Read the full paper at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=49981 Read the full paper at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=49981 DOI: 10.4236/jamp.2014.210104 Author(s) Diana Bílková1* ABSTRACT Moments and cumulants are commonly used to characterize the probability distribution or observed data set. The use of the moment method of parameter estimation is also common in the construction of an appropriate parametric distribution for a certain data set. The moment method does not always produce satisfactory results. It is difficult to determine exactly what information concerning the shape of the distribution is expressed by its moments of the third and higher order. In the case of small samples in particular, numerical values of sample moments can be very different from the corresponding values of theoretical moments of the relevant probability distribution from which the random sample comes. Parameter estimations of the probability distribution made by the moment method are often considerably less accurate than those obtained using other methods, particularly in the case of small samples. The present paper deals with an alternative approach to the construction of an appropriate parametric distribution for the considered data set using order statistics.EWW140929GJR KEYWORDS L-Moments and TL-Moments of Probability Distribution, Sample L-Moments and TL-Moments, Probability Density Function, Distribution Function, Quantile Function, Order Statistics, Income Distribution
