Tietjen-Moore Test for Outliers

Tietjen-Moore test is a function to test for k outliers in both tails.

tmTest(data, k)

Arguments

data

A list of univariate data.

k

The number of suspected outliers.

Value

An object that shows the result of the hypothesis.

Details

The Tietjen-Moore test is used to detect multiple outliers in a univariate data set that follows an approximately normal distribution. If testing for a single outlier, the Tietjen-Moore test is equivalent to the Grubbs' test.

Examples

x <- c(-1.40, -0.44, -0.30, -0.24, -0.22, -0.13, -0.05, 0.06, 0.10, 0.18, 0.20, 0.39, 0.48, 0.63, 1.01)
k <- 3
tmTest(data=x,k=k)

#> 
#> Results of  Tietjen Moore Test 
#> --------------------------
#> H_0:   there are no outliers in the data 
#> H_a:   the  3  most extreme points are outliers 
#> 
#> Test statistic:  E_k =  0.2064704 
#> Significance level:  a =  0.05  
#> Critical value for lower tail:   0.2122447  
#> Critical region: Reject H_0 if E_k <  0.2122447