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分层抽样下正态判别与逻辑回归的最优样本分配AI 解读

For two multivariate normal populations with a common covariance matrix and stratified sampling, we consider two methods of estimation—Fisher's linear discriminant function and logistic regression. Intuition suggests that taking half of the observations from each population is a reasonable design choice. Based on minimizing the expected error regret, asymptotic optimal sample allocations are found. The results indicate that the differences in the expected error regret for…

分层抽样下正态判别与Logistic回归的最优样本配置AI 解读

For two multivariate normal populations with a common covariance matrix and stratified sampling, we consider two methods of estimation—Fisher's linear discriminant function and logistic regression. Intuition suggests that taking half of the observations from each population is a reasonable design choice. Based on minimizing the expected error regret, asymptotic optimal sample allocations are found. The results indicate that the differences in the expected error regret for…

随机变量之和中项数的估计AI 解读

Fixed sample size and sequential procedures are investigated for estimating the parameter n from observations on a sequence of iid random variables each of which is the sum of n iid random variables with known mean and variance.