  In order to improve the already existing models that are used extensively in
bio sciences and applied sciences research, a new class of Weighted Power
function distribution (WPFD) has been proposed with its various properties and
different modifications to be more applicable in real life. We have provided
the mathematical derivations for the new distribution including moments,
incomplete moments, conditional moments, inverse moments, mean residual
function, vitality function, order statistics, mills ratio, information
function, Shannon entropy, Bonferroni and Lorenz curves and quantile function.
We have also characterized the WPFD, based on doubly truncated mean. The aim of
the study is to increase the application of the Power function distribution.
The main feature of the proposed distribution is that there is no induction of
parameters as compare to the other generalization of the distributions, which
are complexed having many parameters. We have used R programming to estimate
the parameters of the new class of WPFD using Maximum Likelihood Method (MLM),
Percentile Estimators (P.E) and their modified estimators. After analyzing the
data, we conclude that the proposed model WPFD performs better in the data sets
while compared to different competitor models.
