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‎The most important goal of statistical science is to analyze the real data of the world around us‎. ‎If this information is analyzed accurately and correctly‎, ‎the results will help us in many important decisions‎. ‎Among the real data around us which its analysis is very important‎, ‎is the water consumption data‎. ‎Considering that Iran is located in a semi-arid climate area of the earth‎, ‎it is necessary to take big steps for predicting and selecting the best and the most appropriate accurate models of water consumption‎, ‎which is necessary for the macro-national decisions‎. ‎But analyzing the real data is usually complicated‎. ‎In the analysis of the real data set‎, ‎we usually encounter with the problems of multicollinearity and outliers points‎. ‎Robust methods are used for analyzing the datasets with outliers and ridge method is used for analyzing the data sets with multicollinearity‎. ‎Also‎, ‎the restriction on the models is resulted from using non-sample information in estimation of regression coefficients‎. ‎In this paper‎, ‎it is proceeded to model the water consumption data using robust stochastic restricted ridge approach and then‎, ‎the performance of the proposed method is examined through a Monte Carlo simulation study‎.

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In today’s world, using the statistical modeling process, natural phenomena can be used to analyze and predict the events under study. ‎ Many hydrological modeling methods do not make the best use of available information because hydrological models show a wide range of environmental processes that complex the model‎‏. ‎‎‎‎In particular, when predicting, parameters affect the performance of statistical models. In many risk assessment issues, the presence of uncertainty in the parameters leads to uncertainty in predicting the model. Global sensitivity analysis is a tool used to show uncertainty and
is used in decision making, risk assessment, model simplifcation and so on. Minkowski distance sensitivity analysis and regional sensitivity analysis are two broad methods that can work with a given sample set of model input-output pair. One signifcant difference between them is that minkowski distance sensitivity analysis analyzes output distributions conditional on input values (forward), while regional sensitivity analysis analyzes input distributions conditional on output values (reverse). In this dissertation, we study the relationship between these two approaches and show that regional sensitivity analysis (reverse), when focusing on probability density functions of input, converges towards minkowski distance sensitivity analysis (forward) as the number of classes for conditioning model outputs in the reverse method increases. Similar to the existing general form of forward sensitivity indices, we derive a general form of the reverse sensitivity indices and provide the corresponding reverse given-data method. Finally, the sensitivity analysis of a water storage design with high dimensions of the model outputs is performed.

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Today, applying statistics in other sciences, including medical sciences, has become very common. Researchers consider optimal design as a tool to increase the efficiency of experiments.
Pharmacokinetics is particularly important in the medical sciences as a branch of pharmacology that studies the performance of drugs in living organisms.
This study aims to introduce optimal designs for models in pharmacokinetic studies. The models used in this paper are known as nonlinear models in the statistical literature. These models depend on specific parameters based on pharmacological factors and time as predictor variables.
Optimal designs are obtained based on functions of the Fisher information matrix. These functions are known as optimal criteria. In this paper, we consider two criteria, A- and E-optimality. Based on these two criteria, locally optimal designs are obtained for the considered models.

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Analysis and modeling the‎ ​‎h​igh-dimensional data is one of the most challenging problems faced by the world nowaday‎. ‎Interpretation of such data is not easy and needs to be applied to modern methods‎. The penalized methods are one of the most popular ways to analyze the high-dimensional data‎. ‎Also‎, ‎the regression models and their analysis are affected by the outliers seriously‎. The least trimmed squares method is one of the best robust approaches to solve the corruptive influence of the outliers‎. ‎Semiparametric models‎, ‎which are a combination of both parametric and nonparametric models‎, ‎are very flexible models‎. ‎They are useful when the model contains both parametric and nonparametric parts‎. ‎The main purpose of this paper is to analyze semiparametric models in high-dimensional data with the presence of outliers using the robust sparse Lasso approach‎. ‎Finally‎, ‎the performance of the proposed estimator is examined using a real data analysis about production of vitamin B2‎.

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Anthropometr is a science that deals with the size of the body including the dimensions of different parts, the field of motion and the strength of the muscles of the body. Specific individual dimensions such as heights, widths, depths, distances, environments and curvatures are usually measured. In this article, we investigate the anthropometric characteristics of patients with chronic diseases (diabetes, hypertension, cardiovascular disease, heart attacks and strokes) and find the factors affecting these diseases and the extent of the impact of each to make the necessary planning.

This research is done descriptively-analytically, the research community of the people of Ravansar county is one of the functions of Kermanshah province. MATLAB, R and SPSS statistical software are used to analyze the data and test the presented hypotheses. Significance level for all tests is less than 0.05. Descriptive statistics methods is used to describe and summarize the variables. The Pearson correlation analysis method is used to investigate the relationship between variables, regression analysis (logistics) is used to investigate the effect of independent variables on the dependent variable. According to the results, it seems that some anthropometric indicators have a significant relationship with risk factors of chronic diseases. So, continuous evaluations, lifestyle changes and increasing the level of awareness to control, prevent and adjust the indicators are suggested.

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In the present article, an attempt has been made to provide a fully operational and useful guide regarding the implementation of two-stage cluster sampling plan in field research, which is one of the most widely used sampling plans according to the current conditions and facilities in the country. . In addition, for further use by researchers in this field, this article provides codes for implementing two-stage cluster sampling with unequal size based on probability proportional to size along with an example on two-stage cluster sampling with probability proportional to size (PPS) with hypothetical data using R software is included.

