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误分类二项数据中两总体概率比较的局部最优设计AI 解读

Optimal experimental designs are found for estimating the difference P1−P2 of two population probabilities, using measuring instruments possibly subject to known error rates. The available experiments considered are to measure, for i = 1 or 2, the outcome of a trial on the population i without error, or with error, or with error with the possibility of checking either positive or negative responses. Given that the cost per observation depends on which experiment is carried…

误分类二项数据中比较两个概率的局部最优设计AI 解读

Optimal experimental designs are found for estimating the difference P1−P2 of two population probabilities, using measuring instruments possibly subject to known error rates. The available experiments considered are to measure, for i = 1 or 2, the outcome of a trial on the population i without error, or with error, or with error with the possibility of checking either positive or negative responses. Given that the cost per observation depends on which experiment is carried…

多元岭回归AI 解读

A multivariate linear regression model with q responses as a linear function of p independent variables is considered with a p × q parameter matrix B. The least-squares or normal-theory maximum likelihood estimate of B is deficient in that it takes no account of the ‘across regression’ correlations, and ignores the Stein effect. A remedy was offered by Brown & Zidek (1980) in the form of a multivariate ridge estimator. A richer class of estimators is obtained here by casting…

关于多元岭回归AI 解读

A multivariate linear regression model with q responses as a linear function of p independent variables is considered with a p × q parameter matrix B. The least-squares or normal-theory maximum likelihood estimate of B is deficient in that it takes no account of the ‘across regression’ correlations, and ignores the Stein effect. A remedy was offered by Brown & Zidek (1980) in the form of a multivariate ridge estimator. A richer class of estimators is obtained here by casting…

Summary Two alternative methods for dealing with the problem of missing observations in regression analysis are investigated. One is to discard all incomplete observations and to apply the ordinary least-squares technique only to the complete observations. The alternative is to compute the covariances between all pairs of variables, each time using only the observations having values of both variables, and to apply these covariances in constructing the system of normal…

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