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Parameter Estimation of Power Function Distribution with TL-moments
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Accurate estimation of parameters of a probability distribution is of immense importance in statistics. Biased and imprecise estimation of parameters can lead to erroneous results. Our focus is to estimate the parameter of Power function distribution accurately because this density is now widely used for modelling various types of data. In this study, L-moments, TL-moments, LL-moments and LH-moments of Power function distribution are derived. In addition, the coefficient of variation, skewness and kurtosis are obtained by method of moments, L-moments and TL-moments. Parameters of the density are estimated using linear moments and compared with method of moments and MLE on the basis of bias, root mean square error and coefficients through simulation study. L-moments proved to be superior for the parameter estimation and this conclusion is equally true for different parametric values and sample size.
Universidad Nacional de Colombia
Title: Parameter Estimation of Power Function Distribution with TL-moments
Description:
Accurate estimation of parameters of a probability distribution is of immense importance in statistics.
Biased and imprecise estimation of parameters can lead to erroneous results.
Our focus is to estimate the parameter of Power function distribution accurately because this density is now widely used for modelling various types of data.
In this study, L-moments, TL-moments, LL-moments and LH-moments of Power function distribution are derived.
In addition, the coefficient of variation, skewness and kurtosis are obtained by method of moments, L-moments and TL-moments.
Parameters of the density are estimated using linear moments and compared with method of moments and MLE on the basis of bias, root mean square error and coefficients through simulation study.
L-moments proved to be superior for the parameter estimation and this conclusion is equally true for different parametric values and sample size.
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