Volume 20 - Article 25 | Pages 599–622
Graduating the age-specific fertility pattern using Support Vector Machines
By Anastasia Kostaki, Javier Moguerza, Alberto Olivares, Stelios Psarakis
References
Aronszajn, N. (1950). Theory of reproducing kernels. Trans. Amer. Math. Soc. 68: 337-404.
Brockmann, M., Gasser, T., and Herrmann, E. (1993). Locally Adaptive Bandwidth choice for kernel regression estimators. Journal of the American Statistical Association 88(424): 1302-1309.
Chandola, T., Coleman, D. A., and Horns, R. W. (2002). Distinctive features of age-specific fertility profiles in the English-speaking world: Common patterns in Australia, Canada, New Zealand and the United States, 1970-98. Population Studies 56: 181-200.
Chandola, T., Coleman, D. A., and Horns, R. W. (1999). Recent European fertility patterns: fitting curves to 'distorted' distributions. Population Studies 53(3): 317-329.
Eurostat New Cronos Database (2006). Europa database: Population and Social Conditions [electronic resource]. Manchester UK.
Gasser, T., Kneip, A., and Kohler, W. (1991). A flexible and fast method for automatic smoothing. Journal of the American Statistical Association 86(415): 643-652.
Gasser, T. and Müller, H. (1979). Kernel estimation of regression functions. In: Smoothing Techniques for Curve Estimation. Lecture Notes in Mathematics 757. New York: Springer-Verlag: 23-68.
Gasser, T. and Müller, H. G. (1984). Estimating regression functions and their derivatives by the kernel method. Scandinavian Journal of Statistics 11: 171-185.
Gilje, E. (1969). Fitting curves to age-specific fertility rates: some examples. Statistical Review of the Swedish National Central Bureau of Statistics III 7: 118-134.
Hadwiger, H. (1940). Eine analytische reprodutions-funktion fur biologische Gesamtheiten. Skandinavisk Aktuarietidskrift 23: 101-113.
Hardle, W. (1990). Applied Non parametric Regression. Cambridge: Cambridge University Press.
Hardle, W. (1991). Smoothing Techniques with Implementation in S. New-York: Springer-Verlag.
Herrmann, E. (1997). Local bandwidth choice in kernel regression estimation. Journal of Computational and Graphical Statistics 6(1): 35-54.
Hoem, J. M. (1976). The statistical theory of demographic rates: A review of current denvelopments (with discussion). Scandinavian Journal of Statistics 3: 169-185.
Hoem, J. M., Madsen, D., Nielsen, J. L., Ohlsen, E., Hansen, H. O., and Rennermalm, B. (1981). Experiments in modelling recent Danish fertility curves. Demography 18: 231-244.
Kostaki, A. and Peristera, P. (2005). Graduating mortality data using Kernel techniques: Evaluation and comparisons. Journal of Population Research 22(2): 185-197.
Moguerza, J. and Muñoz, A. (2006). Support vector machines with applications. Statistical Science 21(3): 322-336.
Moguerza, J., Muñoz, A., and Psarakis, S. (2007). Monitoring nonlinear profiles using support vector machines. Lecture Notes in Computer Science 4789: 574-583.
Muñoz, A. and Moguerza, J. (2005). Building smooth neighbourhood kernels via functional data analysis. Lecture Notes in Computer Science 3697: 631-636.
Pearce, N.D. and Wand, M.P. (2006). Penalized Splines and Reproducing Kernel Methods. The American Statistician 60(3): 233-240.
Peristera, P. and Kostaki, A. (2007). Modeling fertility in modern populations. Demographic Research 16(6): 141-194.
Ruppert, D., Sheather, S. J., and Wand, M. P. (1995). An effective bandwidth selector for local least squares regression. Journal of the American Statistical Association 90: 1257-1270.
Schmertmann, C. P. (2003). A system of model fertility schedules with graphically intuitive parameters. Demographic Research 9(5): 82-110.
Schölkopf, B., Smola, A., Williamson, R., and Bartlett, P. (2000). New support vector algorithms. Neural Computation 12: 1207-1245.
Tikhonov, A.N. and Arsenin, V.Y. (1977). Solutions of ill-posed problems. John Wiley and Sons.
US National Centre of Health Statistics (2003). Natality Data set [electronic resource]. Maryland.
Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York: Springer.