Abstract
Asymptotic statistical theory for estimating functions is reviewed in a generality
suitable for stochastic processes. Conditions concerning existence of a consistent estimator,
uniqueness, rate of convergence, and the asymptotic distribution are treated separately. Our
conditions are not minimal, but can be verified for many interesting stochastic processmodels.
Several examples illustrate the wide applicability of the theory and why the generality is
needed.
suitable for stochastic processes. Conditions concerning existence of a consistent estimator,
uniqueness, rate of convergence, and the asymptotic distribution are treated separately. Our
conditions are not minimal, but can be verified for many interesting stochastic processmodels.
Several examples illustrate the wide applicability of the theory and why the generality is
needed.
| Original language | English |
|---|---|
| Journal | Statistical Inference for Stochastic Processes |
| Volume | 21 |
| Issue number | 2 |
| Pages (from-to) | 415-434 |
| ISSN | 1387-0874 |
| DOIs | |
| Publication status | Published - 2018 |