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线性模型中两个离群值的检验AI 解读

The detection of two outliers in a linear model is considered. The case of two outliers in the same direction is studied in detail. The first Bonferroni inequality is used to obtain nominal upper percentage points. For a random sample from a normal distribution, the proposed statistic reduces to the well known Murphy's test statistic. Similarly, for two outliers on either direction, our statistic reduces to the studentized range statistic. An approximate relationship between…

线性模型中两个异常值的检验AI 解读

The detection of two outliers in a linear model is considered. The case of two outliers in the same direction is studied in detail. The first Bonferroni inequality is used to obtain nominal upper percentage points. For a random sample from a normal distribution, the proposed statistic reduces to the well known Murphy's test statistic. Similarly, for two outliers on either direction, our statistic reduces to the studentized range statistic. An approximate relationship between…

线性回归中检测单个异常值的均值偏移检验AI 解读

The problem of detecting a single outlier in a linear regression model is considered. Results of Srikantan (1961), David & Paulson (1965) and Doornbos (1966) are unified and extended. The various cases considered are: (i) known variance, (ii) external studentization, and (iii) pooled studentization. One-sided and two-sided test statistics for detecting a single outlier are proposed. These statistics are maxima of suitably standardized or studentized weighted residuals.…