Detecting Multiple Outliers in Multi Variate Samples with S-Estimation Method  

Oleh:

Noeryanti

Jenis: Article from Journal - ilmiah nasional
Dalam koleksi: Jurnal Teknologi Industri vol. 5 no. 4 (Oct. 2001), page 217-226.
Topik: S-Estimasi; Minimun Volume Ellipsoide Estimator; High Breakdown Point; Multiple-Outliers; Confirmatory Analysis

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: JJ83.3 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

An improved resampling algorithm for S-estimatiors reduces the number of tunes the objective function is evaluated and increases the speed of convergen. With this algorithm, S-estimates can be computed in less time than minimum volume ellipsoid (MVE) for location / scatter estimates with the same accuracy. Here accuracy refers to the randomness due to the algorithm and S-estimators are also more statistically efficient than the MVE estimators, that is, they have less variability due to the randomness of the data. The high breakdown point S-estimation robust method is used for detecting multiple outliers in several population. The method can avoid the common masking problem in outlier detection, but may tend to declare too many observations as extrem. The confirmatomy analysis is used for remedying this swamping problem. The methods are applied to simulated data sets, on the program S-Plus 2000.

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