Results 11 to 20 of about 1,907,107 (269)
Rates of convergence for regression with the graph poly-Laplacian
Sampling Theory, Signal Processing, and Data Analysis, 2023 AbstractIn the (special) smoothing spline problem one considers a variational problem with a quadratic data fidelity penalty and Laplacian regularization. Higher order regularity can be obtained via replacing the Laplacian regulariser with a poly-Laplacian regulariser.
Nicolás García Trillos +2 more
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Local Logit Regression for Recovery Rate [PDF]
SSRN Electronic Journal, 2017 We propose a flexible and robust nonparametric local logit regression for ...
Sopitpongstorn, Nithi +2 more
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Learning rates for kernel-based expectile regression [PDF]
Machine Learning, 2018 Conditional expectiles are becoming an increasingly important tool in finance as well as in other areas of applications. We analyse a support vector machine type approach for estimating conditional expectiles and establish learning rates that are minimax optimal modulo a logarithmic factor if Gaussian RBF kernels are used and the desired expectile is ...
Muhammad Farooq, Ingo Steinwart
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Minimax Rate of Testing in Sparse Linear Regression [PDF]
Automation and Remote Control, 2019 We consider the problem of testing the hypothesis that the parameter of linear regression model is 0 against an s-sparse alternative separated from 0 in the l2-distance. We show that, in Gaussian linear regression model with p < n, where p is the dimension of the parameter and n is the sample size, the non-asymptotic minimax rate of testing has the ...
Carpentier, Alexandra +4 more
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Learning Rates of Least-Square Regularized Regression [PDF]
Foundations of Computational Mathematics, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Qiang Wu 0003 +2 more
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Optimal Rates of Distributed Regression with Imperfect Kernels
J. Mach. Learn. Res., 2020 Distributed machine learning systems have been receiving increasing attentions for their efficiency to process large scale data. Many distributed frameworks have been proposed for different machine learning tasks. In this paper, we study the distributed kernel regression via the divide and conquer approach.
Hongwei Sun, Qiang Wu 0003
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Optimal Rate of Kernel Regression in Large Dimensions
CoRR, 2023 We perform a study on kernel regression for large-dimensional data (where the sample size $n$ is polynomially depending on the dimension $d$ of the samples, i.e., $n\asymp d^γ$ for some $γ>0$ ). We first build a general tool to characterize the upper bound and the minimax lower bound of kernel regression for large dimensional data through the ...
Weihao Lu 0002 +4 more
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A Study of Count Regression Models for Mortality Rate
Cauchy: Jurnal Matematika Murni dan Aplikasi, 2021 This paper discusses how overdispersed count data to be fit. Poisson regression model, Negative Binomial 1 regression model (NEGBIN 1) and Negative Binomial regression 2 (NEGBIN 2) model were proposed to fit mortality rate data.
Anwar Fitrianto
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International Journal of Turbomachinery, Propulsion and Power, 2020 Injection pattern of the oxidizer injected into the combustion chamber is a significant factor in evaluating the performance of a hybrid rocket. In the hybrid rocket combustion process, oxidizer flows over the solid fuel grain surface, leading to a ...
Pragya Berwal, Shelly Biswas
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MODELING CLUSTERWISE LINEAR REGRESSION ON POVERTY RATE IN INDONESIA
Barekeng, 2023 When a person's income is so low that it cannot cover even the most basic living expenses, they are said to be poor. Data on poverty levels and hypothesized causes are used in this study. If the data pattern forms clusters, one of the regression analyses
Eni Meylisah +4 more
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