Prediction-based estimating functions: Review and new developments

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

The general theory of prediction-based estimating functions for stochastic process models is reviewed and extended. Particular attention is given to optimal estimation, asymptotic theory and Gaussian processes. Several examples of applications are presented. In particular, partial observation of a system of stochastic differential equations is discussed. This includes diffusions observed with measurement errors, integrated diffusions, stochastic volatility models, and hypoelliptic stochastic differential equations. The Pearson diffusions, for which explicit optimal prediction-based estimating functions can be found, are briefly presented.
TidsskriftBrazilian Journal of Probability and Statistics
Udgave nummer3
Sider (fra-til)362-391
StatusUdgivet - 2011

ID: 34253429