The use of local likelihood methods
(Tibshirani and Hastie, 1987; Loader, 1996) in the presence
of interval-censored or aggregated data leads to a natural
consideration of an EM-type strategy, or a local EM
algorithm. We consider local EM to analyze point process
data that are either interval-censored or aggregated into
counts. We present the formulation of local EM algorithms
to estimate density, intensity and risk and their
implementations using piecewise constant functions. It is
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