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 reviews Bayesian Nonparametric methods and discusses how parametric predictive densities can be constructed using nonparametric ideas.

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.

‎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 issues that investors are facing with in capital markets is decision making about selecting an appropriate stock exchange for investment and selecting an optimal portfolio. This process is done through the risk and expected return assessment. On the other hand, in portfolio selection problems if the assets' expected returns are normally distributed, variance and standard deviation are used as a risk measure. However, the expected returns on assets are not necessarily normal and sometimes have significant differences from normal distribution. This paper offers an optimal portfolio by introducing conditional value at risk (CVaR) as a measure of risk in a nonparametric framework considering a given expected return. This method is compared with the linear programming method.The data used in this study consists of monthly returns of 15 companies selected from the top 50 companies in Tehran Stock Exchange during the winter of 1392 which is considered from April of 1388 to June of 1393.The results of this study show the superiority of the nonparametric method over the linear programming method while the nonparametric method is much faster than the linear programming method.