Bayesian multiscale analysis for time series data

Computational Statistics and Data Analysis

Volumn 51, Issue 3, 2006, Pages 1719-1730

  Øigård, Tor Arne ^a   Rue, Håvard ^b   Godtliebsen, Fred ^a  

^a UNIVERSITY OF TROMSØ   (Norway)

^b NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

Author keywords

Gaussian Markov random fields; Multiscale analysis; SiZer; Sparse matrices; Statistical inference; Time series analysis

Indexed keywords

ALGORITHMS; MARKOV PROCESSES; MATHEMATICAL MODELS; TIME SERIES ANALYSIS;

GAUSSIAN MARKOV RANDOM FIELDS; MULTISCALE ANALYSIS; SPARSE MATRICES; STATISTICAL INFERENCE;

DATA REDUCTION;

EID: 33750691984     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2006.07.034     Document Type: Article

References (13)

1. Chaudhuri P., and Marron J.S. SiZer for exploration of structures in curves. J. Amer. Statist. Assoc. 94 (1999) 807-823
2. Erästö, P., 2005. Studies in trend detection of scatter plots with visualization. Doctor of Philosophy Thesis. Department of Mathematics and Statistics, University of Helsinki, Finland.
3. Godtliebsen F., and Øigård T.A. A visual display device for significant features in complicated signals. Comput. Statist. Data Anal. 48 (2005) 317-343
4. Golub G.H., and van Loan C.F. Matrix Computations. third ed (1996), Johns Hopkins University Press, Baltimore
5. Jones, R.H., 1981. Fitting a continuous time autoregression to discrete data. Applied Time Series Analysis, vol. II, Tulsa, Oklahoma, 1980. Academic Press, New York, pp. 651-680.
6. Knorr-Held L., and Rue H. On block updating in Markov random field models for disease mapping. Scand. J. Statist. 29 (2002) 597-614
7. Kohn R., and Ansley C.F. A new algorithm for spline smoothing based on smoothing a stochastic process. SIAM J. Sci. Statist. Compt. 8 (1987) 33-48
8. Lindeberg T. Scale-Space Theory in Computer Vision (1994), Kluwer, Dordrecht
9. Rue H. Fast sampling of Gaussian Markov random fields. J. Roy. Statist. Soc. Ser. B 63 (2001) 325-338
10. Rue H., and Held L. Gaussian Markov Random Fields: Theory and Applications (2005), Chapman & Hall, London
11. Rue, H., Martino, S., 2006. Approximate Bayesian inference for hierarchical Gaussian Markov random fields models. J. Statist. Plann. Inference, to appear.
12. Wahba G. Improper priors, spline smoothing and the problem of guarding against model errors in regression. J. Roy. Statist. Soc. Ser. B 40 (1978) 364-372
13. Wecker W.E., and Ansley C.F. The signal extraction approach to non-linear regression and spline smoothing. J. Amer. Statist. Assoc. 78 (1983) 81-89