论文检索

线性回归模型的验证AI 解读

A new test is proposed in order to verify that a regression function, say $g$, has a prescribed (linear) parametric form. This procedure is based on the large sample behavior of an empirical $L^2$-distance between $g$ and the subspace $U$ spanned by the regression functions to be verified. The asymptotic distribution of the test statistic is shown to be normal with parameters depending only on the variance of the observations and the $L^2$-distance between the regression…

相似分布的非参数验证与拟合优度评估AI 解读

Summary In this paper the problem of assessing the similarity of two cumulative distribution functions F and G is considered. An asymptotic test based on an α-trimmed version of Mallows distance Γα(F, G) between F and G is suggested, thus demonstrating the similarity of F and G within a preassigned Γα(F, G) neighbourhood at a controlled type I error rate. The test proposed is applied to the validation of goodness of fit and for the nonparametric assessment of bioequivalence.…

生物等效性问题的无偏检验AI 解读

It is shown that the standard two one-sided tests procedure for bioequivalence is a biased test. Better tests exist. In this paper, an unbiased $\alpha$-level test and other tests which are uniformly more powerful than the two one-sided tests procedure are constructed. Its power can be noticeably larger than that of the $\alpha$-level two one-sided tests procedure.

非参数析因设计中的Box型近似AI 解读

Linear rank statistics in nonparametric factorial designs are asymptotically normal and, in general, heteroscedastic. In a comprehensive simulation study, the asymptotic chi-squared law of the corresponding quadratic forms is shown to be a rather poor approximation of the finite-sample distribution. Motivated by this problem, we propose simple finite-sample size approximations for the distribution of quadratic forms in factorial designs under a normal heteroscedastic error…

Lehmann 备择下的等效性检验与区间检验AI 解读

Equivalence and interval tests for Lehmann's alternative that extend the well-known Savage test for one-sided hypotheses are proposed. The proposed tests are shown to be unbiased with a strictly unimodal power function, provided the sample sizes in both treatment groups are equal. By means of a numerical investigation of the bias in the case of unequal sample sizes that are not too far apart, the suggested tests still turn out to provide practicable solutions. Because the…