arXiv subjects

Sudheesh Kattumannil

Publications and source records attributed to Sudheesh Kattumannil.

3 recordsLinked to original sources

arXiv2024

Jackknife empirical likelihood ratio test for log symmetric distribution using probability weighted moments

Log symmetric distributions are useful in modeling data which show high skewness and have found applications in various fields. Using a recent characterization for log symmetric distributions, we propose a goodness of fit test for testing log symmetry. The asymptotic distributions of the test statistics under both null and alternate distributions are obtained. As the normal-based test is difficult to implement, we also propose a jackknife empirical likelihood (JEL) ratio test for testing log symmetry. We conduct a Monte Carlo Simulation to evaluate the performance of the JEL ratio test. Finally, we illustrated our methodology using different data sets.

stat.ME↗

arXiv2023

A new goodness of fit test for normal distribution based on Stein's characterization

In this paper, we develop a simple non-parametric test for testing normal distribution based on the distance between empirical zero-bias transformation and empirical distribution. The asymptotic properties of the test statistic are studied. The finite sample performance of the proposed test is evaluated through a Monte Carlo simulation study. The power of our test is compared with several other tests for normality. We illustrate the test procedure using two real data sets. We also develop a jackknife empirical likelihood ratio test for standard normal distribution.

math.ST↗

arXiv2022

Relationships between cumulative entropy/extropy, Gini mean difference and probability weighted moments

In this work, we establish a connection between the cumulative residual entropy and the Gini mean difference (GMD). Some relationships between the extropy and the GMD, and the truncated GMD and dynamic versions of the cumulative extropy are also established. We then show that several entropy and extropy measures discussed here can be brought into the framework of probability weighted moments, which would enable us to find estimators of these measures.

math.ST↗