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Showing 11 results for Variance
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.
Ghadi Mahdavi, Zahra Majedi,
Volume 4, Issue 1 (9-2010)
Abstract
The GARCH(1,1) and GARCH(1,1)-t models lead to highly volatile quantile forecasts, while historical simulation, Variance–Covariance, adaptive generalized Pareto distribution and non-adaptive generalized Pareto distribution models provide more stable quantile forecasts. In general, GARCH(1,1)-t, generalized Pareto distribution models and historical simulation are preferable for most quantiles.
Mehdi Shams, Mehdi Emadi, Naser Reza Arghami,
Volume 5, Issue 2 (2-2012)
Abstract
In this paper the class of all equivariant is characterized functions. Then two conditions for the proof of the existence of equivariant estimators are introduced. Next the Lehmann's method is generalized for characterization of the class of equivariant location and scale function in terms of a given equivariant function and invariant function to an arbitrary group family. This generalized method has applications in mathematics, but to make it useful in statistics, it is combined with a suitable function to make an equivariant estimator. This of course is usable only for unique transitive groups, but fortunately most statistical examples are of this sort. For other group equivariant estimators are directly obtained.
Amal Saki Malehi, Ebrahim Hajizadeh, Kambiz Ahmadi,
Volume 6, Issue 1 (8-2012)
Abstract
The survival analysis methods are usually conducted based on assumption that the population is homogeneity. However, generally, this assumption in most cases is unrealistic, because of unobserved risk factors or subject specific random effect. Disregarding the heterogeneity leads to unbiased results. So frailty model as a mixed model was used to adjust for uncertainty that cannot be explained by observed factors in survival analysis. In this paper, family of power variance function distributions that includes gamma and inverse Gaussian distribution were introduced and evaluated for frailty effects. Finally the proportional hazard frailty models with Weibull baseline hazard as a parametric model used for analyzing survival data of the colorectal cancer patients.
Abdollah Safari, Ali Sharifi, Hamid Pezeshk, Peyman Nickchi, Sayed-Amir Marashi, Changiz Eslahchi,
Volume 6, Issue 2 (2-2013)
Abstract
There are several methods for inference about gene networks, but there are few cases in which the historical information have been considered. In this research we deal with Bayesian inference on gene network. We apply a Bayesian framework to use the available information. Assuming a proper prior distribution and taking the dependency of parameters into account, we seek a model to obtain promising results. We also deal with the hyper parameter estimation. Two methods are considered. The results will be compared by the use of a simulation based on Gibbs sampler. The strengths and weaknesses of each method are briefly mentioned.
Kourosh Dadkhah, Edris Samadi Tudar,
Volume 12, Issue 1 (9-2018)
Abstract
The presence of outliers in data set may affect structure of analysis of variance test so that test results led to wrong acceptance or rejection of null hypothesis. In this paper the method of robust permutation distribution of F statistic based on trimmed mean is proposed. This method by permutation distribution of a function of trimmed mean, reduces the sensitivity to classical assumptions such as normality and presence of outlier and it guarantees the reliability of result. The proposed method is compared with robust analysis of variance based of forward search approach. The proposed method, unlike the forward search-based approach is free of restricted parametric assumptions and computationally spend less time. Numerically assessment results on type I error and power of test, demonstrate good performance of this robust method in comparison with competitor method.
Nabaz Esmailzadeh, Reza Nikbakht,
Volume 12, Issue 2 (3-2019)
Abstract
Variances homogeneity test are mostly applied as a preliminary test to other analyses like test of equality of means. So far, several tests have been offered in randomized complete block design, that the most prevalent of them are Bartlett and Levene tests, and others are generalized kind of these two tests. The distribution of statistics for these tests are obtained asymptotically. Recently, a test has been introduced base on estimated critical values. In this paper, nine tests are examined based on estimated critical values method and their performance are evaluated with various blocks and treatment groups for normal and t-student distributions by a simulation study. The method of estimated critical values has a good performance in the type I error and a power improvement with respect to using asymptotic distribution.
Sakineh Dehghan, Mohamadreza Faridrohani,
Volume 15, Issue 2 (3-2022)
Abstract
The concept of data depth has provided a helpful tool for nonparametric multivariate statistical inference by taking into account the geometry of the multivariate data and ordering them. Indeed, depth functions provide a natural centre-outward order of multivariate points relative to a multivariate distribution or a given sample. Since the outlingness of issues is inevitably related to data ranks, the centre-outward ordering could provide an algorithm for outlier detection. In this paper, based on the data depth concept, an affine invariant method is defined to identify outlier observations. The affine invariance property ensures that the identification of outlier points does not depend on the underlying coordinate system and measurement scales. This method is easier to implement than most other multivariate methods. Based on the simulation studies, the performance of the proposed method based on different depth functions has been studied. Finally, the described method is applied to the residential houses' financial values of some cities of Iran in 1397.
Ali Rostami, Mohammad Khanjari Sadegh, Mohammad Khorashadizadeh,
Volume 17, Issue 1 (9-2023)
Abstract
This article considers the stress-strength reliability of a coherent system in the state of stress at the component level. The coherent series, parallel and radar systems are investigated. For 2-component series or parallel systems and radar systems, this reliability based on Exponential distribution is estimated by maximum likelihood, uniformly minimum variance unbiased and Bayes methods. Also, simulation studies have been done to check estimators' performance, and real data are analyzed.
Hamed Salemian, Eisa Mahmoudi, Sayed Mohammad Reza Alavi,
Volume 18, Issue 1 (8-2024)
Abstract
Often, in sample surveys, respondents refused to answer some questions of a sensitive nature. Randomized response methods are designed not to reveal respondent confidentiality. In this article, a new quantitative randomized response method is introduced, and by conducting a series of simulation studies, we show that the proposed method is preferable to the cumulative and multiplicative methods. By using unbiased predictors, we estimate the covariance between two sensitive variables. In an experimental study using the proposed method, the average number of cheating and the average daily cigarette consumption of the Shahid Chamran University of Ahvaz students are estimated along with their variance, and an estimate for the covariance between them is provided.
Ms. Samira Taheri, Dr Mohammad Ghasem Akbari, Dr Gholamreza Hesamian,
Volume 18, Issue 1 (8-2024)
Abstract
In this paper, based on the concept of $alpha$-values of fuzzy random variables, the fuzzy moving average model of order $q$ is introduced. In this regard, first, the definitions of variance, covariance, and correlation coefficient between fuzzy random variables are presented, and their properties are investigated. In the following, while introducing the fuzzy moving average model of order $q$, this model's autocovariance and autocorrelation functions are calculated. Finally, some examples are presented for the obtained results.
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