Mining and mineral industries supply raw materials to many industries and play an important role in economic self-sufficiency, productive employment, increasing economic growth and per capita income. However, despite the mineral benefits that Iran enjoys, the mining industry has little place not only in the country's exports, but also in national production. In this regard, the main purpose of this article is to review and evaluate the performance of 16 mining companies operating in the Tehran Stock Exchange to evaluate their performance, the most efficient companies to be introduced as role models for other mining companies. In this study, using nonparametric method and data of Kadal Publishers Information System in 1398, the efficiency of mining companies operating in the stock market has been evaluated with two assumptions, constant return to scale and variable return to scale. The results of this study showed that the scale efficiency is 80% and the average management efficiency is 74%. This means that the subdivisions of mining companies, assuming the stability of other conditions, in terms of scale and management, have empty space, in other words, the factors used such as manpower, mining equipment, transportation and heavy and light equipment in a They do not use optimally. Other results of the study showed that the average efficiency of the listed mining companies in the country is 62%. Due to the model of Damavand, Gol Gohar mining and industrial companies, Chadormelo mining and industrial companies and the development of mines in Iran, mining companies are recommended. Inefficiently to imitate them in order to increase efficiency.

‎The recent advancements in technology have faced an increase in the growth rate of data‎.‎According to the amount of data generated‎, ‎ensuring effective analysis using traditional approaches becomes very complicated‎.‎One of the methods of managing and analyzing big data is classification‎.‎%One of the data mining methods used commonly and effectively to classify big data is the MapReduce‎‎In this paper‎, ‎the feature weighting technique to improve Bayesian classification algorithms for big data is developed based on Correlative Naive Bayes classifier and MapReduce Model‎.‎%Classification models include Naive Bayes classifier‎, ‎correlated Naive Bayes and correlated Naive Bayes with feature weighting‎.‎Correlated Naive Bayes classification is a generalization of the Naive Bayes classification model by considering the dependence between features‎.‎%This paper uses the feature weighting technique and Laplace calibration to improve the correlated Naive Bayes classification‎.‎The performance of all described methods are evaluated by considering accuracy‎, ‎sensitivity and specificity‎, ‎accuracy‎, ‎sensitivity and specificity metrics.

Background and purpose: In many area of medical research, a relation analysis between one response variable and some explanatory variables is desirable. Regression is the most common tool in this situation. If we have some assumptions for such normality for response variable, we could use it. In this paper we propose a nonparametric regression that does not have normality assumption for response variable and we focus on longitudinal data.Materials and Methods: Consider nonparametric estimation in a varying coefficient model with repeated measurements (Yij, Xij, tij), for i=1, …, n and j=1, …, ni where Xij=(Xij0,…, Xijk)T and (Yij, Xij,tij) denote the jth outcome, covariate and time design points, respectively, of the ith subject. The model considered here is YijT b(tij)+ei(tij), where b(t)= (b0(t),…bk(t))T, for k³0 are smooth nonparametric functions of interest and ei(t) is a zero-mean stochastic process. The measurements are assumed to be independent for different subjects but can be correlated at different time points within each subject. For evaluating this model, we use data of a cohort of 289 healthy infants born in Shiraz in 2007. The proposed nonparametric regression was fitted to them for obtaining effect rates of mother weight, mother arm circumference and maternal age at delivery time and maternal age at first menarche on boy’s arm circumference.Results: proposed nonparametric regression showed the varied effect of each independent variable over the time but other models achieved constant effect over the time that is in controversy with the inherent property of these natural phenomena.Conclusion: This study shows that this model and the spline nonparametric estimator could be useful in different areas of medical and health studies.

