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Showing 295 results for Type of Study: Applicable
Dr Fatemeh Hosseini, Dr Omid Karimi, Ms Ahdiyeh Azizi,
Volume 23, Issue 1 (9-2018)
Abstract
Often in practice the data on the mortality of a living unit correlation is due to the location of the observations in the study. One of the most important issues in the analysis of survival data with spatial dependence, is estimation of the parameters and prediction of the unknown values in known sites based on observations vector. In this paper to analyze this type of survival, Cox regression model with piecewise exponential function used as a hazard and spatial dependence as a Gaussian random field and as a latent variable is added to the model. Because there is no closed form for posterior distribution and full conditional distributions, also long computing for Markov chain Monte Carlo algorithms, to analyze the model are used the approximate Bayesian methods.
A practical example of how to implement an approximate Bayesian approach is presented.
Afshin Fallah, Khadiheh Rezaei,
Volume 23, Issue 1 (9-2018)
Abstract
When the observations reflect a multimodal, asymmetric or truncated construction or a combination of them, using usual unimodal and symmetric distributions leads to misleading results. Therefore, distributions with ability of modeling skewness, multimodality and truncation have been in the core of interest in statistical literature, always. There are different methods to contract a distribution with these abilities, which using the weighted distribution is one of these methods. In this paper, it is shown that by using a weight function one can create such desired abilities in the corresponding weighted distribution.
Maryam Parsaeian, Sima Naghizadeh, Habib Naderi,
Volume 23, Issue 1 (9-2018)
Abstract
Explaining the problem. The equating process is used to compare the scores of the two different tests with the same theme. The goal of this research is finding the best method of equating data using Logistic model.
Method. we are using the data of Ph.D. test in Statistic major for two consecutive years 92 and 93. For analyzing, we are specifically using the tests of Statistics major which includes 45 questions. Parameters of test and ability of individuals are considered according to the three parametrs model and by using the MULTILOG software. In this study, we are using the Mean-mean, Mean-Sigma, Haebara, and Stocking-Lord methods by considering the unequal groups with Anchor-test design, and we are using the root mean square error for choosing the optimal solution.
Conclusion. The results of this study show that the methods under characteristic curve are more accurate.
Anita Abdollahi Nanvapisheh, ,
Volume 23, Issue 2 (3-2019)
Abstract
In this paper, a new distribution is introduced, which is a generalization of a well-known distribution. This distribution is flexible and applies to income data modeling. We first provide some of the mathematical and distributional properties of this new model and then, to demonstrate the flexibility the new distribution, we will present the applications of this distribution with real data. Data fitting results confirm the appropriateness of this new model for the real data set.
Hamieh Arzhangdamirchi, Reza Pourtaheri,
Volume 23, Issue 2 (3-2019)
Abstract
Many point process models have been proposed for studying variety of scientific disciplines, including geology, medicin, astronomy, forestry, ecology and ect. The assessment of fitting these models is important. Residuals-based methods are appropriate tools for evaluating good fit of spatial point of process models. In this paper, first, the concepts related to the Voronoi residuals are investigated. Then, after fitting a cluster point process to the data set of the position of the trees in the Guilan forest, the proposed model is evaluated using these residuals.
Dr. Mousa Golalizadeh, Mr. Amir Razaghi,
Volume 24, Issue 1 (9-2019)
Abstract
The Principal Components Analysis is one of the popular exploratory approaches to reduce the dimension and to describe the main source of variation among data. Despite many benefits, it is encountered with some problems in multivariate analysis. Having outliers among data significantly influences the results of this method and it sounds a robust version of PCA is beneficial in this case. In addition, having moderate loadings in the final results makes the interpretation of principal components rather difficult. One can consider a version of sparse components in this case. We study a hybrid approach consisting of joint robust and sparse components and conduct some simulations to evaluate and compare it with other traditional methods. The proposed technique is implemented in a real-life example dealing with the crime rate in the USA.
Ramin Kazemi,
Volume 24, Issue 1 (9-2019)
Abstract
The goal of this paper is to introduce the contraction method for analysing the algorithms.
By means of this method several interesting classes of recursions can be analyzed as paricular cases of the general framework. The main steps of this technique is based on contraction properties of algorithm with respect to suitable probability metrics. Typlically the limiting distribution is characterized as a fixed poin of a limiting operator on the class of probability distributions.
Mohammad Jafari Aminabadi, Javid Jowzadani, Hadi Shiroyeh Zad, Khalegh Behrooz Dehkordi,
Volume 24, Issue 1 (9-2019)
Abstract
Regard to daily increasing of customer services share in all over the world, one of most effective parameters on customer satisfaction would be service delivery with the least delay. work allocation method, planning, organizing, prioritizing and service delivery routing have always been one of the main concerns of service providing centers and lack of proper planning in this regard will cause service network traffic, environmental and noise pollution, waste of time and fuel and eventually dissatisfaction of consumers and technicians.
On the other hand, daily division of labor in order to deliver delightful services by considering man’s opinion would not be an optimal choice. In this research, with case study on a home appliance service company and by considering customer demands in city of Isfahan and by data analysis, geographic points of customer’s demands have clustered by k-mean algorithm.
