Conditional least squares estimation in nonstationary nonlinear stochastic regression models

DOI10.1214/09-AOS733zbMath1181.62133arXiv1001.2102MaRDI QIDQ847648

Christine Jacob

Publication date: 19 February 2010

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1001.2102

zbMATH Keywords

asymptotic distributionconsistencytime seriesheteroscedasticitybranching processconditional least squares estimatornonstationary processsubmartingalemartingale differencepolymerase chain reactionquasi-likelihood estimatorstochastic nonlinear regression

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic distribution theory in statistics (62E20) Non-Markovian processes: estimation (62M09) General nonlinear regression (62J02) Markov processes: estimation; hidden Markov models (62M05) Strong limit theorems (60F15) Martingales and classical analysis (60G46)

Related Items (5)

Stochastic methodology for the study of an epidemic decay phase, based on a branching model ⋮ Quasi- and pseudo-maximum likelihood estimators for discretely observed continuous-time Markov branching processes ⋮ Estimation of the infection parameter of an epidemic modeled by a branching process ⋮ Regression analysis of stochastic fatigue crack growth model in a martingale difference framework ⋮ LEAST SQUARES ESTIMATION FOR NONLINEAR REGRESSION MODELS WITH HETEROSCEDASTICITY

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