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球面到球面回归的非参数旋转方法AI 解读

Marco Di Marzioa, Agnese Panzerab & Charles C. Taylorc*a DMQTE, Università di Chieti-Pescara, Pescara, Italyb DiSIA, Università di Firenze, Firenze, Italyc Department of Statistics, University of Leeds, Leeds, UKCONTACT Charles C. Taylor charles@maths.leeds.ac.uk Department of Statistics, University of Leeds, Leeds LS2 9JT, UKSupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.ABSTRACTRegression of data represented as points…

球面数据的非参数回归AI 解读

We develop nonparametric smoothing for regression when both the predictor and the response variables are defined on a sphere of whatever dimension. A local polynomial fitting approach is pursued, which retains all the advantages in terms of rate optimality, interpretability, and ease of implementation widely observed in the standard setting. Our estimates have a multi-output nature, meaning that each coordinate is separately estimated, within a scheme of a regression with a…

机器学习与统计学：交汇AI 解读

Statistical Properties of Tree-Based Approaches to Classification The Decision Tree Algorithm CAL5 Based on a Statistical Approach to its Splitting Algorithm Probabilistic Symbolic Classifiers: An Empirical Comparison from a Statistical Perspective A Multistrategy Approach to Learning Multiple Dependent Concepts Quality of Decision Rules - Definition and Classification Schemes for Multiple Rules DIPOL - A Hybrid Piecewise Linear Classifier Combining Classification Procedures…

平面点子集的 Procrustes 形状分析AI 解读

Summary Consider a set of points in the plane randomly perturbed about a mean configuration by Gaussian errors. In this paper a Procrustes statistic based on the shapes of subsets of the points is studied, and its approximate distribution is found for small variations. We derive various properties of the distribution including the first two moments, a central limit result and a scaled χ2–-approximation. We concentrate on the independent isotropic Gaussian error case,…

核密度估计中平滑参数的Bootstrap选择AI 解读

Cross-validation based on integrated squared error has already been applied to the choice of smoothing parameter in the kernel method of density estimation. In this paper, an alternative resampling plan, based on the bootstrap, is proposed in an attempt to estimate mean integrated squared error. This leads to a further data-based choice of smoothing parameter. The two methods are compared and some simulations and examples demonstrate the relative merits. For large samples,…