It has been tried to reduce the search space by clustering geographic areas and then by using simulated annealing, the optimum path for customer’s probable demands present to the technicians with observance of daily working capacity per cluster.
The computational results show that after clustering by k-means algorithm, routing probable demands with observance of daily working capacity for technicians, the objective function has better improvement in compare with non-clustering case.
Service technician routing by clustering, while being responsive in shortest time, has more repeatability test and cause more order and responsibility sense and more domination on service areas and also has an effective role in reducing time to handle a consumer and getting their satisfaction.
Akram Heidari Garmianaki, Mehrdad Niaparast,
Volume 24, Issue 2 (3-2020)
Abstract
In the present era, classification of data is one of the most important issues in various sciences in order to
detect and predict events. In statistics, the traditional view of these classifications will be based on classic
methods and statistical models such as logistic regression. In the present era, known as the era of explosion
of information, in most cases, we are faced with data that cannot find the exact distribution. Therefore, the
use of data mining and machine learning methods that do not require predetermined models can be useful.
In many countries, the exact identification of the type of groundwater resources is one of the important
issues in the field of water science. In this paper, the results of the classification of a data set for groundwater resources were compared using regression, neural network, and support vector machine.
The results of these classifications showed that machine learning methods were effective in determining the exact type of springs.
Samaneh Beheshtizadeh, Hamidreza Navvabpour,
Volume 25, Issue 1 (1-2021)
Abstract
Evidence-based management and development planning relies on official statistics. There are some obstacles that make it impossible to do a single-mode survey. These obstacles are the sampling frame, time, budget, and accuracy of measurement of each mode. Always we can not use a single-mode survey because of these factors. So we need to use other data collection methods to overcome these obstacles. This method is called the mixed-mode survey, which is a combination of several modes. In this article, we show that mixed-mode surveys can produce more accurate official statistics than single-mode surveys.
Miss Zahra Eslami, Miss Mina Norouzirad, Mr Mohammad Arashi,
Volume 25, Issue 1 (1-2021)
Abstract
The proportional hazard Cox regression models play a key role in analyzing censored survival data. We use penalized methods in high dimensional scenarios to achieve more efficient models. This article reviews the penalized Cox regression for some frequently used penalty functions. Analysis of medical data namely ”mgus2” confirms the penalized Cox regression performs better than the cox regression model. Among all penalty functions, LASSO provides the best fit.
Reza Cheraghi, Dr. Reza Hashemi,
Volume 25, Issue 1 (1-2021)
Abstract
Varying coefficient models are among the most important tools for discovering the dynamic patterns when a fixed pattern does not fit adequately well on the data, due to existing diverse temporal or local patterns. These models are natural extensions of classical parametric models that have achieved great popularity in data analysis with good interpretability. The high flexibility and interpretability of these models have led to use in many real applications. In this paper, after presenting a brief review of varying coefficient models, we use the parameter estimation method using the kernel function and cubic
spline then confidence band and hypothesis testing are investigated. Finally, using the real data of Iran’s inflation rate from 1989 to 2017, we show the application and capabilities of the varying coefficient model in interpreting the results. The main challenge in this application is that the panel or longitudinal models or even time series models with heterogeneous variances such as ARCH and GARCH models and their derived models did not fit adequately well on this dataset which justifies the use of varying coefficient models.
Omid Karimi, Fatemeh Hosseini,
Volume 25, Issue 1 (1-2021)
Abstract
Spatial count data is usually found in most sciences such as environmental science, meteorology, geology and medicine. Spatial generalized linear models based on Poisson (Poisson-lognormal spatial model) and binomial (binomial-logitnormal spatial model) distributions are often used to analyze discrete count data in which spatial correlation is observed. The likelihood function of these models is complex as analytic and so computation. The Bayesian approach using Monte Carlo Markov chain algorithms can be a solution to fit these models, although there are usually problems with low sample acceptance rates and long runtime to implement the algorithms. An appropriate solution is to use the Hamilton (hybrid) Monte Carlo algorithm
in The Bayesian approach. In this paper, the new Hamilton (hybrid) Monte Carlo method for Bayesian analysis of spatial count models on air pollution data in Tehran is studied. Also, the two common Monte Carlo algorithms such as the Markov chain (Gibbs and Metropolis-Hastings) and Langevin-Hastings are used to apply the complete Bayesian approach to the data modeling. Finally, an appropriate approach to data analysis and forecasting in all points of the city is introduced with the diagnostic criteria.
Mohammadreza Faridrohani, Behdad Mostafaiy, Seyyed Mohammad Ebrahim Hosseininasab,
Volume 25, Issue 2 (3-2021)
Abstract
Recently with science and technology development, data with functional nature are easy to collect. Hence, statistical analysis of such data is of great importance. Similar to multivariate analysis, linear combinations of random variables have a key role in functional analysis. The role of Theory of Reproducing Kernel Hilbert Spaces is very important in this content. In this paper we study a general concept of Fisher’s linear discriminant analysis that extends the classical multivariate method to the case functional data. A bijective map is used to link a second order process to the reproducing kernel Hilbert space, generated by its within class covariance kernel. Finally a real data set related to Iranian weather data collected in 2008 is also treated.
