My research focuses on the statistical inference and computationally intensive methods in statistics. One area in which computational power is particularly useful is in the use of Monte Carlo approximations which can replace intractable calculations or asymptotic arguments. The use of these methods, however, requires reliable software and good diagnostics for when there may be problems with the underling assumptions. In my Ph.D. work I examined diagnostics for Edgeworth approximate inference through Monte Carlo methods. Most of my researches are based on order statistics and record values. These kind of data have a lot of applications in different aspects such as reliability and environmental studies. Finite mixtures are also one of my interest filed of research. My latest area of research is in Monte Carlo implementations of the EM algorithm for likelihood inference based on finite mixture models.