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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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The classification of shape data is a significant challenge in the statistical analysis of shapes and machine learning. In this paper, we introduce a multinomial logistic regression model based on shape descriptors for classifying labeled configurations. In this model, the explanatory variables include a set of geometric descriptors such as area, elongation, convexity, and circularity, while the response variable represents the category of each configuration. The inclusion of these descriptors preserves essential geometric information and enhances classification accuracy. We evaluate the proposed model using both simulated data and real datasets, and the results demonstrate its effective performance. Additionally, the proposed method was compared with one of the existing methods in the literature, and the results indicated its superiority in terms of both classification accuracy and computational simplicity.

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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.

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In statistical research, experimental designs are used to investigate the effect of control variables on output responses. These methods are based on the assumption of normal distribution of data and face fundamental challenges in dealing with outliers. The present study examines five different examples of experimental design methods to deal with this challenge: Huber, quadratic, substitution, ranking, and fuzzy regression robustness methods. By providing empirical evidence from real data on seedling growth and weld quality, it is shown that fuzzy can be used as an efficient alternative to conventional methods in the presence of outliers. It is shown that fuzzy not only outperforms the classical experimental design method in the presence of outliers, but also outperforms standard robustness methods in handling outliers.

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Researchers develop generalized families of distributions to better model data in fields like risk management, economics, and insurance. In this paper, a new distribution, the Extended Exponential Log-Logistic Distribution, is introduced, which belongs to the class of heavy-tailed distributions. Some statistical properties of the model, including moments, moment-generating function, entropy, and economic inequality curves, are derived. Six estimation methods are proposed for estimating the model parameters, and the performance of these methods is evaluated using randomly generated datasets. Additionally, several insurance-related measures, including Value at Risk, Tail Value at Risk, Tail Variance, and Tail Variance Premium, are calculated. Finally, two real insurance datasets are employed, showing that the proposed model fits the data better than many existing related models.