Nonparametric regression ≈ Nonparametric regression
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Nonparametric regression using deep neural networks with ReLU activation function Open
Consider the multivariate nonparametric regression model. It is shown that estimators based on sparsely connected deep neural networks with ReLU activation function and properly chosen network architecture achieve the minimax rates of conv…
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Machine learning methods for empirical streamflow simulation: a comparison of model accuracy, interpretability, and uncertainty in seasonal watersheds Open
In the past decade, machine learning methods for empirical rainfall–runoff modeling have seen extensive development and been proposed as a useful complement to physical hydrologic models, particularly in basins where data to support proces…
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Rate-optimal graphon estimation Open
Network analysis is becoming one of the most active research areas in statistics. Significant advances have been made recently on developing theories, methodologies and algorithms for analyzing networks. However, there has been little fund…
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Regression Model-Based Short-Term Load Forecasting for University Campus Load Open
Load forecasting is a critical aspect for power systems planning, operation and control. In this paper, as part of research efforts of an ambitious project at Memorial University of Newfoundland in St. John’s, Canada, to achieve more energ…
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The fused Kolmogorov filter: A nonparametric model-free screening method Open
A new model-free screening method called the fused Kolmogorov filter is proposed for high-dimensional data analysis. This new method is fully nonparametric and can work with many types of covariates and response variables, including contin…
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Nonparametric modal regression Open
Modal regression estimates the local modes of the distribution of $Y$ given $X=x$, instead of the mean, as in the usual regression sense, and can hence reveal important structure missed by usual regression methods. We study a simple nonpar…
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On the rate of convergence of fully connected deep neural network regression estimates Open
Recent results in nonparametric regression show that deep learning, that is, neural network estimates with many hidden layers, are able to circumvent the so-called curse of dimensionality in case that suitable restrictions on the structure…
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Bootstrap of Kernel Smoothing in Nonlinear Time Series Open
Kernel smoothing in nonparametric autoregressive schemes offers a powerful tool in modelling time series. In this paper it is shown that the bootstrap can be used for estimating the distribution of kernel smoothers. This can be
\ndone by m…
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The Highly Adaptive Lasso Estimator Open
Estimation of a regression functions is a common goal of statistical learning. We propose a novel nonparametric regression estimator that, in contrast to many existing methods, does not rely on local smoothness assumptions nor is it constr…
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High-dimensional regression adjustments in randomized experiments Open
Significance As datasets get larger and more complex, there is a growing interest in using machine-learning methods to enhance scientific analysis. In many settings, considerable work is required to make standard machine-learning methods u…
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Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning Open
In this paper, we investigate the impact of compression on stochastic gradient algorithms for machine learning, a technique widely used in distributed and federated learning. We underline differences in terms of convergence rates between s…
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A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions Open
This tutorial introduces the reader to Gaussian process regression as an expressive tool to model, actively explore and exploit unknown functions. Gaussian process regression is a powerful, non-parametric Bayesian approach towards regressi…
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Nonparametric bootstrap inference for the targeted highly adaptive least absolute shrinkage and selection operator (LASSO) estimator Open
The Highly-Adaptive least absolute shrinkage and selection operator (LASSO) Targeted Minimum Loss Estimator (HAL-TMLE) is an efficient plug-in estimator of a pathwise differentiable parameter in a statistical model that at minimal (and pos…
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Nonparametric Shape-Restricted Regression Open
We consider the problem of nonparametric regression under shape constraints. The main examples include isotonic regression (with respect to any partial order), unimodal/convex regression, additive shape-restricted regression and constraine…
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Scalar-on-image regression via the soft-thresholded Gaussian process Open
This work concerns spatial variable selection for scalar-on-image regression. We propose a new class of Bayesian nonparametric models and develop an efficient posterior computational aigorithm. The proposed soft-thresholded Gaussian proces…
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Shape-Restricted Regression Splines with R Package splines2 Open
Splines are important tools for the flexible modeling of curves and surfaces in regression analyses. Functions for constructing spline basis functions are available in R through the base package splines. When the curves to be modeled have …
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<b>qgam</b>: Bayesian Nonparametric Quantile Regression Modeling in <i>R</i> Open
Generalized additive models (GAMs) are flexible non-linear regression models, which can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R package. While the GAM methods provided by mgcv are based on the as…
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A Statistical Learning Approach to Modal Regression Open
This paper studies the nonparametric modal regression problem systematically from a statistical learning view. Originally motivated by pursuing a theoretical understanding of the maximum correntropy criterion based regression (MCCR), our s…
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Lecture notes on ridge regression Open
The linear regression model cannot be fitted to high-dimensional data, as the high-dimensionality brings about empirical non-identifiability. Penalized regression overcomes this non-identifiability by augmentation of the loss function by a…
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Individual Tree Diameter Growth Models of Larch–Spruce–Fir Mixed Forests Based on Machine Learning Algorithms Open
Individual tree growth models are flexible and commonly used to represent growth dynamics for heterogeneous and structurally complex uneven-aged stands. Besides traditional statistical models, the rapid development of nonparametric and non…
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Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods Open
Aboveground biomass (AGB) of shrub communities in the desert is a basic quantitative characteristic of the desert ecosystem and an important index to measure ecosystem productivity and monitor desertification. An accurate and efficient met…
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Optimal sup-norm rates and uniform inference on nonlinear functionals of nonparametric IV regression Open
This paper makes several important contributions to the literature about nonparametric instrumental variables (NPIV) estimation and inference on a structural function h0 and its functionals. First, we derive sup-norm convergence rates for …
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Classification of Non-Parametric Regression Functions in Longitudinal Data Models Open
Summary We investigate a longitudinal data model with non-parametric regression functions that may vary across the observed individuals. In a variety of applications, it is natural to impose a group structure on the regression curves. Spec…
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A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data Open
This paper develops a semi-nonparametric Poisson regression model to analyze motor vehicle crash frequency data collected from rural multilane highway segments in California, US. Motor vehicle crash frequency on rural highway is a topic of…
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Sparse-Input Neural Networks for High-dimensional Nonparametric Regression and Classification Open
Neural networks are usually not the tool of choice for nonparametric high-dimensional problems where the number of input features is much larger than the number of observations. Though neural networks can approximate complex multivariate f…
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Oracle inequalities for sparse additive quantile regression in reproducing kernel Hilbert space Open
This paper considers the estimation of the sparse additive quantile regression (SAQR) in high-dimensional settings. Given the nonsmooth nature of the quantile loss function and the nonparametric complexities of the component function estim…
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Flexible species distribution modelling methods perform well on spatially separated testing data Open
Aim To assess whether flexible species distribution models that perform well at nearby testing locations still perform strongly when evaluated on spatially separated testing data. Location Australian Wet Tropics (AWT), Ontario, Canada (CAN…
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Series estimation under cross-sectional dependence Open
An asymptotic theory is developed for series estimation of nonparametric and semiparametric regression models for cross-sectional data under conditions on disturbances that allow for forms of cross-sectional dependence and heterogeneity, i…
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Robust nonparametric regression: A review Open
Nonparametric regression methods provide an alternative approach to parametric estimation that requires only weak identification assumptions and thus minimizes the risk of model misspecification. In this article, we survey some nonparametr…
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Beyond UCB: Optimal and Efficient Contextual Bandits with Regression\n Oracles Open
A fundamental challenge in contextual bandits is to develop flexible,\ngeneral-purpose algorithms with computational requirements no worse than\nclassical supervised learning tasks such as classification and regression.\nAlgorithms based o…