'Lintah tidak sebar penyakit'

KUALA LUMPUR: Belum ada lagi kajian dilakukan menunjukkan lintah boleh menyebarkan penyakit kepada manusia melalui rawatan bekam termasuk penyakit batuk kering (tibi)

'Tak bertenaga sejak 3 hari': Pemergian Dr Awang Had kehilangan besar kepada negara

Kepemimpinan negara dan ahli akademik menyifatkan pemergian tokoh pendidikan, Prof Emeritus Tan Sri Dr Awang Had Salleh sebagai kehilangan besar kepada negara khususnya dalam bidang pendidikan dan bahasa

Yakin BN kekal memerintah

Ahli akademik yakin Barisan Nasional (BN) akan kekal memerintah pada 5 Mei ini, termasuk memenangi semula kerusi yang tewas dengan majoriti di bawah 1,000 undi pada Pilihan Raya Umum (PRU) lalu

A review on outliers-detection methods for multivariate data

Data in practice are often of high dimension and multivariate in nature. Detection of outliers has been one of the problems in multivariate analysis. Detecting outliers in multivariate data is difficult and it is not sufficient by using only graphical inspection. In this paper, a nontechnical and brief outlier detection method for multivariate data which are projection pursuit method, methods based on robust distance and cluster analysis are reviewed. The strengths and weaknesses of each method are briefly discussed

Comparison of Robust Estimators’ Performance for Detecting Outliers in Multivariate Data

In multivariate data, outliers are difficult to detect especially when the dimension of the data increase. Mahalanobis distance (MD) has been one of the classical methods to detect outliers for multivariate data. However, the classical mean and covariance matrix in MD suffered from masking and swamping effects if the data contain outliers. Due to this problem, many studies used a robust estimator instead of the classical estimator of mean and covariance matrix. In this study, the performance of five robust estimators namely Fast Minimum Covariance Determinant (FMCD), Minimum Vector Variance (MVV), Covariance Matrix Equality (CME), Index Set Equality (ISE),and Test on Covariance (TOC) are investigated and compared. FMCD has been widely used and is known as among the best robust estimator. However, there are certain conditions that FMCD still lacks. MVV, CME, ISE and TOC are innovative of FMCD. These four robust estimators improve the last step of the FMCD algorithm. Hence, the objective of this study is to observe the performance of these five estimator to detect outliers in multivariate data particularly TOC as TOC is the latest robust estimator. Simulation studies are conducted for two outlier scenarios with various conditions. There are three performance measures, which are pout, pmask and pswamp used to measure the performance of the robust estimators. It is found that the TOC gives better performance in pswamp for most conditions. TOC gives better results for pout and pmask for certain conditions