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Showing 1 to 20 of 22 for “"Regression splines"”.
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Investigation of Emission Characteristics during Low Temperature Combustion using Multivariate Adaptive Regression Splines
… inputs that are neither measured nor controlled, regression analysis must be performed via a flexible procedure. The methodology that will be used in this sense is called Multivariate Adaptive Regression Splines (MARS), which allows to approximate functions of many input variables given the value …
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Accounting for spatial confounding in large scale epidemiological studies
… of existing and new methods that use thin plate regression splines to mitigate spatial confounding bias for both cross-sectional and longitudinal analyses. I also introduce a metric to quantify the spatial smoothing induced by thin plate regression splines in varying geographic domains. I first …
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Application of Regression Spline in Multilevel Longitudinal Modeling
… mixed-effects models for longitudinal data, and regression splines are first reviewed. Subsequently, a methodology that applies regression spline in multilevel longitudinal modeling is proposed to deal with the longitudinal data with large numbers of time points. The visual-search data are used …
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Selecting the best model for predicting a term deposit product take-up in banking
… models: random forest, multivariate adaptive regression splines, neural network and support vector machine on the datasets and we compare the models against each other for their ability to identify customers that are likely to take-up a term savings product. As part of the model building …
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Multiview active shape models with SIFT descriptors
… of SIFT descriptors [68]. Multivariate Adaptive Regression Splines (MARS [40]) are used to efficiently match descriptors around landmarks. This modified ASM is fast and performs well on frontal faces. The model is then extended to also handle non-frontal faces. This is done by first estimating …
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Interpolants, Error Bounds, and Mathematical Software for Modeling and Predicting Variability in Computer Systems
… methods including multivariate adaptive regression splines, support vector regressors, multilayer perceptrons, Shepard variants, and the Delaunay mesh are investigated in the context of computer variability modeling. New methods of approximation using Box splines, Voronoi cells, and …
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The struggle is real : a quantitative study of student timeliness to complete assignments in online courses.
… science course using Pearson's r and linear regression splines. Unlike other research on grades and task delays based on students' biased self-evaluations, this study collected the submission times and grades for weekly assignments directly from the learning management system in a traditional …
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Building a Nonparametric Model After Dimension Reduction
To effectively build a regression model with a large number of covariates is no easy task. We consider using dimension reduction before building a parametric or spline model. The dimension reduction procedure is based on a canonical correlation analysis on the predictor variables and a spline basis …
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Análise de métodos não lineares para previsão de vazões médias mensais
… comparar os modelos MARS (Multivariate Adaptive Regression splines), os métodos de interpolação Ótima e por Multiquadrícas para prever vazões afluentes médias mensais para o horizonte de 1 a 12 meses, e analisar a influência do período de calibração na qualidade das previsões de afluências médias …
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Using Statistical Techniques and Machine Learning to Assess Potential Biomarkers and Clinical Characteristics for Differences between Chronic Hepatitis C Virus and Hepatocellular Carcinoma
… were constructed using Multivariable Adaptive Regression Splines (MARS) and Logistic Regression (LR) to further reduce the number of variables. Ideally, only biomarkers capable of predicting HCC are left. While each model produced sub-optimal performance statistics for a direct clinical test, …
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Predictive model to estimate ionized calcium from routine serum biochemical profiles in dogs
… for the creation of a multivariate adaptive regression splines (MARS) model, with the final model being determined by backward elimination. Accuract and diagnostic performance of piCal and its prediction interval (PI) were tested on 519 dogs via Bland-Altman analysis, Pearson’s R, and …
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A Comprehensive Study of Buoyant Rosette Jets Using Laboratory Experiments, CFD, and Machine Learning
… system (ANFIS), and multivariate adaptive regression splines (MARS)—are trained on 870 data points from 34 different Fr number cases obtained experimentally. Input features included Fr number, x/D, and y/D, while the target variable was normalized concentration. ANFIS outperforms the other …
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Imputación de datos faltantes en redes de distribución de baja tensión: Aplicación a edificios de pública concurrencia
… Distance Weighting (IDW), Multiple Linear Regressions Models (MLR Models) y Multiple Imputation by Chained Equations (MICE). En esta primera fase, no solo se demuestra la viabilidad del uso de las citadas técnicas como herramientas capaces de mejorar los sistemas de gestión y monitorización …
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Structural performance evaluation of concrete arch dams using ambient vibration monitoring and GNNS systems
… algorithm known as Gaussian process regression models were developed to predict natural frequencies. In Gaussian process regression, the choice of a covariance function is very important in producing good results. The ability of the different covariance functions in Gaussian process …
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Study of Pelvic Growth and Development Through Examination of Interlandmark Distances and Geometric Morphometric Analyses: Implications for Subadult Skeletal Sex and Age Estimation
… used in sex-specific and pooled-sex nonlinear regression models, including Loess regression and Multivariate Adaptive Regression Splines (MARS). Geometric morphometric analyses were performed to understand shape variation through ontogeny. The unilateral ILDs were tested for the utility of …
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Martial eagles and the national power grid in South Africa: the implications of pylon-nesting for conservation management
… Using a logistic generalised linear model with regression splines for distance to nearest other nest, I found that Martial Eagles strongly avoid proximity to conspecific nests (mean distance to conspecific nest = 28.2 km, range 2.5 - 167.1 km, n = 306). This result shows that minimum spacing …
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Probabilistic renewable energy modelling in South Africa
… issue involves the construction of quantile regression models for forecasting extreme peak electricity demand and determining the optimal number of units to commit at minimal costs for each period using the forecasts obtained from the developed models. The bounded variable mixed-integer …
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Epidemiology of diabetes and related mortality: early screening, socioecological determinants, and the value of prevention
… to build this risk model. A non-parametric regression method, Multivariate Adaptive Regression Splines (MARS), is used to allow for interactions and non-linearity in the model. A risk prediction model using the MARS approach achieved a performance level of 87% accuracy with area under …
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A comparison of some methods of modeling baseline hazard function in discrete survival models
… the accuracy of three variants of penalised regression spline methods in smoothing the baseline hazard function. Root mean squared error (RMSE) analysis suggests that generally all the smoothing methods performed better than the model with a discrete baseline hazard function. No single …
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Semiparametric Varying Coefficient Models for Matched Case-Crossover Studies
… predictors included in the conditional logistic regression model. Any stratum effect is removed by the conditioning on the fixed number of sets of the case and controls in the stratum. However, some matching covariates such as time, and/or spatial location often play an important role as an …
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