Ms Monireh Maanavi, Dr Mahdi Roozbeh,
Volume 26, Issue 1 (12-2021)
Abstract
The method of least squares is a very simple, practical and useful approach for estimating regression coefficients of the linear models. This statistical method is used by users of different fields to provide the best unbiased linear estimator with the least variance. Unfortunately, this method will not have reliable output if outliers are present in the dataset, as the collapse point (estimator consistency criterion) of this method is 0% . It is therefore important to identify these observations. Until now, the various methods have been proposed to identify these observations. In this article, the proposed methods are reviewed and discussed in details. Finally, by presenting a simulation example, we examine each of the proposed methods.
Miss Tayebeh Karami, Dr Muhyiddin Izadi, Dr Mehrdad Niaparast,
Volume 26, Issue 1 (12-2021)
Abstract
The subject of classification is one of the important issues in different sciences. Logistic regression is one of the statistical
methods to classify data in which the underlying distribution of the data is assumed to be known. Today, researchers in
addition to statistical methods use other methods such as machine learning in which the distribution of the data does not
need to be known. In this paper, in addition to the logistic regression, some machine learning methods including CART
decision tree, random forest, Bagging and Boosting of supervising learning are introduced. Finally, using four real data
sets, we compare the performance of these algorithms with respect to the accuracy measure.
Dr Abolfazl Rafiepour,
Volume 26, Issue 1 (12-2021)
Abstract
Nowadays, the need to pay attention to teaching statistics at all levels from elementary school to university has become more apparent. One of the goals of the various educational systems, which is reflected in their upstream documents, is to have citizens who are equipped with statistical literacy. In this regard, many statistical organizations and institutions have mentioned statistics education as one of their special goals and missions. School math textbooks in Iran also have sections devoted to discussions of statistics and probability. An examination of the role of statistics in Iran school Mathematics textbooks shows that there is good progress in including statistics and probability concepts in textbooks, but it is still far from the ideal. In the present article, after a brief discussion on the necessity of paying attention to statistics education in the school mathematics curriculum, the historical course of presenting statistics education will be reviewed. Then, the challenges in introducing the topics of teaching statistics and probability in the school mathematics curriculum are illustrated, and in the end, two new approaches (attention to big-data, use of new technologies in stimulating and modeling real-world phenomena) will be introduced in more detail and with examples.
Ehsan Bahrami Samani, Samira Bahramian,
Volume 26, Issue 1 (12-2021)
Abstract
The occurrence of lifetime data is a problem which is commonly encountered in various researches, including surveys, clinical trials and epidemioligical studies. Recently there has been extensive methodological resarech on analyzing lifetime data. Howerver, because usually little information from data is available to corretly estimate, the inferences might be sensitive to untestable assumptions which this calls for a sensitivity analysis to be performed.
In this paper, we describe how to evaluate the effect that perturbations to the Log-Beta Weibull Regression Responses. Also, we review and extend the application and interpretation of influence analysis methods using censored data analysis. A full likelihood-based approach that allows yielding maximum likelihood estimates of the model parameters is used. Some simulation studies are conducted to evalute the performance of the proposed indices in ddetecting sensitivity of key model parameters. We illustrate the methods expressed by analyzing the cancer data.
Prof. Anoshiravan Kazemnejad, Miss Parisa Riyahi, Dr Shayan Mostafaee,
Volume 26, Issue 1 (12-2021)
Abstract
The multifactorial dimension reduction algorithm is considered as a powerful algorithm for identifying high-order interactions in high dimensional structures. In this study, information of 748 patients with Behcetchr('39')s disease who referred to the Rheumatology Research Center, Shariati Hospital, Tehran, and 776 healthy controls was used to identify the interaction effects between ERAP1 gene polymorphisms involved in the occurrence of Behcetchr('39')s disease using the multifactor dimensionality reduction algorithm. Data analysis was performed using MDR 3.0.2 software. The models obtained from the multifactorial dimensional reduction algorithm with balanced accuracy above 0.6 have been determined to increase the risk of Behcetchr('39')s disease. The multi-factor reduction algorithm has high power and speed in calculating the interaction effects of polymorphisms or genetic mutations and identifying important interactions.
Dr Mahdi Roozbeh, Mr Arta Rouhi, Fatemeh Jahadi, Saeed Zalzadeh,
Volume 26, Issue 2 (3-2022)
Abstract
In this research, the aim is to assess and analyze a method to predict the stock market. However, it is not easy to predict the capital market due to its high dependence on politics but by data modeling, it will be somewhat possible to predict the stock market in the long period of time. In this regard, by using the semi-parametric regression models and support vector regression with different kernels and measuring the predictor errors in the stock market of one stock based on daily fluctuations and comparing methods using the root of mean squared error and mean absolute percentage error criteria, support vector regression model has been the most appropriate fit to the real stock market data with radial kernel and error equal to 0.1.
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