Results 31 to 40 of about 1,574 (90)
Bandwidth Selection Problem in Nonparametric Functional Regression [PDF]
Statistika: Statistics and Economy Journal, 2017 The focus of this paper is the nonparametric regression where the predictor is a functional random variable, and the response is a scalar. Functional kernel regression belongs to popular nonparametric methods used for this purpose. The two key problems
Daniela Kuruczová, Jan Koláček
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Multi-Task Nonparametric Regression Under Joint Sparsity
IEEE AccessThis study investigates a multi-task estimation under joint sparsity. We consider estimating multiple functions when functions of interest share common sparsity patterns.
Jae-Hwan Jhong +2 more
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Latent Variable Nonparametric Cointegrating Regression [PDF]
SSRN Electronic Journal, 2017 This article studies the asymptotic properties of empirical nonparametric regressions that partially misspecify the relationships between nonstationary variables. In particular, we analyze nonparametric kernel regressions in which a potential nonlinear cointegrating regression is misspecified through the use of a proxy regressor in place of the true ...
Wang, Qiying +2 more
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On concurvity in nonlinear and nonparametric regression models
Statistica, 2014 When data are affected by multicollinearity in the linear regression framework, then concurvity will be present in fitting a generalized additive model (GAM). The term concurvity describes nonlinear dependencies among the predictor variables.
Sonia Amodio +2 more
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Comparison of Nonparametric Path Analysis and Biresponse Regression using Truncated Spline Approach
JTAM (Jurnal Teori dan Aplikasi Matematika)Nonparametric path analysis and biresponse nonparametric regression are two flexible statistical approaches to analyze the relationship between variables without assuming a certain form of relationship.
Laila Nur Azizah +4 more
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Media Statistika, 2022 The response variable of the regression analysis has a linear relationship with one of the variable predictors, however the unknown relationship pattern with the other predictor variables.
Hesikumalasari Hesikumalasari +3 more
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Physics-aware nonparametric regression models for Earth data analysis
Environmental Research Letters, 2022 Process understanding and modeling is at the core of scientific reasoning. Principled parametric and mechanistic modeling dominated science and engineering until the recent emergence of machine learning (ML).
Jordi Cortés-Andrés +9 more
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M Robustified Additive Nonparametric Regression [PDF]
, 2002 Additive modelling has been widely used in nonparametric regression to circumvent the curse of dimensionality, by reducing the problem of estimating a multivariate regression function to the estimation of its univariate components. Estimation of these univariate functions, however, can suffer inaccuracy if the data set is contaminated with extreme ...
Tamine, Julien +2 more
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Nonparametric expectile shortfall regression for functional data
Demonstratio MathematicaThis work addresses the issue of financial risk analysis by introducing a novel expected shortfall (ES) regression model, which employs expectile regression to define the shortfall threshold in financial risk management.
Almanjahie Ibrahim M. +4 more
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