References
- Akaike, H. (1973). Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika 60:255–265.
- Aydin, D., Tuzemen, M. (2012). Smoothing parameter selection problem in nonparametric regression based on smoothing spline: A simulation study. Journal of Applied Sciences 12:636–644.
- Cavanaugh, J. (1997). Unifying the derivations for the Akaike and corrected Akaike information criteria. Statistics 33:201–208.
- Craven, P., Wahba, G. (1979). Smoothing noisy data with spline functions. Numerical Mathematics 31:377–403.
- Eilers, P., Marx, B. (1996). Flexible smoothing with B-splines and penalties. Statistical Science 11:89–121.
- Green, P., Silverman, B. (1994). Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach. Boca Raton, FL: Chapman & Hall/CRC.
- Guerrero, V. M. (2007). Time series smoothing by penalized least squares. Statistics and Probability Letters 77:1225–1234.
- Guerrero, V. M. (2008). Estimating trends with percentage of smoothness chosen by the user. International Statistical Review 76:187–202.
- Guerrero, V. M., Galicia-Vázquez, A. (2010). Trend estimation of financial time series. Applied Stochastic Models in Business and Industry 26:205–223.
- Henderson, R. (1924). A new method of graduation. Transactions of the Actuarial Society of America 25:29–40.
- Hodrick, R., Prescott, E. (1997). Postwar U.S. business cycles: An empirical investigation. Journal of Money Credit and Banking 29:1–16.
- Hurvich, C., Simonoff, J., Tsai, C. (1998). Smoothing parameter selection in nonparametric regression using an improved Akaike Information Criterion. Journal of the Royal Statistical Society, Series B 60:271–293.
- Kaiser, R., Maravall, A. (2001). Measuring Business Cycles in Economic Time Series. Lecture Notes in Statistics, Vol. 154. New York: Springer.
- King, R., Rebelo, S. (1993). Low frequency filtering and real business cycles. Journal of Economic Dynamics and Control 17:207–231.
- Kitagawa, G., Gersch, W. (1996). Smoothness Priors Analysis of Time Series. Lecture Notes in Statistics, Vol. 116. New York: Springer.
- Krivobokova, T. (2013). Smoothing parameter selection in two frameworks for penalized splines. Journal of the Royal Statistical Society, Series B 75:725–741.
- Lee, T. (2003). Smoothing parameter selection for smoothing splines: A simulation study. Computational Statistics and Data Analysis 42:139–148.
- Schwarz, G. E. (1978). Estimating the dimension of a model. Annals of Statistics 6:461–464.
- Theil, H. (1963). On the use of incomplete prior information in regression analysis. Journal of the American Statistical Association 58:401–414.
- Wahba, G. (1985). A comparison of GCV and GML for choosing the smoothing parameter in the generalized spline smoothing problem. Annals of Statistics 13:1378–1402.
- Wahba, G. (1990). Spline Models for Observational Data. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).
- Weinert, H. (2012). Fast Compact Algorithms and Software for Spline Smoothing. SpringerBriefs in Computer Science. New York: Springer.
- White, H., Granger, C. (2011). Consideration of trends in time series. Journal of Time Series Econometrics 3(1): Article 2.
- Whittaker, E. (1923). On a new method of graduation. Proceedings of the Edinburgh Mathematical Society 41:63–75.