Robustness of One-and Two-Sample Rank Tests Against Gross Errors

One- and two-sample t tests.

The R function t.test() can be used to perform both one and two sample t-tests on vectors of data. The function contains a variety of options and can be called as follows: > t.test(x, y = NULL, alternative = c("two.sided", "less", "greater"), mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95) Here x is a numeric vector of data values and y is an optional numeric vector of data values...

Symmetrized Approximate Score Rank Tests for the Two-sample Case

One-Sample Logrank Tests

Introduction This module computes the sample size and power of the one-sample logrank test which is used to compare the survival curve of a single treatment group to that of a historic control. Such is often the case in clinical phase-II trials with survival endpoints. Accrual time, follow-up time, and hazard rates are parameters that can be set. Several authors have presented sample size formu...

Rank reduction direction-of-arrival estimators with an improved robustness against subarray orientation errors

The problem of signal direction-of-arrival (DOA) estimation in partly calibrated arrays composed of multiple subarrays with unknown intersubarray parameters and imperfectly known subarray orientations is studied. The recently developed spectral and root versions of the rank reduction estimator (RARE) are applicable to scenarios where no intersubarray calibration is available, but unfortunately,...

Efficiency and Optimality Properties of a Class of k-Sample Rank Tests Against Trend

A class of k-sample rank tests is considered for testing the hypothesis Ho: F i (x) = F (x), i < k against the hypothesis H I : F i (x) = F (x Oi), 01 < 02 < 9 9 9 < O k, 01 4: O k. These tests are based on a rank statistic S = ~ d i (-~) ~ a n (R_i,r 3') in which -~= (nl . . . . . nk) . I fn -Xni, n -* ~i and n 1/20i , n ~ 0 i it is shown that Eff ((~, d ~, 3') I (F, b-')) = Eff (~ IF) Eff (~ ...