A More Robust t-Test
将极值理论与正态近似结合，构造了一种更稳健的t检验，在小样本下比现有方法更好地控制检验大小，适用于均值推断、两总体均值比较及聚类标准误的线性回归系数推断。
Abstract This paper combines extreme value theory for the smallest and largest k observations for some given k>1 with a normal approximation for the average of the remaining observations to construct a more robust alternative to the usual t-test. The new test is found to control size much more successfully in small samples compared to existing methods. This holds for the canonical inference for the mean problem based on an i.i.d. sample, but also when comparing two population means and when conducting inference about linear regression coefficients with clustered standard errors.