1 |
Anselin, L. (1992). Spatial econometrics: Method and models, Kluwer Academic Publishers, Boston.
|
2 |
Brunsdon, C. and Fotheringham, A. S. (1999). Some notes on Parametric signicance tests for geographically weighted regression. Journal of Regional Science, 39, 497-524.
DOI
|
3 |
Fotheringham, A. S., Brunsdon, C. and Charlton, M. (2002). Geographically weighted regressio, John Wiley and Sons, Chichester, UK.
|
4 |
Fotheringham, A. S., Charlton, M. E. and Brunsdon, C. (1996). The geography of parameter space: An investigation of spatial non-stationarity. International Journal of Geographical Information Science, 10, 605-627
DOI
|
5 |
Hwang, C. and Shim, J. (2016). Deep LS-SVM for regression. Journal of the Korean Data & Information Science Society, 27 827-833.
DOI
|
6 |
Hwang, C., Bae, J. and Shim, J. (2016). Robust varying coecient model using L1 penalized locally weighted regression. Journal of the Korean Data & Information Science Society, 27, 1059-1066.
DOI
|
7 |
Kimeldorf, G. and Wahba, G. (1971). Some results on Tchebychean spline functions. Journal of Mathe-matical Analysis and Applications, 33, 82-95.
DOI
|
8 |
Mika, S., Ratsch, G., Weston, J., Schddoto lkopf, B. and Muller, K. R. (1999). Fisher discriminant analysis with kernels. IEEE International Workshop on Neural Networks for Signal Processing IX, Madison, WI, August, 41-48.
|
9 |
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65, 386-408.
DOI
|
10 |
Salvati, N., Ranalli, M. G. and Pratesi, M. (2011). Small area estimation of the mean using non-parametric M-quantile regression: A comparison when a linear mixed model does not hold. Journal of Statistical Computation and Simulation, 81, 945-964.
DOI
|
11 |
Saunders, C., Gammerman, A. and Vork, V. (1998). Ridge regression learning algorithm in dual variable. Proceedings of the 15th International Conference on Machine Learning, San Fransisco, CA, Morgan Kaufmann, 515-521.
|
12 |
Shim, J. Kim, C. and Hwang, C. (2011). Semiparametric least squares support vector machine for accelerated failure time model. Journal of Korean Statistical Society, 40, 75-83.
DOI
|
13 |
Vapnik, V. (1995). The nature of statistical learning theory, Springer, Berlin.
|
14 |
Smola, A. J., Friess, T. T. and Schlkopf, B. (1998). Semiparametric support vector and linear programming machines. Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems, Cambridge, MA, MIT Press, 585-591.
|
15 |
Suykens, J. A. K. and Vandewalle, J. (1999). Least squares support vector machine classiers. Neural Pro-cessing Letters, 9, 293-300.
DOI
|
16 |
Suykens, J. A. K., Vandewalle, J. and DeMoor, B. (2001). Optimal control by least squares support vector machines. Neural Networks, 14, 23-35.
DOI
|
17 |
Wahba, G. (1990). Spline models for observational data, CMMS-NSF Regional Conference Series in Applied Mathematics, 59, SIAM, Philadelphia.
|
18 |
Xu, J., Zhang, X. and Li, Y. (2001). Kernel MSE algorithm: A unied framework for KFD, LS-SVM. Proceedings of the International Joint Conference on Neural Networks, IJCNN 2001, Washington, DC, IEEE, 1486-1491.
|
19 |
Zhang, L. J. (2004). Modeling spatial variation in tree diameter-height relationships. Forest Ecology and Management, 189, 317-329.
DOI
|