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Abstract
The Definition of Computational Statistics Clearly, computational statistics has a connection to the field of statistics as a whole. Determining what we mean by the area of statistics is therefore necessary before we define computational statistics in its appropriate sense. At its most fundamental level, statistics deals with turning unprocessed data into knowledge [Wegman, 1988]. Any scientist who is faced with an application that calls for the analysis of raw data must consider issues like:
- What data should be collected to answer the questions in the analysis?
- How much information needs to be gathered?
- What conclusions can be made based on the information?
How much of those conclusions can be believed? The science of uncertainty is a topic that statistics addresses, and it can assist the scientist in answering these queries.
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References
Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850. Journal of Geophysical Research 111, D12106. doi:10.1029/2005JD006548. Buckley, M.J., 1994.
Fast computation of a discretized thin-plate smoothing spline for image data. Biometrika 81, 247–258. Chatterjee, S., Hadi, A.S., 1986.
Influential observations, high leverage points, and outliers in linear regression. Statistical Science 1, 379–393. Craven, P., Wahba, G., 1978.
Smoothing noisy data with spline functions. Estimating the correct degree of smoothing by the method of generalized crossvalidation. Numerische Mathematik 31, 377–403. Eilers, P.H., 2003. A perfect smoother. Anal. Chem. 75, 3631–3636. Garcia, D.,
Matlab functions in BioméCardio. http://www.biomecardio.com/matlab, 2009. Ref Type: Electronic Citation. Golub, G., Heath, M., Wahba, G., 1979.
Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics 21, 215–223. Hastie, T., Loader, C., 1993. Local regression: Automatic kernel carpentry.
Statistical Science 8, 120–129. Heiberger, R.M., Becker, R.A., 1992.
Design of an S function for robust regression using iteratively reweighted least squares. Journal of Computational and Graphical Statistics 1, 181–196. Hoaglin, D.C., Welsch, R.E., 1978.
The hat matrix in regression and ANOVA. The American Statistician 32, 17–22. Keller, H.B., 1965.
On the solution of singular and semidefinite linear systems by iteration. Journal of the Society for Industrial and Applied Mathematics: Series B, Numerical Analysis 2, 281–290. Kennedy, J., Met Office Hadley Centre observations datasets. http://hadobs.metoffice.com/crutem3, 2007. Ref Type: Electronic Citation. Rousseeuw, P.J., Leroy, A.M., 1987.