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Showing 5 results for Efficiency
Mehrdad Niaparast, Sahar Mehr-Mansour,
Volume 4, Issue 1 (9-2010)
Abstract
The main part of optimal designs in the mixed effects models concentrates on linear models and binary models. Recently, Poisson models with random effects have been considered by some researchers. In this paper, an especial case of the mixed effects Poisson model, namely Poisson regression with random intercept is considered. Experimental design variations are obtained in terms of the random effect variance and indicated that the variations depend on the variance parameter. Using D-efficiency criterion, the impression of random effect on the experimental setting points is studied. These points are compared with the optimal experimental setting points in the corresponding model without random effect. We indicate that the D-efficiency depends on the variance of random effect.
Hamed Mohamadghasemi, Ehsan Zamanzade, Mohammad Mohammadi,
Volume 10, Issue 1 (8-2016)
Abstract
Judgment post stratification is a sampling strategy which uses ranking information to give more efficient statistical inference than simple random sampling. In this paper, we introduce a new mean estimator for judgment post stratification. The estimator is obtained by using ordering observations in post strata. Our simulation results indicate that the new estimator performs better than its leading competitors in the literature.
Abouzar Bazyari, Narges Mousavi,
Volume 12, Issue 2 (3-2019)
Abstract
In this article, we wish to find and select appropriate estimators for statistical population density function using line transect sampling in the present of detection functions with light and heavy tailed distributions. Also it is shown that how the type of detection function could be effective in selection of the best estimator and then we propose a unbiased estimators that has the lower variance than the existed estimators. the simulation results show that if detection functions have heavy tailed distribution, then the new estimators have least mean square error.
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.
Ali Najafi Majid Abadi, Nader Nematollahi,
Volume 14, Issue 2 (2-2021)
Abstract
Judgment post-stratification is a method of using additional information of ranking in the simple random sampling, to increase the efficiency of the estimators of population parameters. In this paper, we use judgment post-stratification instead of simple random sampling in stratums of stratified sampling, and present new estimators for population mean. Then, we compare the proposed estimators with random stratified mean estimator by using a simulation study. The simulation results show that the proposed estimators perform better than the random stratified mean estimator in most of the cases.
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