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Showing 9 results for AkBARI
Roshanak Aliakbari Saba, Alireza Zahedian, Marzieh Arbabi,
Volume 9, Issue 1 (9-2015)
Abstract
Annual estimation of average household incomes is one of the main goals of the household income and expenditure survey in Iran. So, regarding importance of accuracy of gathered data and reasons that lead to error in measuring household income, in this paper, model-based methods are used for estimating income measurement error and adjusting sample households declared income for 2011 household income and expenditure survey.
Masoumeh Akbari Lakeh, Zohreh Safarzadeh,
Volume 12, Issue 2 (3-2019)
Abstract
The Pareto distribution has many applications in economics and actuarial sciences. So far, a lot of properties of this distribution based on order data such as order statistics and records are studied. In this paper, a new version of notion of near-record observations is defined. Then, some results of characterization of Pareto distribution based on this new definition are obtained.
Roshanak Aliakbari Saba, Nasrin Ebrahimi, Lida Kalhori Nadrabadi, Asieh Abbasi,
Volume 15, Issue 2 (3-2022)
Abstract
The ranked set sampling method uses the ranking information of the units to provide a more representative sample of the population to the survey designers. The sampling distribution is closer to the actual distribution of the population. In this article, to ensure the effectiveness of ranked set sampling in extensive surveys conducted to prepare official statistics, we intend to use this sampling method to improve the efficiency of key estimates of household expenditure and income survey of the Statistics Center of Iran. The results show that using ranked set sampling to design household expenditure and income surveys can improve the efficiency of key estimates of the study, provided that the ranking variable used has a high correlation with the main variables of the study. Obviously, in the absence of a suitable and available variable for ranking the units, the information of the sampling frame can be used to construct a ranking variable correlated with the key variables of the survey.
Doctor Masoumeh Akbari, Mrs Arefeh Kasiri, Doctor Kambiz Ahmadi,
Volume 17, Issue 1 (9-2023)
Abstract
In this paper, quantile-based dynamic cumulative residual and failure extropy measures are introduced. For a presentation of their applications, first, by using the simulation technique, a suitable estimator is selected to estimate these measures from among different estimators. Then, based on the equality of two extropy measures in terms of order statistics, symmetric continuous distributions are characterized. In this regard, a measure of deviation from symmetry is introduced and how it is applied is expressed in a real example. Also, among the common continuous distributions, the generalized Pareto distribution and as a result the exponential distribution are characterized, and based on the obtained results, the exponentiality criterion of a distribution is proposed.
Miss. Mahdieh Mozafari, Dr. Mohammad Khanjari Sadegh, Dr. Mohammad Ghasem Akbari, Dr. Gholamreza Hesamian,
Volume 18, Issue 1 (8-2024)
Abstract
In this paper, fuzzy order statistics are expressed based on the concept of α-value, and some of its applications in reliability have been examined. For this purpose, if the lifetime distribution of the system components is known, some of the reliability criteria of the $i$th order statistic using the definition of a fuzzy random variable based on the α-value have been investigated. Also, if the lifetime distribution of the components is unknown or only the fuzzy observations of the lifetime of the components are available, the empirical distribution function of the fuzzy data is used to estimate the reliability based on ordinal statistics, and examples are provided to illustrate the results.
Hossein Mohammadi, Mohammad Ghasem Akbari, Gholamreza Hesamian,
Volume 18, Issue 1 (8-2024)
Abstract
First, this article defines a meter between fuzzy numbers using the support function. Then, based on the support function, the concepts of variance, covariance, and correlation coefficient between fuzzy random variables are expressed, and their properties are investigated. Then, using the above concepts, the p-order fuzzy autoregressive model is introduced based on fuzzy random variables, and its properties are investigated. Finally, to explain the problem further, examples will be presented and compared with similar models using some goodness of fit criteria.
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.
Maryam Maleki, Hamid Reza Nili-Sani, M.g. Akbari,
Volume 18, Issue 2 (2-2025)
Abstract
In this paper, we consider the issue of data classification in which the response (dependent) variable is two (or multi) valued and the predictor (independent) variables are ordinary variables. The errors could be nonprecise and random. In this case, the response variable is also a fuzzy random variable. Based on this and logistic regression, we formulate a model and find the estimation of the coefficients using the least squares method. We will describe the results with an example of one independent random variable. Finally, we provide recurrence relations for the estimation of parameters. This relation can be used in machine learning and big data classification.
Elham Ranjbar, Mohamad Ghasem Akbari, Reza Zarei,
Volume 19, Issue 1 (9-2025)
Abstract
In the time series analysis, we may encounter situations where some elements of the model are imprecise quantities. One of the most common situations is the inaccuracy of the underlying observations, usually due to measurement or human errors. In this paper, a new fuzzy autoregressive time series model based on the support vector machine approach is proposed. For this purpose, the kernel function has been used for the stability and flexibility of the model, and the constraints included in the model have been used to control the points. In order to examine the performance and effectiveness of the proposed fuzzy autoregressive time series model, some goodness of fit criteria are used. The results were based on one example of simulated fuzzy time series data and two real examples, which showed that the proposed method performed better than other existing methods.
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