数据链接：
官方服务：
资源简介：
Code and input files for the kernel smoothing analysis
应用场景：
创建时间：
2015-04-22
相关数据集
Penalized nonparametric scalar-on-function regression via principal coordinates
A number of classical approaches to nonparametric regression have recently been extended to the case of functional predictors. This paper introduces a new method of this type, which extends intermedia
DataCite Commons2020-09-04 更新190
Appendix B. Derivations of conventional and autocorrelated kernel-density estimators.
Derivations of conventional and autocorrelated kernel-density estimators.
DataCite Commons2020-09-03 更新90
Fully Nonparametric Regression for Bounded Data Using Dependent Bernstein Polynomials
We propose a novel class of probability models for sets of predictor-dependent probability distributions with bounded domain. The proposal extends the Dirichlet–Bernstein prior for single density esti
Figshare2016-05-13 更新70
Kernel Density Smoothing Using Probability Density Functions and Orthogonal Polynomials
This article is the first of a series devoted to providing a way to correctly explore stock market data through kernel smoothing methods. Here, we are mainly interested in kernel density smoothing, ou
DataCite Commons2020-09-18 更新100
Appendix B. Derivations of conventional and autocorrelated kernel-density estimators.
Derivations of conventional and autocorrelated kernel-density estimators.
DataCite Commons2025-05-01 更新90
