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In the present article, a two-sample test for testing location and/or scale parameter of two multivariate populations is proposed. Baumgartner et al. [A nonparametric test for the general two-sampl...

In the present article, we develop a nonparametric quality control chart for monitoring process variability based on in-control deciles with variable sampling interval policy. The control chart bas...

Traditional control charts like Hotelling $$T^2$$ are based on the assumption of multivariate normality and also inapplicable to high-dimensional data. A notion of data depth has been used to measure centrality of a given point in a given data cloud. The data depth inferences do not require multivariate normality and any constraint on the dimension of the data. Liu (J Am Stat Assoc 90(432):1380–1387, 1995) provided control charts for a multivariate processes based on data depth, and the performance of the chart is not reported. There exist few tests for the location parameter of multivariate distribution based on data depth. Using these tests, we proposed nonparametric control charts to detect a shift in the location parameter of the multivariate process. We investigate the performance of the proposed control charts using the average run-length measure for various distributions. Also, the control chart procedure is illustrated by using wine quality data.

This paper develops the cascaded models for classification of PIMA Indian diabetes database. The k-nearest neighbour method is used to impute the missing data and the processed data is used for further classification. This is done in two steps, in first step k-means clustering algorithm is used for extracting hidden patterns in data set then in second step the classification is done by using suitable classifier. k-means algorithm combined with artificial neural network classifier and k-means algorithm combined with logistic regression classifier achieve classification accuracy above 98%.