Bayesian smoothing and regression splines for measurement error problems

Journal of the American Statistical Association

Volumn 97, Issue 457, 2002, Pages 160-169

  Berry, S M ^a   Carroll, R J ^b   Ruppert, D ^c  

^a Berry Consultants   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

^c Cornell University   (United States)

Author keywords

Bayesian methods; Efficiency; Errors in variables; Functional method; Generalized linear models; Kernel regression; Measurement error; Nonparametric regression; P splines; Regression splines; SIMEX method; Smoothing splines; Structural modeling

Indexed keywords

EID: 0036489053     PISSN: 01621459     EISSN: None     Source Type: Journal    
DOI: 10.1198/016214502753479301     Document Type: Note

References (25)

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2. "On the statistical analysis of dirty pictures" (with discussion)
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4. Flexible parametric measurement error models
5. Polynomial regression with errors in variables
6. Simulation-extrapolation estimation in parametric measurement error models
7. "Flexible smoothing with B-splines and penalties" (with discussion)
8. Nonparametric regression with errors in variables
9. Sampling-based approaches to calculating marginal densities
10. Inference from iterative simulation using multiple sequences
11. Bayesian backfitting
12. Maximum likelihood estimation via the ECM algorithm: A general framework
13. Bayesian confidence intervals for a smoothing spline
14. The average posterior variance of a smoothing spline and a consistent estimate of the average squared error
15. Spatially adaptive penalties for spline fitting
16. Nesting EM algorithms for computational efficieny
17. Improper priors, spline smoothing and the problem of guarding against model errors in regression
18. Bayesian 'Confidence intervals' for the cross-validated smoothing spline