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Showing 31 results for Subject:
Abdolrahman Rasekh, Behzad Mansouri, Narges Hedayatpoor,
Volume 13, Issue 1 (9-2019)
Abstract
The study of regression diagnostic, including identification of the influential observations and outliers, is of particular importance. The sensitivity of least squares estimators to the outliers and influential observations lead to extending the regression diagnostic in order to provide criteria to assess the anomalous observations. Detecting influential observations and outliers in the presence of collinearity is a complicated task, in the sense that collinearity may cover some of the unusual data. One of the considerable methods to identify outliers is the mean shift outliers method. In this article, we extend the mean shift outliers method to the ridge estimates under linear stochastic restrictions, which is used to reduce the effect of collinearity, and to provide the test statistic to identify the outliers in these estimators. Finally, we show the ability of our proposed method using a practical example of real data.
Sayed Mohammad Reza Alavi, Mohammad Joharzadeh, Rahim Chinipardaz,
Volume 13, Issue 1 (9-2019)
Abstract
Usually in survey sampling when the sensitive questions are asked directly, the respondents do not provide true answers. The randomized response techniques have been introduced to protect the privacy responses. In this article we focus on Simons randomized response technique for qualitative variables. Using the combination of the two different Simmons’ models, a new combined randomized response technique is introduced to increase protection of privacy. Using simulation in R package, efficiency of the proposed model is compared to the Simmons’ and Alavi and Tajodini's (1394) models. Finally, the proposed model has been employed for estimating the proportion of student cheating in Shahid Chamran University.
Mohammad Nasirifar, Mohammadreza Akhoond, Mohammadreza Zadkarami,
Volume 13, Issue 2 (2-2020)
Abstract
The parameters of reliability for the most family marginal distribution is estimated with the assumption of independence between two component stress and strength, but, unfortunately when these two component are correlated, have been less discussed. Recently, a method based on a copula function for estimating the reliability parameter is proposed under the assumption of correlation between stress and strength components. In this paper, this method is used to estimate the reliability parameter when the distribution of componets is Generalized Exponential (GE). For this purpose FGM, generalized FGM and frank copula function have been used. Then simulation is also used to demonstrate the suitability of the estimates. In the end, reliability parameter for data relative contribution of major groups in terms of age breakdown of the population of urban and rural areas in Iran in the year 1390 will be estimated.
Sayed Mohammad Reza Alavi, Sara Nayyeri, Mohammad Reza Akhoond,
Volume 13, Issue 2 (2-2020)
Abstract
In many sample surveys, the variables of interest, such as student cheating in a university are sensitive in nature. In such situations, the interviewees respond to direct questions untruthful, or refuse to answer. The various indirect methods such as randomized response technique and item count technique are introduced to collect sensitive information. In this paper a new item count is proposed, then its randomized version called randomized item count model is introduced. Using this model an unbiased estimator for the sensitive proportion of the population is obtained. The variance of the estimator and an estimate for its variance are obtained. A criterion for comparing efficiency and privacy is introduced simultaneously. Using simulation, the proposed model is evaluated and its efficiency and privacy are compared with the Simons’ technique. Based on this criterion, it is shown that the proposed method is better than the Simons method. The proportion of student cheating in the Shahid Chamran University of Ahvaz is estimated using the proposed model.
Shadi Saeidi Jeyberi, Mohammadreza Zadkarami, Gholamali Parham,
Volume 14, Issue 1 (8-2020)
Abstract
In this paper, Bayesian fuzzy estimator is obtained first, for the fuzzy data based on the probability prior distribution and afterward based on the possible model and the possibility of a prior distribution. Considering the effect of the membership functions on the fuzzy and possibility Bayesian estimators, a membership function that gives the optimal fuzzy and possibility Bayesian estimators will be introduced for the data. The optimality of the new triangular-gaussian membership function is denoted by using the normal and exponential data sets.
Jalal Chachi, Alireza Chaji,
Volume 15, Issue 1 (9-2021)
Abstract
This article introduces a new method to estimate the least absolutes linear regression model's parameters, which considers optimization problems based on the weighted aggregation operators of ordered least absolute deviations. In the optimization problem, weighted aggregation of orderd fitted least absolute deviations provides data analysis to identify the outliers while considering different fitting functions simultaneously in the modeling problem. Accordingly, this approach is not affected by outlier observations and in any problem proportional to the number of potential outliers selects the best model estimator with the optimal break-down point among a set of other candidate estimators. The performance and the goodness-of-fit of the proposed approach are investigated, analyzed and compared in modeling analytical dataset and a real value dataset in hydrology engineering at the presence of outliers. Based on the results of the sensitivity analysis, the properties of unbiasedness and efficiency of the estimators are obtained.
Dr Alireza Chaji,
Volume 16, Issue 2 (3-2023)
Abstract
High interpretability and ease of understanding decision trees have made
them one of the most widely used machine learning algorithms. The key to building
efficient and effective decision trees is to use the suitable splitting method. This
paper proposes a new splitting approach to produce a tree based on the T-entropy criterion
for the splitting method. The method presented on three data sets is examined
by 11 evaluation criteria. The results show that the introduced method in making
the decision tree has a more accurate performance than the well-known methods of
Gini index, Shannon, Tisalis, and Renny entropies and can be used as an alternative
method in producing the decision tree.
Fatemeh Ghapani, Babak Babadi,
Volume 17, Issue 2 (2-2024)
Abstract
In this paper, we introduce the weighted ridge estimators of fixed and random effects in stochastic restricted linear mixed measurement error models when collinearity is present. The asymptotic properties of the resulting estimates are examined. The necessary and sufficient conditions, for the superiority of the weighted ridge estimators against the weighted estimator in order to select the ridge parameter based on the mean squared error matrix of estimators, are investigated. Finally, theoretical results are augmented with a simulation study and a numerical example.
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.
Jalal Chachi, Mohammadreza Akhond, Shokoufeh Ahmadi,
Volume 18, Issue 2 (2-2025)
Abstract
The Lee-Carter model is a useful dynamic stochastic model representing the evolution of central mortality rates over time. This model only considers the uncertainty about the coefficient related to the mortality trend over time but not the age-dependent coefficients. This paper proposes a fuzzy extension of the Lee-Carter model that allows quantifying the uncertainty of both kinds of parameters. The variability of the time-dependent index is modeled as a stochastic fuzzy time series. Likewise, the uncertainty of the age-dependent coefficients is quantified using triangular fuzzy numbers. Considering this last hypothesis requires developing and solving a fuzzy regression model. Once the generalization of the desired fuzzy model is introduced, we will show how to fit the logarithm of the central mortality rate in Khuzestan province using by using fuzzy numbers arithmetic during the years 1401-1383 and random fuzzy forecast in the years 1402-1406.
Zahra Nicknam, Rahim Chinipardaz,
Volume 19, Issue 1 (9-2025)
Abstract
Classical hypothesis tests for the parameters provide suitable tests when the hypotheses are not restricted. The best are the uniformly most powerful test and the uniformly most powerful unbiased test. These tests are designed for specific hypotheses, such as one-sided and two-sided for the parameter. However, in practice, we may encounter hypotheses that the parameters under test have typical restrictions in the null or alternative hypothesis. Such hypotheses are not included in the framework of classical hypothesis testing. Therefore, statisticians are looking for more powerful tests than the most powerful ones. In this article, the union-intersection test for the sign test of variances in several normal distributions is proposed and compared with the likelihood ratio test. Although the union-intersection test is more powerful, neither test is unbiased. Two rectangular and smoothed tests have been examined for a more powerful test.
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