for Journals by Title or ISSN
for Articles by Keywords
Followed Journals
Journal you Follow: 0
Sign Up to follow journals, search in your chosen journals and, optionally, receive Email Alerts when new issues of your Followed Journals are published.
Already have an account? Sign In to see the journals you follow.
Journal Cover International Journal of Advanced Statistics and Probability
  [2 followers]  Follow
  This is an Open Access Journal Open Access journal
   ISSN (Print) 2307-9045 - ISSN (Online) 2307-9045
   Published by Science Publishing Corporation Homepage  [12 journals]
  • Stochastic renewal process model for maintenance (case study: thermal
           electricity generation in Sudan)

    • Authors: Mohammedelameen Qurashi, Ahamed Mohamed Abdalla Hamdi
      Pages: 11 - 15
      Abstract: The renewal process defines as a counting process where the times between the count renewals is a random variables and their distribution is identical. In the electricity generation machines there are spare parts replaced due to damage or expired and replacement process occur repeatedly and the renewal process of here assume that times between replacements are independent random variables and it has identical probability distribution. In this paper, renewal process model has applied on the time of fault for machine in Bahri Thermal Station for electricity generation, which is belong to the National Electricity Authority in Sudan during the period (2011-2015). Through the renewal process model estimation is clear that, the failure time (renewal) for the machines follow Weibull distribution with 2-parameters and when the time trend has been tested it is clear that no trend exist which mean that the renewal process represent Homogeneous Poisson Process (HPP), and the repair rate (renewal) is occurred constantly. In addition, the findings approve that whenever the repair rate (renewal) increase the mean time between failures (MTBF) increases too and this clear in machine no (6).
      PubDate: 2016-01-16
      Issue No: Vol. 4, No. 1 (2016)
  • Bayesian estimation of the shape parameter of generalized Rayleigh
           distribution under non-informative prior

    • Authors: Yakubu Aliyu, Abubakar Yahaya
      Pages: 1 - 10
      Abstract: A decade ago, two-parameter Burr Type X distribution was introduced by Surles and Padgett [14] which was described as Generalized Rayleigh Distribution (GRD). This skewed distribution can be used quiet effectively in modelling life time data. In this work, Bayesian estimation of the shape parameter of GRD was considered under the assumption of non-informative prior. The estimates were obtained under the squared error, Entropy and Precautionary loss functions. Extensive Monte Carlo simulations were carried out to compare the performances of the Bayes estimates with that of MLEs. It was observed that the estimate under the Entropy loss function is more stable than the estimates under squared error loss function, Precautionary loss function and MLEs.
      PubDate: 2015-12-28
      Issue No: Vol. 4, No. 1 (2015)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-2015