In the present study an effort has been made to find out the best fitting distribution of wind speed data recorded in Islamabad on a daily basis for the years 2001 to 2003. For this purpose two parameter Gamma, Weibull, Lognormal, Rayleigh, three parameter Burr and Frechet distributions are fitted to data and parameters for each distribution are estimated using the Maximum Likelihood (ML) metho...

Microphysics parameterization plays an important role in Numerical Weather Prediction (NWP). Most NWP models, including the Weather Research Forecast (WRF) model, use single-moment (bulk water) schemes derived based on an assumption of the exponential distribution with a fixed intercept parameter for raindrop size distributions (DSDs) such as the Marshall-Palmer (M-P) DSD model (Marshall and Pa...

The aim of this article is to study the estimation of the parameter of ЭРланга distribution based on complete samples. The Bayes estimators of the parameter of ЭРланга distribution are obtained under three different loss functions, namely, weighted square error loss, squared log error loss and entropy loss functions by using conjugate prior inverse Gamma distribution. Then the minimax estimator...

The study aims to estimate the parameter of the Generalized Pareto Distribution under Type II censored samples. The Bayes Estimators have been derived under a class of informative and non-informative Priors using different symmetric and asymmetric Loss functions. The Credible Intervals, Highest Posterior Density (HPD) intervals, Posterior Predictive Distributions and Posterior Predictive Interv...

Understanding the subgraph distribution in random networks is important for modeling complex systems. In classic Erdos networks, which exhibit a Poissonian degree distribution, the number of appearances of a subgraph G with n nodes and g edges scales with network size as approximately N(n-g). However, many natural networks have a non-Poissonian degree distribution. Here we present approxima...

A weighted version of Lindley Distribution has been presented in this paper, offering a new single-parameter lifetime distribution. The expressions for the moment generating function (mgf), cumulant (cgf), characteristic function, moments, reliability and hazard rate functions distribution have also obtained. Tierney Kadane approximation method used to derive Bayes estimators its parameter (θ),...

This paper considers conditional Gaussian networks. The parameters in the network are learned by using conjugate Bayesian analysis. As conjugate local priors, we apply the Dirichlet distribution for discrete variables and the Gaussian-inverse gamma distribution for continuous variables, given a configuration of the discrete parents. We assume parameter independence and complete data. Further, t...

In this paper, Bayesian and E – Bayesian method of estimation are proposed for estimating the parameter of Rayleigh distribution. The Bayes estimate of the parameter is derived under the assumption that the prior distribution is informative i.e. gamma prior using Linex loss function. Further, comparison between the E-Bayes estimators with the associated Bayes estimators have been carried out th...

‎the aim of this paper is to study distribution of ratios of generalized order statistics from pareto distribution. parameter estimation of pareto distribution based on generalized order statistics and ratios of them have been obtained. inferences using method of moments and unbiased estimator have been obtained to develop point estimations. consistency of unbiased estimator has been illustrate...