One of the major projects in the economic reform plan is settlement of distribution system. The main objectives of this project is to organize the distribution of goods, continuous monitoring of distribution, reducing transaction costs and improving efficiency in the distribution industry. In this context, the main purpose of this article is to review and evaluate the performance of distribution companies. According to definition, efficiency in economic literature means maximum output with definite amount of input or minimum input with definite amount of output. Today, two methods are used for efficiency evaluation that contains; 1) parametric method 2) non parametric method. In this essay, according to nonparametric method that is based on mathematical programming methods, efficiency of Wholesaler businesses in 2008, with two assumptions (constant return to scale and variable return to scale) is evaluated. Results show that under first assumption; Wholesaler businesses Razi, Beh Pakhsh, Sanaye Morgh Madar Iran, Pakhsh Pegah, Saie Saman, Parnian Mahyar and Pakhsh Alborz have the most efficiency and the efficiency average is 58%. In according to second assumption; Wholesaler businesses Ghasem Iran, Sahamie Am Kaf, Daro Pakhsh, Pakhsh Hejrat, Pakhsh Ferdos, Henkel Pak Vash, Pakhsh Pishgam, Kimia Pakhsh Fam, Jahan Dam daro, Darosazan Iran, Poyshe Alke, Livar, Dampezeshkan Amin and Zarin 6 Iranian Journal of Trade Studies Pakhsh Nasr join the efficients and that efficiency average based on this assumption is %71.

Estimation of a quantile density function from biased data is a frequent problem in industrial life testing experiments and medical studies. The estimation of a quantile density function in the biased nonparametric regression model is inves-tigated. We propose and develop a new wavelet-based methodology for this problem. In particular, an adaptive hard thresholding wavelet estimator is constructed. Under mild assumptions on the model, we prove that it enjoys powerful mean integrated squared error properties over Besov balls. The performance of proposed estimator is investigated by a numerical study. In this study, we develop two types of wavelet estimators for the quantile density function when data comes from a biased distribution function. Our wavelet hard thresholding estimator which is introduced as a nonlinear estimator, has the feature to be adaptive according to q(x). We show that these estimators attain optimal and nearly optimal rates of convergence over a wide range of Besov function classes.

In the meta-analysis of clinical trials, usually the data of each trail summarized by one or more outcome measure estimates which reported along with their standard errors. In the case that summary data are multi-dimensional, usually, the data analysis will be performed in the form of a number of separated univariate analysis. In such a case the correlation between summary statistics would be ignored. In contrast, a multivariate meta-analysis model, use from these correlations synthesizes the outcomes, jointly to estimate the multiple pooled effects simultaneously. In this paper, we present a nonparametric Bayesian bivariate random effect meta-analysis.

This paper presents approximate confidence intervals for each function of parameters in a Banach space based on a bootstrap algorithm. We apply kernel density approach to estimate the persistence landscape. In addition, we evaluate the quality distribution function estimator of random variables using integrated mean square error (IMSE). The results of simulation studies show a significant improvement achieved by our approach compared to the standard version of confidence intervals algorithm. Finally, real data analysis shows that the accuracy of our method compared to that of previous works for computing the confidence interval.

Discriminant analysis is a way for classification of one object or a group to one or more separate groups that are known or unknown. in scientific researches we often use linear or quadratic functions for classification. But in this paper, we suggest a nonlinear discrimination method that uses two nonparametric.regression methods, namely multivariate adaptive regression splines and adaptive additive model in a simulation study, we investigate the application way of proposed methods and comparing them with the ordinary nonlinear discrimination methods via their means of error rates.

Introduction: To assess efficiency as a relative concept, the difference between real and expected values must be determined by comparing the productivity of economic units of industry and the efficiency in potential production conditions. As economic units, hospitals need to utilize efficiency analysis in order to make optimal use of their available resources. Therefore, the present study compared the efficiency of different hospitals of Shahid Beheshti University of Medical Sciences.Methods: Since this applied analytical research referred to the documents and performance statistics of hospitals and therfore used real data, evaluating the validity and reliability had no relevance. In order to measure the relative efficiency of health care in 16 hospitals associated with Shahid Beheshti University of Medical Sciences and also to determine efficient and inefficient hospitals during 2005-2007, CCR (Charnes-Cooper-Rhodes) input oriented multiplier model of data Envelopment analysis (DEA) was used for data analysis. In addition, AP (Anderson-Peterson) model was used to rank efficient hospitals. LINDO software was utilized to solve the model.Results: Based on our results, 43.75%, 31.25% and 37.5% of hospitals were efficient respectively in 2005, 2006 and 2007.Conclusion: The results suggested that the efficiency of inefficient hospitals could be improved through the potential savings in resources (62.44% in infrastructure, 43.88% in the number of active beds, 37.15% in the number of physicians and 38.58% in the number of paramedics).