multivariate adaptive regression splines (MARS model)
Estimation of scour downstream of a ski-jump bucket using the multivariate adaptive regression splines
... as Multivariate Adaptive Regression Splines (MARS), have been proposed for modeling the complex systems based on the input and output data ...
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Audio based Classification of Video Genre Using Multivariate Adaptive Regression Splines
... In this paper, Multivariate Adaptive Regression Splines is presented for automatic video genre classification using only audio features. The experiments have indicated that it produce the ...
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Kriner, Monika (2007): Survival Analysis with Multivariate adaptive Regression Splines. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... the multivariate adaptive regression splines ...linear splines with alternating slope enable to model lin- ear as well as nonlinear influences of the covariates and particularly ...
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Multivariate adaptive regression splines models for vehicular emission prediction
... The Multivariate Adaptive Regression Splines (MARS) were introduced for fitting the relationship between a set of predictors and dependent variables (Friedman ...a multivariate, ...
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Survival Analysis with Multivariate Adaptive Regression Splines using Cox-Snell Residual
... Multivariate adaptive regression splines (MARS) are a useful tool to identify linear and nonlinear effects and interactions between two or more covariates in classical regression models ...
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Non parametric Estimation of GARCH (2, 2) Volatility model: A new Algorithm
... of multivariate adaptive regression splines(MARS) model estimation algorithm proposed by Chung (2012) and an algorithm proposed by Buhlman and McNeil(200), develops an algorithm for ...
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Development of predictive models for shear strength of HSC slender beams without web reinforcement using machine-learning based techniques
... linear regression, advanced machine learning methods of Multivariate Adaptive Regression Splines (MARS), and Group Method of Data Handling (GMDH) ...robust regression models, ...
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Data mining and statistical approaches in debris flow susceptibility modelling using airborne LiDAR data
... Abstract: Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the ...
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Data Mining Techniques for Forecasting the Medical Resource Consumption of Patients with Diabetic Nephropathy Lee et al.
... multiple regression, stepwise regression, multivariate adaptive regression splines (MARS), support vector regression, and two- stage model ...stepwise regression ...
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Variable selection methods for developing a biomarker panel for prediction of dengue hemorrhagic fever
... by Multivariate Adaptive Regression Splines, Learning Ensemble, Random Forest, Bayesian Moving Averaging, Stochastic Search Variable Selection, and Generalized Regularized Logistics ...
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Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds
... non-parametric regression in recent ...multiple regression and machine learning approaches (including generalized ad- ditive models, multivariate adaptive regression splines, ...
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Comparison of Machine Learning Regression Methods to Simulate NO3 Flux in Soil Solution under Potato Crops
... tion dynamics in suction lysimeters in potato plots receiving different N application rates. Four machine learning function approximation methods were compared: multiple linear regressions, multivariate ...
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Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model
... Introduced by Freidman (1991), MARS has previously been applied hydrology (Abraham and Steinberg, 2001; Sharda et al., 2008; Cheng and Cao, 2014; Deo et al., 2015; Kisi, 2015; Waseem et al., 2015) but its application for ...
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A genome wide association analysis of Framingham Heart Study longitudinal data using multivariate adaptive splines
... the multivariate adaptive splines for analysis of longitudinal data (MASAL) presented by Zhang [8] to analyze the FHS longitudinal ...nonparametric regression approach that was devel- oped ...
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Automatic, computer aided geometric design of free-knot, regression splines
... spatially adaptive smoothing techniques such as: the wavelet shrinkage methods of Donoho and Johnstone (1994, 1995), the variable bandwidth kernel method of Fan and Gijbels (1995), hybrid adaptive ...
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Geometrically designed, variable knot regression splines
... hybrid adaptive splines (HAS) of Luo and Wahba (1997) and the penalized splines, considered by Eubank (1988), Wahba (1990), Marx and Eilers (1996), Mammen and van der Geer (1997), Rupert and Carroll ...
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Geometrically Designed, Variable Knot Regression Splines: Asymptotics and Inference
... with splines of a fixed degree is a linear optimization problem, if the number of knots and their location are ...free-knot regression splines have been ...developed adaptive knot selection ...
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Geometrically designed, variable know regression splines: asymptotics and inference
... with splines of a fixed degree is a linear optimization problem, if the number of knots and their location are ...free-knot regression splines have been ...developed adaptive knot selection ...
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Adaptive multivariate global testing
... a multivariate test, lying in a pos- sibly high-dimensional design space, can be hard to illustrate and ...of multivariate tests is typically restricted to a limited part of the design ...
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A Cluster Elastic Net for Multivariate Regression
... high-dimensional regression: cluster repre- sentative lasso (CRL) and cluster group lasso ...in multivariate regression that leverages correlated traits based on a network ...
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