Estimation of Parameters of the Generalized Geometric Distribution Using Ranked Set Sampling

Biometrics ◽  
1996 ◽  
Vol 52 (2) ◽  
pp. 685 ◽  
Author(s):  
Dinesh S. Bhoj ◽  
M. Ahsanullah
Keyword(s):  
Geometric Distribution ◽  
Ranked Set Sampling ◽  
Estimation Of Parameters ◽  
Generalized Geometric Distribution

On zero-distorted generalized geometric distribution

2015 ◽  
Vol 45 (18) ◽  
pp. 5427-5442 ◽  
Author(s):  
D. V. S. Sastry ◽  
Deepesh Bhati ◽  
R. N. Rattihalli ◽  
E. Gómez-Déniz
Keyword(s):  
Geometric Distribution ◽  
Generalized Geometric Distribution

scholarly journals Compound Extended Geometric Distribution and Some of Its Properties

2019 ◽  
Vol 8 (3) ◽  
pp. 96
Author(s):  
Sandhya E ◽  
Latha C M
Keyword(s):  
Method Of Moments ◽  
Geometric Distribution ◽  
Estimation Of Parameters

The compound extended geometric distribution (CEG distribution) is introduced. Probabilities of the distribution are evaluated. Certain statistical, distributional and reliability properties are discussed. The distribution is characterized using S- function. An AR(1) process corresponding to CEG distribution is derived. Simulation and estimation of parameters are done using the method of moments and BHHJ estimation.

Estimation of ordinal population with multi-observer ranked set samples using ties information

2021 ◽  
pp. 096228022110259
Author(s):  
Amirhossein Alvandi ◽  
Armin Hatefi
Keyword(s):  
Likelihood Estimation ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Training Data ◽  
Estimation Of Parameters ◽  
Multiple Sources ◽  
Study Variable ◽  
Regression Approach ◽  
Ranking Information ◽  
Representative Samples

In many surveys, we often deal with situations where measuring the study variable is expensive; however, there are easy-to-measure characteristics which can be used as ranking information to obtain more representative samples from the population. Ranked set sampling is successfully employed in these cases as an alternative to commonly used simple random sampling. When the data is ordinal categorical, it is common to apply the ordinal logistic regression approach to ranked set sampling data for the estimation of parameters. This technique first depends on the information of training data. Besides, one is not capable of using the ranking information in the estimation process. In this paper, we propose a ranked set sampling scheme in which ranking information from multiple sources can be combined and incorporated efficiently into both data collection and estimation. The ranked set sampling data is used for non-parametric and maximum likelihood estimation of ordinal categorical population. Through extensive simulation studies, the performance of estimators is evaluated. The methods are finally applied to analyze bone disorder data and obesity data.

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