Error Normality Testing in a Model of Two-Way Nested Classification

The paper deals with the construction of a normality test for the verification of the assumption about normal distribution of random errors in a fixed model of two-way nested classification. For this purpose adequate linear transformations and orthogonal observable random vectors have been presented. To the adjusted residual vector obtained after these transformations, the D'Agostino normality test has been applied.     

Testing the Mean Vector and the Correlation Coefficient in Life-Testing Under Bivariate Normality

In life-testing situations under bivariate normality of (X, U), a few smallest or a few largest Y-observations may not be available. Tests for μ = 0 (mean vector) and o = 0 (correlation coefficient) are developed from the available Y-observations and their concomitant X-observations. The robustness of these tests to departures from normality is investigated.