|
|
|
|
|
 |
Search published articles |
 |
|
General users only can access the published articles
Showing 3 results for Subject:
Pegah Afshin, Bardia Panahbehagh, Amir Hossein Sanatpour,
Volume 13, Issue 2 (2-2020)
Abstract
We introduce a modified Poisson sampling, with a fixed lower bound of sample size. The design is a combination of simple random sampling and Poisson sampling. Simple random sampling is used to compensate for the lack of sample size from remaining elements in the finite population, after execution of a
Poisson sampling. At the first stage, the units are sampled independently with given inclusion probabilities. But in the second stage, inclusion probabilities are dependent to each other. Because it is important to know, which of the elements are selected in the first stage and which of them are remained. Some advantages of our design are: simple performance, controlling sample size, ability to perform the method of probability proportional to size. The simulations show that the design can dominate its rival design in probability proportional to size sampling.
Akram Kohansal, Nafiseh Alemohammad, Fatemeh Azizzadeh,
Volume 14, Issue 2 (2-2021)
Abstract
The Bayesian estimation of the stress-strength parameter in Lomax distribution under the progressive hybrid censored sample is considered in three cases. First, assuming the stress and strength are two random variables with a common scale and different shape parameters. The Bayesian estimations of these parameters are approximated by Lindley method and the Gibbs algorithm. Second, assuming the scale parameter is known, the exact Bayes estimation of the stress-strength parameter is obtained. Third, assuming all parameters are unknown, the Bayesian estimation of the stress-strength parameter is derived via the Gibbs algorithm. Also, the maximum likelihood estimations are calculated, and the usefulness of the Bayesian estimations is confirmed, in comparison with them. Finally, the different methods are evaluated utilizing the Monte Carlo simulation and one real data set is analyzed.
Meisam Moghimbeygi,
Volume 16, Issue 2 (3-2023)
Abstract
This article introduces a semiparametric multinomial logistic regression model to classify labeled configurations. In the regression model, the explanatory variable is the kernel function obtained using the power-divergence criterion. Also, the response variable was categorical and showed the class of each configuration. This semiparametric regression model is introduced based on distances defined in the shape space, and for this reason, the correct classification of shapes using this method has been improved compared to previous methods. The performance of this model has been investigated in the comprehensive simulation study. Two real datasets were analyzed using this article's method as an application. Finally, the method presented in this article was compared with the techniques introduced in the literature, which shows the proper performance of this method in classifying configurations.
|
|
|
|
|
|
|
|
|
|
|
