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一类独立性非线性秩检验的渐近正态性
Asymptotic normality of a class of nonlinear rank statistics to test the null hypothesis of total independence of an $m$-variate population is proved. The rank statistics are generated from $2m$-variate square integrable functions such that they are symmetric and nondegenerate. Some results under contiguous alternatives are also given.
无限列联表中的独立性检验
This paper deals with distribution-free tests for independence under the constraint that the population has a bivariate discrete distribution. The locally most powerful conditional test, given the marginal empirical distributions, is derived. The unconditional asymptotic distribution of the conditional test statistic standardized by the conditional mean and variance is also given under the hypothesis of independence and under contiguous alternatives. Furthermore, some…
Conditions to ensure the asymptotic normality of rank statistics having a scores generating function with finitely many jumps are obtained. These conditions are derived by studying rank statistics with a scores generating function $J$ such that $J(u) = 1$ or $0$ as $u \geqq s$ or $u < s$ for a fixed $s, 0 < s < 1$. No differentiability conditions are imposed on the underlying distribution functions at the jump points of the scores generating function.
删失数据独立性检验的局部最强秩检验
In this paper locally most powerful rank tests for independence with censored data for a one-parameter family are derived. The statistic derived has discrete score functions and its asymptotic normality follows from a theorem essentially given by Ruymgaart [6].