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Simple and multiple P-splines regression with shape constraintsBOLLAERTS, Kaatje; EILERS, Paul H. C; VAN MECHELEN, Iven et al.British journal of mathematical & statistical psychology. 2006, Vol 59, pp 451-469, issn 0007-1102, 19 p., 2Article

Application of multivariate adaptive regression splines (MARS) to simulate soil temperatureYANG, C.-C; PRASHER, S. O; LACROIX, R et al.Transactions of the ASAE. 2004, Vol 47, Num 3, pp 881-887, issn 0001-2351, 7 p.Article

Predictability of price of tea from sensory assessments and biochemical information using data-mining techniquesPAUL, Sanjoy K.Journal of the science of food and agriculture. 2008, Vol 88, Num 8, pp 1354-1362, issn 0022-5142, 9 p.Article

On the optimal amount of smoothing in penalised spline regressionWAND, M. P.Biometrika. 1999, Vol 86, Num 4, pp 936-940, issn 0006-3444Article

Improved smoothing spline regression by combining estimates of different smoothnessLEE, Thomas C. M.Statistics & probability letters. 2004, Vol 67, Num 2, pp 133-140, issn 0167-7152, 8 p.Article

Exploring the relationship between information technology investments and firm performance using regression splines analysisOSEI-BRYSON, Kweku-Muata; MYUNG KO.Information & management. 2004, Vol 42, Num 1, pp 1-13, issn 0378-7206, 13 p.Article

Smooth quantile ratio estimationDOMINICI, Francesca; COPE, Leslie; NAIMAN, Daniel Q et al.Biometrika. 2005, Vol 92, Num 3, pp 543-557, issn 0006-3444, 15 p.Article

Using multivariate adaptive regression splines (MARS) to develop crash modification factors for urban freeway interchange influence areasHALEEM, Kirolos; GAN, Albert; JINYAN LU et al.Accident analysis and prevention. 2013, Vol 55, pp 12-21, issn 0001-4575, 10 p.Article

Evaluating the water sector in Italy through a two stage method using the conditional robust nonparametric frontier and multivariate adaptive regression splinesVIDOLI, Francesco.European journal of operational research. 2011, Vol 212, Num 3, pp 583-595, issn 0377-2217, 13 p.Article

Multivariate adaptive regression splines (MARS) in chromatographic quantitative structure-retention relationship studiesPUT, R; XU, Q. S; MASSART, D. L et al.Journal of chromatography. 2004, Vol 1055, Num 1-2, pp 11-19, issn 0021-9673, 9 p.Article

Design of Railway Wheel Detector Insusceptible to Electromagnetic NoiseOTAKE, Asuka; TAKAYASU, Kenta; WAKAO, Shinji et al.IEEE transactions on magnetics. 2010, Vol 46, Num 8, pp 2731-2734, issn 0018-9464, 4 p.Conference Paper

Application of multivariate Adaptive Regression Splines (MARS) to the preference mapping of cheese sticksXIONG, R; MEULLENET, J.-F.Journal of food science. 2004, Vol 69, Num 4, pp SNQ131-SNQ139, issn 0022-1147Article

Parametric and semi-parametric approaches in the analysis of short-term effects of air pollution on healthBACCINI, Michela; BIGGERI, Annibale; LAGAZIO, Corrado et al.Computational statistics & data analysis. 2007, Vol 51, Num 9, pp 4324-4336, issn 0167-9473, 13 p.Article

Combining non-parametric models with logistic regression : an application to motor vehicle injury dataKUHNERT, Petra M; DO, Kim-Anh; MCCLURE, Rod et al.Computational statistics & data analysis. 2000, Vol 34, Num 3, pp 371-386, issn 0167-9473Article

Penalized spline models for functional principal component analysisFANG YAO; LEE, Thomas C. M.Journal of the Royal Statistical Society. Series B, statistical methodology. 2006, Vol 68, pp 3-25, issn 1369-7412, 23 p., 1Article

A Comparison of Two-Group Classification MethodsHOLDEN, Jocelyn E; HOLMES FINCH, W; KELLEY, Ken et al.Educational and psychological measurement. 2011, Vol 71, Num 5, pp 870-901, issn 0013-1644, 32 p.Article

Efficient estimation of three-dimensional curves and their derivatives by free-knot regression splines, applied to the analysis of inner carotid artery centrelinesSANGALLI, Laura M; SECCHI, Piercesare; VANTINI, Simone et al.Applied statistics. 2009, Vol 58, Num 3, pp 285-306, issn 0035-9254, 22 p.Article

Mining the customer credit using classification and regression tree and multivariate adaptive regression splinesLEE, Tian-Shyug; CHIU, Chih-Chou; CHOU, Yu-Chao et al.Computational statistics & data analysis. 2006, Vol 50, Num 4, pp 1113-1130, issn 0167-9473, 18 p.Article

A Mixed Model for Investigating a Population of Asymptotic Growth Curves Using Restricted B-SplinesJONES, Geoffrey; LEUNG, Joyce; ROBERTSON, Hugh et al.Journal of agricultural, biological, and environmental statistics. 2009, Vol 14, Num 1, pp 66-78, issn 1085-7117, 13 p.Article

Varying-coefficient functional linear regression modelsCardot, Hervé; Sarda, Pascal.Publications du Laboratoire de statistique et probabilités. 2005, issn 0248-3289, 1Vol, 18 p.Report

From finite sample to asymptotics: A geometric bridge for selection criteria in spline regressionKOU, S. C.Annals of statistics. 2004, Vol 32, Num 6, pp 2444-2468, issn 0090-5364, 25 p.Article

Modeling vector nonlinear time series using POLYMARSDE GOOIJER, Jan G; RAY, Bonnie K.Computational statistics & data analysis. 2003, Vol 42, Num 1-2, pp 73-90, issn 0167-9473, 18 p.Article

Bounded optimal knots for regression splinesMOLINARI, Nicolas; DURAND, Jean-Francois; SABATIER, Robert et al.Computational statistics & data analysis. 2004, Vol 45, Num 2, pp 159-178, issn 0167-9473, 20 p.Article

Nonparametric regression in the presence of measurement errorCARROLL, R. J; MACA, J. D; RUPPERT, D et al.Biometrika. 1999, Vol 86, Num 3, pp 541-554, issn 0006-3444Article

Estimation de l'évolution d'un total en présence d'information auxiliaire : une approche par splines de régression = Finite population total evolution estimation using auxiliary information: a regression spline approachGOGA, Camelia.Comptes rendus. Mathématique. 2004, Vol 339, Num 6, pp 441-444, issn 1631-073X, 4 p.Article

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