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). Spline linear regression used for evaluating financial assets. Analele Universitaţii “Constantin Brâncusi” din Târgu Jiu, Seria Economie, Nr. 4/2010 , pp. 310-321. 6. Greiner, A. (2009). Estimating penalized spline regressions: theory and application to economics. Applied Economics Letters, 16 , pp. 1831-1835. 7. Haupt, H., Kagerer, K., Steiner, W. (2014). Smooth quantile bases modeling of brand sales, price and promotional effects from retail scanner panels. Journal of Applied Econometrics, DOI 10.1016/j.jeconom.2014.06.003. 8. Liu, R., Yang, L. (2010). Spline

References Aggrey S.E. (2002). Comparison of three nonlinear and spline regression models for describing chicken growth curves. Poultry Sci., 81: 1782-1788. AOAC (2005). Official Methods of Analysis of AOAC International, 16th Edition. Association of Analytical Chemists, Arlington, VA, USA . Darmani Kuhi H., Kebreab E., Lopez S., France J. (2003). Acomparative evaluation of functions for the analysis of growth in male broilers. J. Agric. Sci. Camb., 140: 451-459. Eleroğlu H., Yıldırım A., Şekeroğlu A., Çoksöyler F.N., Duman M. (2014). Comparison of growth curves

, vol. 9, no. 1, pp. 65-76. S irmans G. S., M ac D onald L., M acpherson D. A. et al ., 2006, The Value of Housing Characteristics: A Meta Analysis , The Journal of Real Estate Finance and Economics, vol. 33, no. 3, pp. 215-240. S peyrer J. F., R agas W. R., 1991, Housing prices and flood risk: An examination using spline regression , The Journal of Real Estate Finance and Economics, vol. 4, no. 4, pp. 395-407. S tendebakken M. O. G., G rytli E. R., O lsson , N. O. E., 2015, Proposed aspects for evaluation of the value of spaces in historic buildings

. (2009). Reinforcement learning and adaptive dynamic programming for feedback control, IEEE Circuits and Systems Magazine 9 (3): 32–50. Lewis, F., Vrabie, D. and Syrmos, V. (2012). Optimal Control , 3rd Edn, John Wiley & Sons, Hoboken, NJ. Liu, D., Wei, Q., Wang, D., Yang, X. and Li, H. (2017). Adaptive Dynamic Programming with Applications in Optimal Control , Springer, Berlin. Manne, A.S. (1960). Linear programming and sequential decisions, Management Science 6 (3): 259–267. Marsh, L.C. and Cormier, D.R. (2001). Spline Regression Models , Number 137, Sage

, L. A. Zadeh and J. Żurada (Eds.), Artificial Intelligence and Soft Computing—ICAISC 2008 , Lecture Notes in Artificial Intelligence, Vol. 5097, Springer, Berlin, Heidelberg, pp. 597-608. Krzyżak, A., Kohler M., and Schäfer D. (2000). Application of structural risk minimization to multivariate smoothing spline regression estimates, Bernoulli   8 (4): 475-489. Ng, A. (2004). Feature selection, l 1 vs. l 2 regularization, and rotational invariance, ACM International Conference on Machine Learning, Banff, Alberta, Canada , Vol. 69, pp. 78-85. Schmidt, J

Versicherungsunternehmen Österreichs Wien Skantz, T. R.; Strickland, T. H. (1987): House Prices and a Flood Event: An Empirical Investigation of Market Efficiency. In: The Journal of Real Estate Research 2, 2, 75-83. Skantz T. R. Strickland T. H. 1987 House Prices and a Flood Event: An Empirical Investigation of Market Efficiency The Journal of Real Estate Research 2 2 75 – 83 Speyrer, J. F.; Ragas, W. R. (1991): Housing prices and flood risk: An examination using spline regression. In: The Journal of Real Estate Finance and Economics 4, 4, 395-407. 10.1007/BF00 219506 Speyrer J. F

regression by using linear spline regression and a likelihood-based model to explain nonlinearity in the relationship between tree rings and precipitation. One of the most accurate and tested process-based models, the Vaganov-Shaskin model ( VS model, Shishov et al ., 2016 ; Vaganov et al ., 2011 ), uses a piecewise linear function to model the relationship between daily environmental data and tree-rings. A similar idea is implemented in a simplified version of the VS model – the VS-Lite model ( Tolwinski-Ward et al ., 2011 ). The advantages of using nonlinear models