Weighted least squares estimates in linear regression models for processes with uncorrelated increments

DOI10.1016/S0304-4149(96)00076-2zbMath0881.62097WikidataQ127861589 ScholiaQ127861589MaRDI QIDQ1374641

M. T. Wasan, Tiee-Jian Wu

Publication date: 10 December 1997

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

zbMATH Keywords

Gauss-Markov theoremcontinuous-time multiple linear regressionstochastic processes with orthogonal increments

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear regression; mixed models (62J05) Point estimation (62F10) Stochastic processes (60G99)

Related Items (2)

Linear sufficiency and linear admissibility in a continuous time Gauss-Markov model. ⋮ Estimation in multiple linear regression Berkson model for processes with uncorrelated incre\-ments

Cites Work

• Strong consistency of least squares estimates in multiple regression II
• Gait analysis and the bootstrap
• Time integrated least squares estimators of regression parameters of independent stochastic processes
• When the data are functions
• Strong consistency of least squares estimates in linear regression models driven by semimartingales
• Convergence systems and strong consistency of least squares estimates in regression models
• Order of convergence of regression parameter estimates in models with infinite variance
• An Approach to Time Series Analysis
• Recursive parameter estimation for counting processes with linear intensity
• Quasi-least-squares estimation in semimartingale regression models
• A Remark on Strong Consistency of LS Estimates under Weakly Correlated Observation Errors
• Weak and strong consistency of the least squares estimators in regression models
• Linear Statistical Inference and its Applications
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