Asymptotic theory of conditional inference for stochastic processes

DOI10.1016/0304-4149(86)90116-XzbMath0611.62105WikidataQ126654393 ScholiaQ126654393MaRDI QIDQ1087283

Paul D. Feigin

Publication date: 1986

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

zbMATH Keywords

conditional inferencelocally asymptotically mixed normalapproximate ancillarityasymptotic conditional riskdecision theoretic conditionalityLAMNlocal ancillaritylocal asymptotic theorynonergodic stochastic process

Mathematics Subject Classification ID

Statistical decision theory (62C99) Non-Markovian processes: hypothesis testing (62M07)

Related Items

Stable convergence of the log-likelihood ratio to a mixture of infinitely divisible distributions, Unit root tests for cross-sectionally dependent panels: the influence of observed factors

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Stable convergence of semimartingales
• Asymptotic conditional inference for regular nonergodic models with an application to autoregressive processes
• Asymptotic optimal inference for non-ergodic models
• A note on asymptotic inference in a class of non-stationary processes
• Asymptotic tests of composite hypotheses for non-ergodic type stochastic processes
• Conditional exponential families and a representation theorem for asymptotic inference
• On mixing and stability of limit theorems
• The efficiency criteria problem for stochastic processes
• On asymptotic ancillarity and inference for Yule and regular nonergodic processes
• Local ancillarity
• Efficient conditional tests for mixture experiments with applications to the birth and branching processes
• Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information
• On the characterization of point processes with the order statistic property
• Estimation in the birth process