 

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Kernel density estimation is a standard method for estimating the probability density function, which in many cases works well. However, it has been found that it does not work well for negative, sloping, and wide-tail distributions, which are common features of the distribution of longevity, income, and so on. The purpose of this paper is to evaluate the performance of multiplicative bias correction (MBC) methods using asymmetric kernel estimators and compare this estimator with other boundary problem solving methods. In this paper, in addition to introducing MBC methods in combination with asymmetric kernel estimators, a simulation study shows that this estimator can, in some cases, provide a much better fit for density estimation than the standard kernel estimator. MBC methods using asymmetric kernel estimators were also used to estimate the lifetime density of transplanted corneas in 119 patients.

 

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This study aimed to apply the theory of planned behaviors on entrepreneurial tendencies and the effect of this tendency on the development of information technology among 18-30 years old Iranian youth in the winter of 1401. A part of the sample was based on the age group listed in the characteristics of mobile operators (18-30 years old) in Tehran province who received the questionnaire completely randomly using SMS system and sending the link address, and also another section was a of students aged 18-30 years old of The Islamic Azad University of South Tehran Branch, and the research questionnaire was provided to them. The validity of the questionnaire was confirmed by experts in ICT and its reliability was obtained based on Cronbach's alpha test with an alpha coefficient of at least 0.70 (criterion). The results showed that according to the theory of planned behaviors, entrepreneurial tendency has an effect on information technology development in Iranian youth aged 18-30 years. 

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‎The use of administrative resources in censuses provides the possibility of reducing costs‎, ‎improving data quality and producing information with a shorter time sequence‎. ‎The mentioned cases‎, ‎in addition to the annual monitoring of indicators of sustainable development goals‎, ‎can also play a significant role in meeting the growing needs of the country's planning and research system‎, ‎but there are many challenges in this regard‎, ‎one of the most important of which is the assessment of the quality of administrative data‎. ‎Quality assessment is the most important aspect of using registration and administrative data in the census‎, ‎and it is one of the necessities of the registers-based population and housing censuses‎, ‎where traditional quality assessment criteria cannot be used‎. ‎In other words‎, ‎despite the advantages of using register and administrative data in the census‎, ‎there are many key quality risks that need to be addressed and assessed before using them in the census‎. ‎In this paper‎, ‎the methods by which the national statistics offices can evaluate the quality of the data obtained from the registers with the aim of producing high-quality statistical outputs have been reviewed‎. ‎Therefore‎, ‎the tools and key indicators used to quantify the quality assessment in each of the four stages of quality assessment including source‎, ‎input data‎, ‎process and output in the census process are introduced‎.

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Given the limited energy resources globally, energy optimization is crucial. A significant portion of this energy is consumed
by buildings. Therefore, the aim of this research is to explore the simultaneous factors affecting the heating and cooling of
buildings. In the current research, 768 different residential buildings simulated with Ecotect software have been investigated.
Joint regression model and exploratory data analysis methods were used to identify the influencing factors of the heating and
cooling of buildings. Based on variables such as relative compactness, overall height, surface area, and roof of the buildings,
a new variable called ”type” (building model) was introduced and shown to be one of the strongest factors affecting the
heating and cooling of buildings. This variable is related to the shape of the building. In the joint regression model, it is
assumed that the responses follow a multivariate normal distribution. Then this model is compared with separate regression
models (without assuming responses correlation) and using Akaike’s information criterion and deviance information criterion,
pointing to the superiority of the joint regression model. Additionally, the model parameters are estimated using the maximum
likelihood estimation method and the amount of Akaike model compared to the separate model is a decrease of 0.0072%,
which shows the superiority of the joint regression model. The deviance information criterion is equal to 0.001736%, and in
comparison with the chi distribution, the null hypothesis is rejected to test the superiority of the models, which is regressed
to the superiority of the joint model.

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Drought is one of the most important concepts in hydrology, which has gained increased significance in recent years,
and the results of its modeling and analysis are crucial for risk assessment and management. This study examines drought at
the Zahedan station during the statistical period from 1951 to 2017 using the standardized precipitation index and explains
multivariate data modeling methods using Vine Copulas. Various models are compared using goodness-of-fit criteria, and
the best model is selected. Additionally, joint return periods are calculated and analyzed.

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Over the last decades there have been, increasing challenges to the traditional census. Collecting information from every person in a country using traditional methods is a massive and costly exercise and thus a key concern. Further, reduced willingness amongst the population to respond to the census questionnaires and unexpected crises such as the Covid-19 pandemic have maked it increasingly difficult for NSOs to produce reliable figures with the necessary geographical and substantive detail.But developments of new technologies and approaches to data collection mean that there are also emerging opportunities. The increasing desire to use administrative resources in the implementation of censuses has made it possible to reduce costs, improve data quality, and produce frequent information on an annual basis. A study of the different approaches adopted by some countries in the Asia-Pacific region shows that administrative data are currently being used in different ways to support census operations. Examining these approaches will be very useful to help and guide for countries contemplating the use of or expansion of their use of administrative data for censuses. In this article, while reviewing the definition of register and the types of administrative registers used in register-based census, the proceedings taken in some countries in moving towards register-based census are presented.
 

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Customer churn is one of the major economic concerns of many companies, including banks, and banks have focused their attention on customer retention, because the cost of attracting a new customer is much higher than the cost of keeping a customer.
Customer churn prediction and profiling are two major economic concerns for many companies. 
Different learning approaches have been proposed; however, a priori choice of the most suitable model to perform both tasks remains non-trivial as it is highly dependent on the intrinsic characteristics of the churn data. 
Our study compares several machine learning methods with several resampling approaches for data balancing of a public bank data set.
Our evaluations, reported in terms of area under the curve (AUC) and sensitivity, explore the influence of rebalancing strategies and difference machine learning methods. 
This work identifies the most appropriate methods in an attrition context and an effective pipeline based on an ensemble approach and clustering. Our strategy can enlighten marketing or human resources services on the behavioral patterns of customers and their attrition probability.