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Asymptotic efficient estimation of the change point with unknown distributions. (English) Zbl 0714.62027
Summary: Suppose \(X_ 1,...,X_ n\) are distributed according to a probability measure under which \(X_ 1,...,X_ n\) are independent, \(X_ i\sim F_ 0\), for \(i=1,...,[\theta_ nn]\) and \(X_ i\sim F^{(n)}\) for \(i=[\theta_ nn]+1,...,n\), where [x] denotes the integer part of x.
We consider the asymptotic efficient estimation of \(\theta_ n\) when the distributions are not known. Our estimator is efficient in the sense that if \(F^{(n)}=F_{\eta_ n}\), \(\eta_ n\to 0\) and \(\{F_{\eta}\}\) is a regular one-dimensional parametric family of distributions, then the estimator is asymptotically equivalent to the best regular estimator.

MSC:
62G05 Nonparametric estimation
62G20 Asymptotic properties of nonparametric inference
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