Testing the Population Coefficient of Variation

Testing the Population Coefficient of Variation

Modified signed log-likelihood test for the coefficient of variation of an inverse Gaussian population

In this paper, we consider the problem of two sided hypothesis testing for the parameter of coefficient of variation of an inverse Gaussian population. An approach used here is the modified signed log-likelihood ratio (MSLR) method which is the modification of traditional signed log-likelihood ratio test. Previous works show that this proposed method has third-order accuracy whereas the traditi...

Selecting the Normal Population with the Smallest Coefficient of Variation

We consider the problem of selecting the normal population with the smallest coefficient of variation, which is a natural goal when the means as well as the variances of the populations are unknown and unequal. The indifference-zone approach (Bechhofer 1954) to this problem has been previously considered by Choi, Jeon and Kim (1982). We review their selection procedure and provide tables of sam...

Testing for a constant coefficient of variation in nonparametric regression

In the common nonparametric regression model Yi = m(Xi) + σ(Xi) i we consider the problem of testing the hypothesis that the coefficient of the scale and location function is constant. The test is based on a comparison of the observations Yi/σ̂(Xi) with their mean by a smoothed empirical process, where σ̂ denotes the local linear estimate of the scale function. We show weak convergence of a cente...

Robust tests for testing the parameters of a normal population

This article aims to provide a simple robust method to test the parameters of a normal population by using the new diagnostic tool called the “Forward Search” (FS) method. The most commonly used procedures to test the mean and variance of a normal distribution are Student’s t test and Chi-square test, respectively. These tests suffer from the presence of outliers. We introduce the FS version of...