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Showing 9 results for Subject:
Nabaz Esmaeilzadeh, Hooshang Talebi,
Volume 2, Issue 2 (2-2009)
Abstract
So far, the Plackett-Burman (PB) designs have been considered as saturated non-regular fractional factorial designs for screening purposes. Since introduction of the hidden projection of PB's by Wang and Wu (1995), the estimation capability of such projections onto a subset of factors has been investigated by many researchers. In this paper, by considering the search and estimation capability of a design, we introduce the post-stage search designs, using sparsity principle of factorial effects. That is, by the post-stage property of a design, we mean the capability of such a design in searching and estimating possible nonzero 3-factorial interactions along with estimation of the general mean, main effects and active 2-factor interaction effects, identified in the pre-stage. We show that a 12-runs PB projections onto 4 and 5 factors are post-stage search designs.
Sakineh Sadeghi, Iraj Kazemi,
Volume 3, Issue 1 (9-2009)
Abstract
Recently, dynamic panel data models are comprehensively used in social and economic studies. In fitting these models, a lagged response is incorrectly considered as an explanatory variable. This ad-hoc assumption produces unreliable results when using conventional estimation approaches. A principle issue in the analysis of panel data is to take into account the variability of experimental individual effects. These effects are usually assumed fixed in many studies, because of computational complexity. In this paper, we assume random individual effects to handle such variability and then compare the results with fixed effects. Furthermore, we obtain the model parameter estimates by implementing the maximum likelihood and Gibbs sampling methods. We also fit these models on a data set which contains assets and liabilities of banks in Iran.
Nasrollah Iranpanah,
Volume 3, Issue 2 (3-2010)
Abstract
Abstract: In many environmental studies, the collected data are usually spatially dependent. Determination of the spatial correlation structure of the data and prediction are two important problem in statistical analysis of spatial data. To do so, often, a parametric variogram model is fitted to the empirical variogram of the data by estimating the unknown parameters of the mentioned variogram. Since there are no closed formulas for the variogram parameters estimator, they are usually computed numerically. Therefore, the precision measures of the variogram parameters estimator and spatial prediction can be calculated using bootstrap methods. Lahiri (2003) proposed the moving block bootstrap method for spatial data, in which observations are divided into several moving blocks and resampling is done from them. Since, in this method, the presence of boundary observations in the resampling blocks have less selection chance than the other observations, therefore, the estimator of the precision measures would be biased. In this paper, revising the moving block bootstrap method, the separate block bootstrap method was presented for estimating the precision measures of the variogram parameters estimator and spatial prediction. Then its usage was illustrated in an applied example.
Haleh Nekoee, Hooshang Talebi,
Volume 4, Issue 2 (3-2011)
Abstract
Two designs, with N runs and k factors all with two levels are said to be isomorphic or equivalent if one is obtained from another by permuting rows, columns or/and changing the levels of one or more factors. When N and k increase the matter of isomorphic recognition of two designs will be complicated. Therefore it is essential to apply needed conditions which are able to recognize and separate non-isomorphic designs. It should be done in the least possible time. Majority of needed existed conditions in the literature review can’t meet the two objectives, maximum separation in minimum span, at the same time. In this paper, a new method has been used to present non-equivalent. This new method has been designed abased on choice and comparisons of one or some rows of design matrix. This new method hopefully has higher ability to recognize non-equivalence. Besides, the new method has lower calculation and therefore is able to determine non-equivalence of two designs.
Mohammad Hossein Aalamatsaz, Foroogh Mahpishanian,
Volume 5, Issue 1 (9-2011)
Abstract
There is a family of generalized Farlie-Gumbel-Morgenstern copulas, known as the semiparametric family, which is generated by a function called distribution-based generator. These generators have been studied typically for symmetric distributions in the literature. In this article, is proposed a method for asymmetric case which increases the flexibility of distribution-based generators and, thus, the model. In addition, a method for generalizing general generators is provided which can also be used to obtain more flexible distribution-based generators. Clearly, with more flexible generators more desirable models can be found to fit real data.
Ehsan Zamanzade,
Volume 7, Issue 1 (9-2013)
Abstract
In this paper, two new entropy estimators are proposed. Then, entropy-based tests of exponentiality based on our entropy estimators are introduced. Simulation results show that the proposed estimators and related goodness of fit tests have good performances in comparison with their leading competitors.
Ehsan Zamanzade,
Volume 8, Issue 2 (3-2015)
Abstract
In this paper, an improved mean estimator for unbalanced ranked set samples is proposed. The estimator is obtained by using the fact that distribution function of order statistics are stochastically ordered. Also, it is showed that this estimator is convergent and has better performance than its empirical counterpart in unbalanced ranked set samples.
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.
Mr. Mohsen Motavaze, Dr. Hooshang Talebi,
Volume 17, Issue 1 (9-2023)
Abstract
Production of high-quality products necessitates identifying the most influential factors, among many factors, for controlling and reducing quality variation. In such a setting, the factorial designs are utilized to determine the active factors with maximal information and model an appropriate relation between the factors and the variable of interest. In this regard, robust parameter designs dividing the factors to control- and noise factors are efficient methods for offline quality control for stabilizing the quality variation in the presence of the noise factors. Interestingly, this could be achieved through exploiting active control by noise interactions. One needs to experiment with numerous treatments to detect the active interaction effects. Search designs are suggested to save treatments, and a superior one is recommended among the appropriate ones. To determine the superior design, one needs a design criterion; however, the existing criteria could be more beneficial for robust parameter designs. In this paper, we proposed a criterion to rank the search designs and determine the superior one.
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