A general estimation method using spacings

Abstract

A general parametric estimation method which makes use of the coverage probabilities or spacings is proposed. Under some regularity conditions, it is shown that such estimators are asymptotically normal. This method generalizes the maximum spacing method of estimation that has been discussed in the literature. Furthermore, it is shown that the maximum spacing estimator is asymptotically most e cient within the subclass of spacings-based estimators under consideration.

Marshall-Olkin Extended Exponential Distribution: Different Method of Estimations

“…As it was mentioned by Cheng and Amin [11], Anatolyev and Kosenok [15] and Ghosh and Jammalamadaka [19] that the MPS method also shows asymptotic properties like the Maximum likelihood estimator and is asymptotically equivalent to MLE. Interval estimation using MPS is also discussed by Singh et al [17].…”

An Alternative to Maximum Likelihood Based on Spacings

“…In regular cases MPS estimates are consistent, asymptotically normal and efficient, which was derived in subsequent papers from the first principles (e.g. Shao and Hahn, 1999;Ghosh and Jammalamadaka, 2001).…”

“…As far as extensions of the MPS idea are concerned, Lind (1994) provides a rationale for the MPS in the information theory, Ekström (1997) suggests use of "high order" spacings, Ghosh and Jammalamadaka (2001) expand the class to contain estimators with objective functions based on various divergence criteria (interestingly, the MPS estimator is the only one asymptotically efficient in the extended class), and Ranneby, Jammalamadaka and Teterukovskiy (2004) propose a generalization to multivariate situations.…”

A test of goodness-of-fit based on Gini's index of spacings

“…See e.g., Ekstr om (1997) and Ghosh and Jammalamadaka (2001). A special case of this measure is the Kullback-Liebler distance, which takes the simple form n+1 i=1 log D i in this particular instance.…”