A New Generalization of Pranav Distribution Using Weighting Technique

Anwar Hassan¹ , Mohd Altaf Dar² , Peer Bilal Ahmad³ , Bilal Ahmad Para⁴

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.1 , pp.25-32, Feb-2019

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v6i1.2532

Online published on Feb 28, 2019

Copyright © Anwar Hassan, Mohd Altaf Dar, Peer Bilal Ahmad, Bilal Ahmad Para . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

574 Views    555 Downloads    127 Downloads

Abstract :
In this paper, we introduce a new generalization of Pranav distribution called as Weighted Pranav Distribution (WPD). The statistical properties of this distribution are derived and the model parameters are estimated by maximum likelihood estimation. Finally, an application to real data set is presented to examine the model performance.

Key-Words / Index Term :
Pranav Distribution, Weighting Technique, Structural Properties and Maximum Likelihood Estimation

References :
[1]. Akaike, H. (1976). A new look at the statistical model identification. IEEE Trans. Autom.Control , 19 , 716–723.
[2]. Ayesha, A. (2017). Size Biased Lindley Distribution and Its Properties a Special Case of Weighted Distribution. Applied Mathematics, 08(06), 808-819.
[3]. Fisher, R.A. (1934). The effects of methods of ascertainment upon the estimation of frequencies. Ann. Eugenics, 6, 13-25.
[4]. Hassan, A., Dar, S. A., & Para, B. A. (2018), On three Parameter Weighted Quasi Akash Distribution: Properties and Applications, IOSR Journal of Engineering (IOSRJEN), 08(11), 01-10.
[5]. Hassan, A., Wani, S.A., & Para, B. A. (2018). On three Parameter Weighted Quasi Lindley Distribution: Properties and Applications, International Journal of Scientific Research in Mathematical and Statistical Sciences, 5(5), pp. 210-224 DOI: https://doi.org/10.26438/ijsrmss/v5i5.210224.
[6]. Hinkley, D. On quick choice of power transformation. Applied Statistics, 67-69 (1977).
[7]. Jing (2010) Weighted Inverse Weibull and Beta-Inverse Weibull Distribution, Georgia Southern University Digital Commons@Georgia Southern.
[8]. Para, B.A., & Jan, T. R. (2018). On three Parameter Weighted Pareto Type II Distribution: Properties and Applications in Medical Sciences. Applied Mathematics and Information Sciences Letters, 6 (1), 13-26.
[9]. Rao, C.R (1965). On discrete distributions arising out of method of ascertainment, in classical and Contagious Discrete, G.P. Patil .ed ;Pergamon Press and Statistical publishing Society, Calcutta, pp-320-332.
[10]. Schwarz, G. (1987). Estimating the dimension of a model. Ann. Stat., 5, 461–464.
[11]. Shanker, R., & Shukla, K. K. (2018). A Generalized Size-Biased Poisson-Lindley Distribution and Its Applications to Model Size Distribution of Freely-Forming Small Group. Journal of Scientific Research, 10(2), 145-158.