Asympptotic powers of one-and two-sample rank tests against location-alternatives including contamination

I } Rate O~ Consistency of One Sample Tests of Location Rate of Consistency of One Sample Tests of Location

Let Xl' ""Xn be a sample from a population with continuous distribution function F(x -8) such that F(x) +F(-x) =1 and o 80 cannot tend to 1 as 8 -80 -+ 00 more than n times faster than the tails of F tend to O. Some standard as well as robust tests are considered with respect to this rate of conver...

Max-type rank tests, U-tests, and adaptive tests for the two-sample location problem - An asymptotic power study

For the two-sample location problem we first consider two types of tests, linear rank tests with various scores, but also some tests based on U-statistics. For both types we construct adaptive tests as well as max-type tests and investigate their asymptotic and finite power properties. It turns out that both the adaptive tests have larger asymptotic power than the max-type tests. For small samp...

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...

Power of Rank Tests Against Location Shift Alternative

We study the power of some exceedance rank tests against location shift and Lehman alternatives for distributions from the uniform, normal, exponential, gamma, and lognormal families. We give the corresponding power functions, obtained by Monte Carlo simulation and discuss the relative merits of the tests.

Symmetrized Approximate Score Rank Tests for the Two-sample Case