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Researchers often face the problem of how to address missing data. Multiple imputation by chained equations is one of the most common methods for imputation. In theory, any imputation model can be used to predict the missing values. However, if the predictive models are incorrect, it can lead to biased estimates and invalid inferences. One of the latest solutions for dealing with missing data is machine learning methods and the SuperMICE method. In this paper, We present a set of simulations indicating that this approach produces final parameter estimates with lower bias and better coverage than other commonly used imputation methods. Also, implementing some machine learning methods and an ensemble algorithm, SuperMICE, on the data of the Industrial establishment survey is discussed,  in which the imputation of different variables in the data co-occurs. Also, the evaluation of various methods is discussed, and the method that has better performance than the other methods is introduced.

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This paper investigates repetitive acceptance sampling inspection plans of lots based on type I censoring when the lifetime has a Tsallis q-exponential distribution. A repetitive acceptance sampling inspection plan is introduced, and its components, along with the optimal average sample number and the operating characteristic value of the plan, are calculated under the specified values for the parameter of distribution and consumer's and producer's risks using a nonlinear programming optimization problem. Comparing the results of the proposed repetitive acceptance sampling plan with the optimal single sampling inspection plan demonstrates the efficiency of the repetitive acceptance sampling plan over the single sampling plan. Moreover, repetitive sampling plans with a limited linear combination of risks are introduced and compared with the existing plan. Results of the introduced plan in tables and figures show that this plan has a lower ASN and, therefore, more efficiency than the existing design. A practical example in the textile industry is used to apply the proposed schemes.

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In this paper, a novel approach for forecasting a time sequence of probability density functions is introduced, which is based on Functional Singular Spectrum Analysis (FSSA). This approach is designed to analyze functional time series and address the constraints in predicting density functions, such as non-negativity and unit integral properties. First, appropriate transformations are introduced to convert the time series of density functions into a functional time series. Then, FSSA is applied to forecast the new functional time series, and finally, the predicted functions are transformed back into the space of density functions using the inverse transformation. The proposed method is evaluated using real-world data, including the density of satellite imagery.