A sufficient condition on the covariance matrix for F tests in linear models to be valid
使线性模型 F 检验有效的协方差矩阵充分条件AI 解读 ↓
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使线性模型 F 检验有效的协方差矩阵充分条件AI 解读 ↓
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Consider the linear model for the random n x 1 vector Y distributed multivariate normally with mean X/3 and covariance matrix C, where X is of size n x p and rank k < p, C is a nonnegative-definite matrix of constants and fi is a p x 1 vector of unknown parameters. In many situations we are interested in testing only one or a few hypotheses about the fli's. For example in the randomized block design we are mainly interested in testing the equality of the treatment effects. For these situations we give a sufficient condition on C so that the usual F test will still be valid, i.e. that the null distribution will not change. Some earlier results on full rank models (Graybill, 1976, p. 210; Halperin, 1951; Arnold, 1979) and on repeated measurements designs (Halperin, 1951; Huynh & Feldt, 1970) showing the validity of F tests when C has certain forms, can be deduced as special cases of the result to be presented. The main result is proved in ? 2. In ? 3 the necessity of the condition is discussed. We use A to denote a g-inverse of the matrix A satisfying AA -A = A. The transpose of A is denoted by A' and 4(A) denotes the column space of A.
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组间不平衡下的风险异质性稳健推断
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