Journal of Quality Engineering and Management

Abstract In time series analysis, ignoring outliers leads to misidentification of the model, biased estimation of parameters, and poor predictions. One of the reliable non-parametric methods in predicting and improving the quality of multivariate time series modeling is the multivariate Singular Spectrum Analysis (MSSA) technique, which does not require any initial assumptions. The presence of outliers affects the Frobenius norm of matrix and reduces the efficiency of the MSSA method. In this research, a new version of MSSA based on the L₁- norm is proposed. Then the performance of this method is compared with basic MSSA using simulation studies and real data.

Abstract The two parameter exponential distribution is particularly important among statistical distributions due to its constant failure rate and has applications in the fields of medicine, biology, clinical trials, public health, engineering, economics, demographics, and life span data, and reliability. Due to the importance of life span data, two parameter exponential distribution with censored data has recently attracted the attention of many researchers, but so far the inference about the location parameter with random censored data in the presence of outlier data has not been discussed. In this article, the location and scale parameters of two parameter exponential distribution under random censoring with the presence of k outliers are estimated by Bayesian and classical methods. Due to the importance of the spatial parameter, when censoring the two parameter exponential distribution with the presence of outlier data, the spatial parameter is considered the same but the scale parameter is different. In the Bayesian estimation of parameters, the is checked using Gibbs sampling under the error squared loss function. We recommend used the Bayesian estimation.
The generalized variance is given according to the dimensions of the parameters using the maximum likelihood method.