spline/ai/met paper ancts

[Grace Wahba]

 Announcing new/revised manuscripts available 
 on the web .... 
    http/www.stat.wisc.edu/~wahba  click on `TRLIST'
    also available by ftp in files listed at the end of this msg

   Luo, Z. and Wahba, G. "Hybrid Adaptive Splines" TR 947, June 1995,
   submitted. --- Combines ideas from smoothing splines and MARS to 
   obtain spatial adaptivity, numerical efficiency. Numerical comparisons
   to wavelet simulations in Donoho et al, JRSSB 57, 1995. Approach 
   parallels Orr, Neural Computation 7,1995, which we just became 
   aware of-similar results on a different class of examples --.

   Wahba, G., Wang, Y., Gu, C., Klein, R. and Klein, B. " Smoothing Spline
   ANOVA for Exponential Families, with Application to the Wisconsin
   Epidemiological Study of Diabetic Retinopathy." May 1995, to appear,
   Annals of Statistics. --Expanded and *slightly revised* version of TR
   940, December 1994. This paper was the basis for the Neyman
   Lecture given at the Annual Meeting of the Institute of
   Mathematical Statistics at Chapel Hill 1994, delivered by the first
   author. Collects results from SS-ANOVA, algorithm for 
   choosing multiple smoothing parameters for Bernoulli data, implementing
   confidence intervals. Applies results to the estimation of four-
   year risk of progression of diabetic retinopathy, using data 
   from the Wisconsin Epidemiological Study of Diabetic Retinopathy.
   (data available in GRKPACK documentation above). 
   ***A discussion of the backfitting algorithm and SS-ANOVA has 
   been added***--.

   Xiang, D. and Wahba, G. " A Generalized Approximate Cross Validation for
   Smoothing Splines with Non-Gaussian Data." TR 930, September 1994,
   submitted. --Another look at choosing the regularization parameter
   for Bernoulli data. Parallels work of Moody, Liu in the NN literature.

   Wahba, G., Johnson, D. R., Gao, F. and Gong, J. " Adaptive tuning of
   numerical weather prediction models: Part I: randomized GCV and related
   methods in three and four dimensional data assimilation." TR 920, April
   1994. *revised and shortened* version to appear, Monthly Weather Review
   --describes how to use the randomized trace method to
   implement GCV in very large problems in 
   numerical weather prediction, approximate solution of pde's with 
   discrete noisy observations.

   Also available  by ftp in gzipped postscript in 
   ftp.stat.wisc.edu/pub/wahba in the following files, 
   respectively

            has.ps.gz
	    exptl.ssanova.rev.ps.gz
            gacv.ps.gz 
	    tuning-nwp.rev.ps.gz