Length-biased Weighted Maxwell Distribution

Abstract

The concept of length-biased distribution can be employed in development of proper models for life-time data. In this paper, we develop the length-biased form of Weighted Maxwell distribution (WMD). We study the statistical properties of the derived distribution including moments, moment generating function, hazard rate, reverse hazard rate, Shannon entropy and estimation of parameters.

Characterization of Phani Distribution Based on Generalized Order Statistics

“…Another name of this distribution is Wiebull-Uniform distribution introduced by Bourguignon et al [1]. Modi and Gill [2] provide additional information on this distribution and its applications.…”

A New Version of Prakaamy Distribution with Properties and Applications

“…Mudasir and Ahmad (2018), discussed the characterization and estimation of length biased Nakagami distribution. Modi and Gill (2015), discussed the length biased weighted Maxwell distribution. Dey et al (2015) discussed weighted exponential distribution with its properties and different methods of estimation.…”

Marshall–Olkin Length-Biased Maxwell Distribution and Its Applications

“…For this reason, some extensions were developed by applying diverse notorious schemes. We may refer to those presented in [9][10][11][12]. In particular, Modi [11] and Saghir [12] proposed to extend the M distribution through the use of the length-biased scheme, introducing the length-biased Maxwell (LBM) distribution with parameter α > 0.…”

“…It is proven that the LBM model offers an interesting alternative to the M model on certain aspects, while keeping the simplicity of one-parameter adjustment. The merits of the LBM distribution are discussed in more detail in [11][12][13][14]. For these reasons, the LBM distribution is a candidate to be at the top of the list of useful one-parameter lifetime distributions, alongside the exponential distribution, Rayleigh distribution, Maxwell distribution, Lindley distribution by [15], Shanker distribution by [16], length-biased exponential (LBE) distribution introduced by [17] and the distribution for instantaneous failures proposed by [18], to name a few.…”