On likelihood distance for outliers detection

The likelihood distance has been widely used to detect outlying observations in data analysis. Cook and Weisberg (5) suggested that the likelihood distance may be compared to a chi 2 distribution for large samples. In this paper, we show that use of the chi 2 distribution is inappropriate. The results indicate that the likelihood distance does not follow an asymptotically chi 2 distribution. Instead, it converges to 0 in probability as the sample size increases. We show that for a nondegenerate limiting distribution, a multiplication factor related to the sample size n is needed. In general, the limiting distribution of this modified statistic is model-dependent.