A Procedure for Outlier Identification in Data Sets from Continuous Distributions

Autores: Adolfo J. Quiroz, N. Balakrishnan
Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 13, Nº. 1, 2004, págs. 247-262
Idioma: inglés
DOI: 10.1007/bf02603008
Texto completo no disponible (Saber más ...)
Resumen
- We propose a procedure, based on sums of reciprocals of p-values, for the identification of outliers in univariate or multivariate data sets coming from continuous distributions. Using results of Csörgö (1990), we find the limiting distribution of the relevant statistic for completely specified models. By simulations, we obtain approximate quantiles for the asymptotic distribution (which does not depend on the specific model or the dimension where the data live) and for the finite sample distribution in different dimensions of our statistic when parameters are estimated, for the multivariate Gaussian model and a multivariate double exponential model with independent coordinates. Monte Carlo evaluation shows that the procedure proposed is effective in the identification of outliers, and that it is sensitive to sample size, a feature seldom found in outlier identification methods.