Nonparametric Regression with Correlated Errors

By Jean D. Opsomer, Yuedong Wang, Yuhong Yang. 2001

321 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/942471025e9ae8ce3820cea2181244aeaa722d7d

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Citations in the corpus

CONSISTENCY OF CROSS VALIDATION FOR COMPARING REGRESSION PROCEDURES (2007, methodology): “See Opsomer, Wang and Yang [17] for a review and references related to the use of CV for bandwidth selection for nonparametric regression with dependent errors.”
Smoothing parameter selection methods for nonparametric regression with spatially correlated errors (2005, methodology): “Unfortunately, most smoothing parameter selection methods do not perform well in the presence of correlated errors, as research in the one-dimensional case has shown (see Hart (1996) and Opsomer et al. (2001) for overviews).”
The GDP-Temperature relationship: Implications for climate change damages (2021, unknown): “Using multiple splits avoids the randomness inherent in splitting the data only once (Opsomer et al. 2001).”
A Note on Penalized Spline Smoothing With Correlated Errors (2007, background): “In fact, even the smallest misspecifications of the correlation structure can result in serious overfitting or underfitting, as demonstrated by Opsomer et al. (2001).”
A note on the validity of cross-validation for evaluating autoregressive time series prediction (2018, methodology): “For example, Opsomer et al. (2001) show that standard CV underestimates bandwidths in a kernel estimator regression framework if autocorrelation of the error is high, so that the method overfits the data.”
Smoothing Splines: Methods and Applications (2011, unknown): “Setting uk = θkΣkc as in the Gaussian case (Opsomer et al. 2001) and noting that ZDZ = Σθ , we have u = DZc and u {Cov(u)}+u = nλcZDDDZc/σ(2) = nλcZDZc/σ(2) = nλc Σθc/σ (2).”
A Hierarchy of Limitations in Machine Learning (2020, background): “And as Opsomer et al. (2001) point out, without assuming a parametric form for either the mean or covariance functions, models are not identifiable.”
Optimal estimation of the mean function based on discretely sampled functional data: Phase transition (2011, background): “Interested readers are referred to Opsomer, Wang and Yang (2001) for a recent survey of existing results.”
More Efficient Local Polynomial Estimation in Nonparametric Regression With Autocorrelated Errors (2003, methodology): “¢/ is nonparametric, which has been analyzed by, for example, Robinson (1983) and Masry (1996a,b).”
Using difference‐based methods for inference in nonparametric regression with time series errors (2003, methodology): “Methodology for nonparametric regression with correlated errors, including different approaches to the choice of bandwidth, has been surveyed and discussed by Opsomer et al. (2001).”
A survey of cross-validation procedures for model selection (2009, unknown): “Therefore, when data ξ 1 , . . . , ξ n are not independent, CV must be modi-ﬁed, like other model selection procedures (in non-parametric regression with dependent data, see the review by Opsomer et al., 2001).”
The Hodrick-Prescott Filter A Special Case of Penalized Spline Smoothing (2010, methodology): “Ruppert et al. (2003) report that the number and location of the knots is in general not as critical a choice.”
Comparing parameter choice methods for regularization of ill-posed problems (2011, methodology): “Several extensions of GCV have been proposed to deal with correlated data in certain smoothing problems, where the correlation is known or parametrically specified, and good results have been obtained for large sample sizes [112, 144].”
Nonparametric Spatial Prediction (2004, background): “We also refer the reader to [13] whose comparative study and results would provide valuable information on the choice of the smoothing parameter.”
Cross-Validation for Correlated Data (2019, background): “(14) was first suggested by (Altman 1990) in context of time-series (Altman 1990) and later by (Opsomer et al. 2001) in context of spatial data analysis.”
Semiparametric Nonlinear Mixed-Effects Models and Their Applications (2001, methodology): “This connection holds for general smoothing spline and SS ANOVA models (Wang 1998b; Opsomer et al. 2001).”
Inference in Semiparametric Dynamic Models for Binary Longitudinal Data (2006, background): “This issue has been studied by Diggle and Hutchinson (1989), Altman (1990), Smith, Wong, and Kohn (1998), Wang (1998), and Opsomer, Wang, and Yang (2001) .”