On size-biased generalized logarithmic series distribution

2009 ◽  
Vol 46 (2) ◽  
pp. 157-168
Author(s):  
Anwar Hassan ◽  
Khurshid Mir
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Bayesian Estimation ◽  
Recurrence Relation ◽  
Geometric Series ◽  
Monte Carlo Simulation Technique ◽  
Generalized Logarithmic Series Distribution ◽  
Logarithmic Series Distribution ◽  
Logarithmic Series ◽  
The Relationship

In this paper a size-biased generalized logarithmic series distribution (SBGLSD), a particular case of the weighted generalized logarithmic series distribution, taking the weights as the variate values is defined. The moments and recurrence relation of SBGLSD are obtained. We have also established the relationship between the moments of size-biased generalized logarithmic series distribution and size-biased generalized geometric series distribution (SBGGSD). Bayesian estimation of SBGLSD is discussed and a comparison is made with the generalized logarithmic series distribution (GLSD) by using the Monte Carlo simulation technique.

scholarly journals On Methods of Estimation for Generalized Logarithmic Series Distribution and Its Application to Counts of Red Mites on Apple Leaves

2021 ◽  
Vol 9 (3) ◽  
pp. 151-155
Author(s):  
Fehim J Wani ◽  
Keyword(s):  
Maximum Likelihood ◽  
Estimation Of Parameters ◽  
Chi Square ◽  
Estimation Techniques ◽  
Apple Leaves ◽  
Generalized Logarithmic Series Distribution ◽  
Logarithmic Series Distribution ◽  
Logarithmic Series ◽  
Extra Parameter

The Generalized Logarithmic Series Distribution (GLSD) adds an extra parameter to the usual logarithmic series distribution and was introduced by Jain and Gupta (1973). This distribution has found applications in various fields. The estimation of parameters of generalized logarithmic series distribution was studied by the methods of maximum likelihood, moments, minimum chi square and weighted discrepancies. The GLSD was fitted to counts of red mites on apple leaves and it was observed that all the estimation techniques perform well in estimating the parameters of generalized logarithmic series distribution but with varying degree of non-significance.

On Using Monte Carlo Simulations for Project Risk Management

2016 ◽  
pp. 150-173
Author(s):  
Cristiana Tudor ◽  
Maria Tudor
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Decision Makers ◽  
Project Schedule ◽  
Project Risk ◽  
Monte Carlo Simulation Technique ◽  
Basic Probability ◽  
The Cost

This chapter covers the essentials of using the Monte Carlo Simulation technique (MSC) for project schedule and cost risk analysis. It offers a description of the steps involved in performing a Monte Carlo simulation and provides the basic probability and statistical concepts that MSC is based on. Further, a simple practical spreadsheet example goes through the steps presented before to show how MCS can be used in practice to assess the cost and duration risk of a project and ultimately to enable decision makers to improve the quality of their judgments.

Influence of the Back-Jump Correlations on the Fermi-Dirac Statistics

1991 ◽  
Vol 02 (01) ◽  
pp. 227-231
Author(s):  
T. BARSZCZAK ◽  
R. KUTNER
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Technique ◽  
Monte Carlo Simulation Technique

The influence of the essential Bardeen-Herring back-jump correlations on the Fermi-Dirac statistics is studied by the Monte Carlo simulation technique and semi-analytically.

Exact cash-account deflator for the G2++ model

2020 ◽  
Vol 07 (01) ◽  
pp. 2050009
Author(s):  
Francesco Strati ◽  
Luca G. Trussoni
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Insurance Industry ◽  
Random Variable ◽  
Time Span ◽  
Discrete Random Variable ◽  
Variable Formula ◽  
Monte Carlo Simulation Technique ◽  
Insurance Contracts ◽  
Embedded Options

In this paper, we shall propose a Monte Carlo simulation technique applied to a G2++ model: even when the number of simulated paths is small, our technique allows to find a precise simulated deflator. In particular, we shall study the transition law of the discrete random variable :[Formula: see text] in the time span [Formula: see text] conditional on the observation at time [Formula: see text], and we apply it in a recursive way to build the different paths of the simulation. We shall apply the proposed technique to the insurance industry, and in particular to the issue of pricing insurance contracts with embedded options and guarantees.

EFFECTS OF SURFACE LOSS IN REELS SPECTRA OF SILVER

1997 ◽  
Vol 04 (05) ◽  
pp. 955-958 ◽  
Author(s):  
K. TÖKÉSI ◽  
L. KÖVÉR ◽  
D. VARGA ◽  
J. TÓTH ◽  
T. MUKOYAMA
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Energy Loss ◽  
Electron Scattering ◽  
Primary Beam ◽  
Monte Carlo Simulation Technique ◽  
Surface Normal ◽  
Beam Incidence ◽  
Polycrystalline Silver ◽  
Electron Energy Loss

The energy distribution of the electrons backscattered in the direction of the surface normal of polycrystalline silver samples was studied using reflected electron energy loss spectroscopy (REELS) at 200 eV and 2 keV primary beam energies. For modeling the electron scattering processes, the Monte Carlo simulation technique was used and the REELS spectra were calculated at various (25°, 50° and 75°, with respect to the surface normal) angles of primary beam incidence. The effects of the surface energy loss process in REELS are evaluated from the comparison of the experimental and simulated spectra.

Sign in / Sign up

Export Citation Format

Share Document