Data for: An RKHS model for variable selection in functional linear regression

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函数型数据

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数据链接：

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资源简介：

R code used in "An RKHS model for variable selection in functional linear regression".

创建时间：

2018-05-05

Variable Selection in the Presence of Factors: A Model Selection Perspective

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贝叶斯模型

In the context of a Gaussian multiple regression model, we address the problem of variable selection when in the list of potential predictors there are factors, that is, categorical variables. We adop

DataCite Commons2021-04-20 更新120

Minimum Regularized Covariance Trace Estimator and Outlier Detection for Functional Data

函数型数据

异常检测

We propose the Minimum Regularized Covariance Trace (MRCT) estimator, a novel method for robust covariance estimation and functional outlier detection designed primarily for dense functional data. The

DataCite Commons2024-05-11 更新50

Variable Selection with Second-Generation P-Values

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惩罚回归

Many statistical methods have been proposed for variable selection in the past century, but few balance inference and prediction tasks well. Here we report on a novel variable selection approach calle

Figshare2021-09-29 更新30

The Mahalanobis Distance for Functional Data With Applications to Classification

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分类

This article presents a new semidistance for functional observations that generalizes the Mahalanobis distance for multivariate datasets. The main characteristics of the functional Mahalanobis semidis

DataCite Commons2020-09-04 更新50

Reversible jump Markov chain Monte Carlo algorithms for Bayesian variable selection in logistic mixed models

贝叶斯统计

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In this article, to reduce computational load in performing Bayesian variable selection, we used a variant of reversible jump Markov chain Monte Carlo methods, and the Holmes and Held (HH) algorithm,

DataCite Commons2020-09-02 更新50

Data for: An RKHS model for variable selection in functional linear regression

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