1 |
Shim, J., Park, H. J. and Seok, K. H. (2008). Kernel Poisson regression for longitudinal data. Journal of Korean Data & Information Science Society, 19, 1353-1360.
과학기술학회마을
|
2 |
Yuan, M. and Wahba, G. (2004). Doubly penalized likelihood estimator in heteroscedastic regression. Statistics & Probability Letters, 69, 11-20.
DOI
ScienceOn
|
3 |
Fan, J. Q. and Yao, Q. W. (1998). Efficient estimation of conditional variance functions in stochastic regression. Biometrika, 85, 645-660.
DOI
ScienceOn
|
4 |
Mercer, J. (1909). Functions of positive and negative type and their connection with theory of integral equations. Philosophical Transactions of Royal Society, A, 415-446.
DOI
|
5 |
Hwang, C. (2008). Mixed effects kernel binomial regression. Journal of Korean Data & Information Science Society, 19, 1327-1334.
과학기술학회마을
|
6 |
Kimeldorf, G. S. and Wahba, G. (1971). Some results on Tchebycheffian spline functions. Journal of Mathematical Analysis and its Applications, 33, 82-95.
DOI
|
7 |
Suykens, J. A. K. and Vanderwalle, J. (1999). Least square support vector machine classifier. Neural Processing Letters, 9, 293-300.
DOI
ScienceOn
|
8 |
Xiang, D. and Wahba, G. (1996). A generalized approximate cross validation for smoothing splines with non-gaussian data. Statistian Sinica, 6, 675-692.
|
9 |
Anderson, T. G. and Lund, J. (1997). Estimating continuous-time stochastic volatility models of short-term interest rate. Journal of Econometrics, 77, 343-377.
DOI
ScienceOn
|
10 |
Golub, G. H., Heath, M. and Wahba, G. (1979). Generalized cross validation as a method for choosing a good ridge parameter. Technometrics, 21, 215-223.
DOI
ScienceOn
|
11 |
Juditsky, A, Hjalmarsson, H., Benveniste, A., Deylon, B., Ljung, L., Sj o. berg, J. and Zhang, Q. (1995). Nonlinear black-box modelling in system identification: Mathematical foundations. Automatica, 31, 1725-1750.
DOI
ScienceOn
|
12 |
Liu, A., Tong, T. and Wang, Y. (2007). Smoothing spline estimation of variance functions. Journal of Computational and Graphical Statistics, 16, 312-329.
DOI
ScienceOn
|
13 |
Aizerman, M. A., Braverman, E. M. and Rozonoer, L. I. (1964). Theoretical foundation of potential function method in pattern recognition learning. Automation and Remote Control, 25, 821-837.
|
14 |
Ruppert, D., Wand, M. P., Holst, U. and Hossjer, O. (1997). Local polynomial variance-function estimation. Technometrics, 39, 262-73.
DOI
ScienceOn
|
15 |
Shim, J. and Lee, J. T. (2009). Kernel method for autoregressive data. Journal of Korean Data and Information Science Society, 20, 949-954.
과학기술학회마을
|
16 |
Shim, J., Park, H. J. and Seok, K. H. (2009). Variance function estimation with LS-SVM for replicated data. Journal of Korean Data and Information Science Society, 20, 925-931.
과학기술학회마을
|