A Robust Wald-Type Test for Testing the Equality of Two Means from Log-Normal Samples
Methodology and Computing in Applied Probability
The log-normal distribution is one of the most common distributions used for modeling skewed and positive data. It frequently arises in many disciplines of science, specially in the biological and medical sciences. The statistical analysis for comparing the means of two independent log-normal distributions is an issue of significant interest. In this paper we present a robust test for this problem. The unknown parameters of the model are estimated by minimum density power divergence estimators (Basu et al. Biometrika 85(3):549–559 1998). The robustness as well as the asymptotic properties of the proposed test statistics are rigorously established. The performance of the test is explored through simulations and real data analysis. The test is compared with some existing methods, and it is demonstrated that the proposed test outperforms the others in the presence of outliers.
Basu, Ayanendranath; Mandal, Abhijit; Martín, Nirian; and Pardo, Leandro, "A Robust Wald-Type Test for Testing the Equality of Two Means from Log-Normal Samples" (2019). Journal Articles. 916.