Estimation in multiple linear regression Berkson model for processes with uncorrelated incre\-ments

DOI10.1016/j.jspi.2007.02.003zbMath1130.62092OpenAlexW2016584201MaRDI QIDQ2474370

Huang-Yu Chen, Tiee-Jian Wu

Publication date: 6 March 2008

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2007.02.003

zbMATH Keywords

Gauss-Markov theoremmeasurement errorcontinuous-time linear regressionprocesses with uncorrelated 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) Non-Markovian processes: estimation (62M09)

Uses Software

• fda (R)

Cites Work

• fda (R)
• Gait analysis and the bootstrap
• Time integrated least squares estimators of regression parameters of independent stochastic processes
• When the data are functions
• Functional data analysis
• Weighted least squares estimates in linear regression models for processes with uncorrelated increments
• Are There Two Regressions?
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