conditional methods for identifying outliers in logistic regression data. Tests for a single outlier and multiple outliers are developed assuming a logistic slippage model. The p-values for these tests are determined using an explicit enumeration of all possible responses consistent with the observed value of the sufficient statistic. Justifications are given for preferring this computationally intensive approach to standard methods based on asymptotic…

调查数据交叉分类表的调整卡方检验AI 解读

Holt, Scott & Ewings (1980) propose a modified Pearson X2 statistic for testing independence in a two-way table of survey data. It is shown that the multiplier of X2 needed for this modification is a weighted sum of cell and marginal cell design effects. A similar adjustment of the X2 and G2 statistics is possible whenever log linear models with closed form estimates are fitted to a cross-classified table of survey data.

调查数据交叉分类表的调整卡方检验AI 解读

Holt, Scott & Ewings (1980) propose a modified Pearson X2 statistic for testing independence in a two-way table of survey data. It is shown that the multiplier of X2 needed for this modification is a weighted sum of cell and marginal cell design effects. A similar adjustment of the X2 and G2 statistics is possible whenever log linear models with closed form estimates are fitted to a cross-classified table of survey data.