Abstract
State Dependent Parameter (SDP) modelling has been developed by Professor Peter Young in the 1990s to identify non-linearities in the context of dynamic transfer function models. SDP is a very efficient approach and it is based on recursive filtering and Fixed Interval Smoothing (FIS) algorithms. It has been applied successfully in many applications, especially to identify Data-Based Mechanistic models from observed time series data in environmental sciences. In this paper we highlight the role played by the SDP ideas, namely in the simplified State-Dependent Regression (SDR) form, in the context of sensitivity analysis and meta-modelling. Fruitful joint co-operation with Peter Young has led to a series of papers, where SDR has been applied to perform sensitivity analysis, to reduce model’s complexity and to build meta-models (or emulators) capable to reproduce the main features of large simulation models. Finally, we will describe how SDR algorithms can be effectively used in the context of the identification and estimation of tensor product smoothing splines ANOVA models, improving their performances.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
DACE is a Matlab toolbox used to construct a kriging approximation models on the basis of data coming from computer experiments (see [3]).
References
Borgonovo, E.: Measuring uncertainty importance: investigation and comparison of alternative approaches. Risk Anal. 26, 1349–1361 (2006)
Borgonovo, E.: A new uncertainty importance measure. Reliab. Eng. Syst. Saf. 92, 771–784 (2007)
Lophaven, S., Nielsen, H., Sondergaard, J.: DACE a Matlab kriging toolbox, version 2.0. Technical Report IMM-TR-2002-12, Informatics and Mathematical Modelling, Technical University of Denmark (2002). http://www.immm.dtu.dk/~hbn/dace
Gu, C.: Smoothing Spline ANOVA Models. Springer, Berlin (2002)
Kalman, R.: A new approach to linear filtering and prediction problems. J. Basic Eng. D 82, 35–45 (1960)
Lin, Y., Zhang, H.: Component selection and smoothing in smoothing spline analysis of variance models. Ann. Stat. 34, 2272–2297 (2006)
Ng, C., Young, P.C.: Recursive estimation and forecasting of non-stationary time series. J. Forecast. 9, 173–204 (1990)
Priestley, M.B.: Nonlinear and Nonstationary Time Series Analysis. Academic Press, New York (1988)
Ratto, M., Pagano, A., Young, P.C.: Non-parametric estimation of conditional moments for sensitivity analysis. Reliab. Eng. Syst. Saf. 94, 237–243 (2009)
Ratto, M., Pagano, A.: Using recursive algorithms for the efficient identification of smoothing spline ANOVA models. AStA Adv. Stat. Anal. 94(4), 367–388 (2010)
Ratto, M., Pagano, A., Young, P.C.: State dependent parameter metamodelling and sensitivity analysis. Comput. Phys. Commun. 177, 863–876 (2007)
Sadeghi, J., Tych, W., Chotai, A., Young, P.C.: Multi-state dependent parameter model identification and estimation for nonlinear dynamic systems. Electron. Lett. 46(18), 1265–1266 (2011)
Saltelli, A., Chan, K., Scott, M. (eds.): Sensitivity Analysis. Wiley, New York (2000)
Storlie, C., Bondell, H., Reich, B., Zhang, H.: Surface estimation, variable selection, and the nonparametric oracle property. Stat. Sin. 21(2), 679–705 (2011)
Schweppe, F.: Evaluation of likelihood functions for Gaussian signals. IEEE Trans. Inf. Theory 11, 61–70 (1965)
Tibshirani, R.: Regression shrinkage and selection via the LASSO. J. R. Stat. Soc. B 58(1), 267–288 (1996)
Wahba, G.: Spline Models for Observational Data. CBMS-NSF Regional Conference Series in Applied Mathematics (1990)
Wecker, W.E., Ansley, C.F.: The signal extraction approach to non linear regression and spline smoothing. J. Am. Stat. Assoc. 78, 81–89 (1983)
Weinert, H., Byrd, R., Sidhu, G.: A stochastic framework for recursive computation of spline functions: Part II, smoothing splines. J. Optim. Theory Appl. 30, 255–268 (1983)
Young, P.C.: Time variable and state dependent modelling of nonstationary and nonlinear time series. In: Rao, T.S. (ed.) Developments in Time Series Analysis, pp. 374–413. Chapman and Hall, London (1993)
Young, P.C.: Data-based mechanistic modeling of environmental, ecological, economic and engineering systems. Environ. Model. Softw. 13, 105–122 (1998)
Young, P.C.: Nonstationary time series analysis and forecasting. Progr. Environ. Sci. 1, 3–48 (1999)
Young, P.C.: Stochastic, dynamic modelling and signal processing: Time variable and state dependent parameter estimation. In: Fitzgerald, W.J., Smith, R.L., Walden, A.T., Young, P.C. (eds.) Nonlinear and Nonstationary Signal Processing, pp. 74–114. Cambridge University Press, Cambridge (2000)
Young, P.C.: The identification and estimation of nonlinear stochastic systems. In: Mees, F.A.I. (ed.) Nonlinear Dynamics and Statistics. Birkhäuser, Boston (2001)
Young, P.C.: Data-based mechanistic modelling: natural philosophy revisited? (in this book)
Young, P.C., McKenna, P., Bruun, J.: The identification and estimation of nonlinear stochastic systems. Int. J. Control 74, 1837–1857 (2001)
Young, P.C., Pedregal, D.J.: Recursive fixed interval smoothing and the evaluation of Lidar measurements. Environmetrics 7, 417–427 (1996)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag London Limited
About this chapter
Cite this chapter
Ratto, M., Pagano, A. (2012). State Dependent Regressions: From Sensitivity Analysis to Meta-modeling. In: Wang, L., Garnier, H. (eds) System Identification, Environmental Modelling, and Control System Design. Springer, London. https://doi.org/10.1007/978-0-85729-974-1_9
Download citation
DOI: https://doi.org/10.1007/978-0-85729-974-1_9
Publisher Name: Springer, London
Print ISBN: 978-0-85729-973-4
Online ISBN: 978-0-85729-974-1
eBook Packages: EngineeringEngineering (R0)