Generalized Linear Regression on Sampled Signals and Curves: A P-Spline Approach

REFERENCES

• Alsberg , B. K. 1993 . “Representation of Spectra by Continuous Functions,” . Journal of Chemometrics , 7 : 177 – 193 .
   Web of Science ®Google Scholar
• Brown , P. J. and Mäkeläinen , T. 1992 . “Regression, Sequenced Measurements and Coherent Calibration,”. ” . In Bayesian Statistics 4 Edited by: Bernardo , J. M. 97 – 108 . Oxford , , U.K. : Clarendon Press. .
   Google Scholar
• de Boor , C. 1978 . A Practical Guide to Splines New York : Springer-Verlag. .
   Google Scholar
• Denham , M. C. and Brown , P. J. 1993 . “Calibration With Many Variables,” . Applied Statistics , 42 : 515 – 528 .
   Web of Science ®Google Scholar
• Dierckx , P. 1993 . Curve and Surface Fitting With Splines Oxford , , U.K. : Clarendon Press. .
   Google Scholar
• Dobson , A. J. 1990 . An Introduction to Generalized Linear Models London : Chapman and Hall. .
   Google Scholar
• Eilers , P. H. C. and Marx , B. D. 1996 . “Flexible Smoothing Using B-Splines and Penalized Likelihood” (with comments and rejoinder) . Statistical Science , 11 : 89 – 121 .
   Web of Science ®Google Scholar
• Fahrmeir , L. and Tutz , G. 1994 . Multivariate Statistical Modeling Based on Generalized Linear Models Berlin : Springer-Verlag. .
   Google Scholar
• Frank , I. E. and Friedman , J. H. 1993 . “A Statistical View of Some Chemometric Regression Tools,” . Technometrics , 35 : 109 – 148 .
   Web of Science ®Google Scholar
• Friedman , J. H. and Silverman , B. W. 1989 . “Flexible Parsimonious Smoothing and Additive Modeling” (with discussion) . Technometrics , 31 : 3 – 39 .
   Web of Science ®Google Scholar
• Goutis , C. and Fearn , T. 1996 . “Partial Least Squares on Smooth Factors,” . Journal of the American Statistical Association , 91 : 627 – 632 .
   Web of Science ®Google Scholar
• Hastie , T. , Buja , A. and Tibshirani , R. 1995 . “Penalized Discriminant Analysis,” . The Annals of Statistics , 23 : 73 – 102 .
   Web of Science ®Google Scholar
• Hastie , T. and Mallows , C. 1993 . Discussion of “A Statistical View of Some Chemometrics Regression Tools,” . Technometrics , 35 : 140 – 143 . by I. E. Frank and J. H. Friedman
   Web of Science ®Google Scholar
• Hastie , T. and Tibshirani , R. 1990 . Generalized Additive Models London : Chapman and Hall. .
   Google Scholar
• Helland , I. S. 1988 . “On the Structure of Partial Least Squares Regression,” . Communications in Statistics, Part B—Simulation and Computation , 17 : 581 – 607 .
   Web of Science ®Google Scholar
• Kooperberg , C. , Stone , C. J. and Truong , Y. K. 1995 . “Hazard Regression,” . Journal of the American Statistical Association , 90 : 78 – 94 .
   Web of Science ®Google Scholar
• Land , S. R. and Friedman , J. H. 1996 . “Variable Fusion: A New Method of Adaptive Signal Regression,” Stanford University, Dept. of Statistics. . Technical Report 114
   Google Scholar
• le Cessie , S. and van Houwelingen , J. C. 1992 . “Ridge Estimators in Logistic Regression,” . Applied Statistics , 41 : 191 – 201 .
   Web of Science ®Google Scholar
• Martens , H. and Naes , T. 1989 . Multivariate Calibration New York : Wiley. .
   Google Scholar
• Marx , B. D. 1996 . “Iteratively Reweighted Partial Least Squares Estimation for Generalized Linear Regression,” . Technometrics , 38 : 374 – 381 .
   Web of Science ®Google Scholar
• Marx , B. D. and Eilers , P. H. C. 1998 . “Direct Generalized Additive Modeling With Penalized Likelihood,” . Computational Statistics and Data Analysis , 28 : 193 – 209 .
   Web of Science ®Google Scholar
• Marx , B. D. and Smith , E. P. 1990 . “Principal Component Estimation for Generalized Linear Regression,” . Biometrika , 77 : 23 – 31 .
   Web of Science ®Google Scholar
• McCullagh , P. and Nelder , J. A. 1989 . Generalized Linear Models, , 2nd ed. London : Chapman and Hall. .
   Google Scholar
• Myers , R. H. 1990 . Classical and Modern Regression With Applications, , 2nd ed. Boston : PWS-Kent. .
   Google Scholar
• Osborne , B. G. , Fearn , T. , Miller , A. R. and Douglas , S. 1984 . “Application of Near Infrared Reflectance Spectroscopy to the Compositional Analysis of Biscuits and Biscuit Dough,” . Journal of Scientific Food Agriculture , 35 : 99 – 105 .
   Web of Science ®Google Scholar
• O'Sullivan , F. 1986 . “A Statistical Perspective on Ill-Posed Problems” (with discussion) . Statistical Science , 1 : 505 – 527 .
   Google Scholar
• Reinsch , C. 1967 . “Smoothing by Spline Functions,” . Numerische Mathematik , 10 : 177 – 183 .
   Web of Science ®Google Scholar
• Stone , M. and Brooks , R. J. 1990 . “Continuum Regression: Cross-Validated Sequentially Constructed Prediction Embracing Ordinary Least Squares, Partial Least Squares and Principal Components Regression,” . Journal of the Royal Statistical Society, Ser. B , 52 : 237 – 269 .
   Google Scholar
• Wold , H. 1975 . “Soft Modeling by Latent Variables: The Nonlinear Iterative Partial Least Squares Approach,”. ” . In Perspectives in Probability and Statistics, Papers in Honour of M.S. Bartlett Edited by: Gani , J. 117 – 142 . London : Academic Press. .
   Google Scholar