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According to the first nth observations of the upper record from exponential distribution, in this article, we can compute maximum likelihood estimation of this distribution parameter. We, then, concentrate on point prediction of the future upper record values in exponential distribution based both on classic and Bayes approaches and second degree and linex loss functions.We, ultimately, deal with numerical comparison available point predictions through Monte Carlo simulation.

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‎A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG)‎. ‎There are basically two methods used for learning Bayesian network‎: ‎parameter-learning and structure-learning‎. ‎One of the most effective structure-learning methods is K2 algorithm‎. ‎Because the performance of the K2 algorithm depends on node ordering‎, ‎more effective node ordering inference methods are needed‎. ‎In this paper‎, ‎based on the fact that the parent and child variables are identified by estimated Markov Blanket (MB)‎, ‎we first estimate the MB of a variable using Grow-Shrink algorithm‎, ‎then determine the candidate parents of a variable by evaluating the conditional frequencies using Dirichlet probability density function‎. ‎Then the candidate parents are used as input for the K2 algorithm‎. ‎Experimental results for most of the datasets indicate that our proposed method significantly outperforms previous method‎.  

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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.

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At the end of December 2019, the spread of a new infectious disease was reported in Wuhan, China, caused by a new coronavirus and officially named Covid-19 by the World Health Organization. As the number of victims of the virus exceeded 1,000, the World Health Organization chose the official name Covid-19 for the disease, which refers to "corona", "virus", "disease" and the year 2019.
 The forecasting about Covid-19 can help the government make better decisions. In this paper, an objective approach is used for forecasting Covid-19 based on the statistical methods. The most important goal in this paper is to forecast the prevalence of coronavirus for confirmed, dead and improved cases and to estimate the duration of the management of this virus using the exponential smoothing method. The exponential smoothing family model is used for short time-series data. This model is a kind of moving average model that modifies itself. In other words, exponential smoothing is one of the most widely used statistical methods for time series forecasting, and the idea is that recent observations will usually provide the best guidance for the future. Finally, according to the exponential smoothing, we will provide some suggestions.

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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.