Goodness-of-Fit Test: Khmaladze Transformation vs Empirical Likelihood

This paper compares two asymptotic distribution free methods for goodness-of-fit test of one sample of location-scale family: Khmaladze transformation and empirical likelihood methods. The comparison is made from the perspective of empirical level and power obtained from simulations. When testing for normal and logistic null distributions, we try various alternative distributions and find that Khmaladze transformation method has better power in most cases. R-package which was used for the simulation is available online. See section 5 for the detail

Factors influencing CDM locations in China

Environmental Economics and Policy,

A Fast Algorithm for Implementation of Koul's Minimum Distance Estimators and Their Application to Image Segmentation

Minimum distance estimation methodology based on an empirical distribution function has been popular due to its desirable properties including robustness. Even though the statistical literature is awash with the research on the minimum distance estimation, the most of it is confined to the theoretical findings: only few statisticians conducted research on the application of the method to real world problems. Through this paper, we extend the domain of application of this methodology to various applied fields by providing a solution to a rather challenging and complicated computational problem. The problem this paper tackles is an image segmentation which has been used in various fields. We propose a novel method based on the classical minimum distance estimation theory to solve the image segmentation problem. The performance of the proposed method is then further elevated by integrating it with the ``segmenting-together" strategy. We demonstrate that the proposed method combined with the segmenting-together strategy successfully completes the segmentation problem when it is applied to the complex, real images such as magnetic resonance images

Application of the Cramer-von Mises type optimization to a binomial distribution

This paper proposes the novel estimator for the success probability parameter of a binomial distribution. To that end, we use the Cramer-von Mises type optimization methodology which has been popular for the parameter estimation in continuous distributions. Upon obtaining the estimator, desirable properties of the proposed estimation method such as asymptotic distribution and robustness are rigorously investigated. Simulation studies demonstrate that the proposed estimator compares favorably with other well-celebrated estimators