http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/30 Fri, 24 Apr 2026 21:48:15 GMT 2026-04-24T21:48:15Z http://http://starc.stthomas.ac.in:8080/xmlui:8080/xmlui/bitstream/id/1879da84-771a-4355-809e-e0b2cbf4d397/ http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/30 http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/403 FAST AND ROBUST BIVARIATE CONTROL CHARTS FOR INDIVIDUAL OBSERVATIONS Sajesh, T.A There are various circumstances where it is important to simultaneously monitor or control two or more related quality characteristics. Independently tracking these quality characteristics might be quite deceptive. Hotelling's T2 chart, in which the T2 statistics are generated using the classical estimates of location and scatter, is the most well-known multivariate process monitoring and control approach. It is well known that the existence of outliers in a dataset has a significant impact on classical estimators. Any statistic that is computed using the classical estimates will be distorted by even a single outlier. The non-robustness issue is investigated in this study, which also suggests four robust bivariate control charts based on the robust Gnandesikan-Kettenring estimator. This study employs four highly robust scale estimators, with the best breakdown point, namely the Qn estimator, Sn estimator, MAD estimator, and τ estimator, in order to robustify the Gnandesikan- Kettenring estimator. Through the use of a Monte Carlo simulation and a real-life data, the performance of the suggested control charts is assessed. The four techniques all outperform the traditional method and provide greater computing efficienc Fri, 01 Dec 2023 00:00:00 GMT http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/403 2023-12-01T00:00:00Z http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/399 EMPIRICAL STUDY ON ROBUST REGRESSION ESTIMATORS AND THEIR PERFORMANCE Sajesh, T.A; Raveendran, Lakshmi Regression Analysis is statistical technique to model data. But the presence of outliers and influential points affect data modelling and its interpretation. Robust regression analysis is an alternative choice to this. Here we made an attempt to study different robust estimators and propose a new robust reweighted Sn covariance based regression estimator. We have evaluated the performance empirically and the simulation study shows our proposed estimator is preferable to OLS and other robust regression estimators in terms of the MSE criteria. Also, proposed robust Sn covariance regression estimator produce outperforming results for regression equivaraince and breakdown criterion. Robustness of the proposed estimator is proved empirically. The proposed method is innovatively used to model fluid data. R software is used for simulation and study Tue, 01 Aug 2023 00:00:00 GMT http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/399 2023-08-01T00:00:00Z http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/121 Sn covariance Kunjunni, Sajana O; Abraham, Sajesh T Main purpose of this paper is to study a robust measure of estimating dependence between random variables that can be used as an alternative to classical covariance estimator. An efficient univariate nested L-estimator (repeated median) Sn with high breakdown point is used to define bivariate dispersion. Results regarding in the characteristics of proposed estimator is discussed through this paper. Sun, 16 Jun 2019 00:00:00 GMT http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/121 2019-06-16T00:00:00Z http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/93 Multidimensional outlier detection and robust estimation using Sn covariance Kunjunni, Sajana O; Abraham, Sajesh T This article presents a robust method for detecting multiple outliers from multidimensional data using robust Mahalanobis distance. Initial scatter matrix for robust Mahalanobis distance is constructed using a robust estimator of covariance (SnCov) established from a robust scale estimator Sn and casewise median are chosen to be the location vector. The performance of the proposed method is evaluated using the results of simulated samples. This outlier detection method is compared with some well-known methods available in the current literature. The application of the proposed method in real-life data is also executed in this article. Mon, 17 Feb 2020 00:00:00 GMT http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/93 2020-02-17T00:00:00Z