Likelihood based inference for partially observed renewal processes

Abstract

a b s t r a c tThis paper is concerned with inference for renewal processes on the real line that are observed in a broken interval. For such processes, the classic history-based approach cannot be used. Instead, we adapt tools from sequential spatial point process theory to propose a Monte Carlo maximum likelihood estimator that takes into account the missing data. Its efficacy is assessed by means of a simulation study and the missing data reconstruction is illustrated on real data.

State estimation for aoristic models

“…However, it breaks down completely when censoring breaks the orderly progression of time. In such cases, state estimation techniques are needed that are able to fill in the gaps (Brix & Diggle, 2001; Lieshout, 2016).…”

State estimation for aoristic models

“…However, it breaks down completely when censoring breaks the orderly progression of time. In such cases, state estimation techniques are needed that are able to fill in the gaps (Brix & Diggle, 2001; Van Lieshout, 2016).…”