<abstract> <p>This paper addresses neutrosophic statistics that will be used to design a double- acceptance sampling plan. We will design the sampling plans when the lifetime of the product follows the neutrosophic Weibull distribution. The plan parameters of the proposed double sampling plan will be determined using nonlinear optimization at various indeterminacy values and parameters. The productivity of the double sampling plan using neutrosophic statistics over the sampling plan under classical statistics will be given. The presentation of the proposed double sampling plan will be given with the help of industrial data.</p> </abstract>

In this paper, the neutrosophic trimmed average, neutrosophic trimmed standard deviation, and neutrosophic trimmed coefficient of variation (NTCV) are introduced. The application of the proposed neutrosophic trimmed descriptive statistics is given with the help of measurement data. The comparisons of the proposed NTCV are compared with the existing neutrosophic coefficient of variation (NCV). From the comparisons, it is concluded that the proposed NTCV is more efficient than NCV in terms of consistency and measures of indeterminacy. Based on the study, it is recommended to apply the proposed NTCV in the industry when there is a need to make decisions on the basis of measurement data.

The focus of this thesis is to review the three basic penalty estimators, namely, ridge regression estimator, LASSO, and elastic net estimator in the light of the deficiencies of least-squares estimator. Ill-conditioned design matrix is the major source of problem in this case. To overcome this problem, ridge regression was developed, and it opened the door for penalty estimators. Its impact is visible with various linear and non-linear models. A superb discovery in the class of subset selection is the LASSO (Least Absolute Shrinkage and Selection Operator) which selects subsets and estimates the coefficients simultaneously. Finally, we consider the elastic net penalty estimator which combine the L 1 and L 2 penalty function. Resulting estimator is weighted LASSO by ridge factor. We obtain the L 2 -risk expressions and compare with pre-test and Stein-type estimators. For the location model, we discovered that the naive elastic net is better than elastic net estimators as opposed to the conclusion in the current literature. On the other hand in case of regression model, the elastic net performs reasonably compared to LASSO and ridge regression.