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- W2187632958 abstract "Nonparametric estimation of a cumulative distribution function, F ,i s accomplished from data containing independent observations of two types. The first type of observation is simply a recorded value of a random variable X distributed according to F. The second type is incomplete (or censored) and contains partial information about X, namely only the indicator of the event (X ≤ d) is available. The value d belongs to ag rid{d1 ≤· · · ≤dr}, so the second type can be thought of as a stratified sample of dichotomous observations, each of them being represented as a pair containing a nonrandom dj and realizations of the indicator Yj = I(X ≤ dj). Asymptotically efficient estimates are derived for a cumulative distribution func- tion (CDF) and therefore, for a wide class of functionals that can be expressed via the CDF. Their limit distribution turns out to be normal, while this asymptotic nor- mality can be established uniformly with respect to any precompact set of CDF's. This uniformity implies asymptotic efficiency of the proposed estimates." @default.
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- W2187632958 modified "2023-09-27" @default.
- W2187632958 title "ASYMPTOTICALLY EFFICIENT NONPARAMETRIC ESTIMATION WITH ADDITIONAL DICHOTOMOUS OBSERVATIONS" @default.
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