Bootstrap Methods for Canonical Correlation Analysis of Functional Data
Journal Title: Journal of Data Science and Intelligent Systems - Year 2024, Vol 2, Issue 3
Abstract
The bootstrap method is a very general resampling procedure for investigating the distributional property of statistics. In this paper, we present two bootstrap methods with the aim of studying the functional canonical components for functional data. The bootstrap I method constructs the bootstrap replications by resampling from the raw data, while the bootstrap II algorithm samples with replacement from the principal component scores. Simulation studies are conducted to examine the performance of the proposed bootstrap methods. The method is also applied to the motion analysis dataset, which consists of the angles formed by the hip and knee of each of 39 children over each child’s gait cycle. Numerical simulations and real data analysis show the good performance of both bootstrap methods for functional canonical correlation analysis. Moreover, as measured by the mean error and mean squared error, the bootstrap II algorithm performs better in approximating sample canonical components than the bootstrap I method.
Authors and Affiliations
Haoyu Yu, Lihong Wang
Keywords
Related Articles
- EP ID EP752190
- DOI 10.47852/bonviewJDSIS32021578
- Views 45
- Downloads 0
ARM for Analyzing Factors Influencing Vaccinations During the COVID-19 Outbreak
This article investigates factors influencing Coronavirus 2019 (COVID-19) vaccinations and public concerns using association rule mining (ARM). The experiment was conducted in Phuket at the beginning of 2022 when many pe...
Chemical Engineering Numerical Analysis with R: Peng-Robinson Equation of State
Likely, many text on MATLAB, C++, FORTRAN and Python programming languages exist in chemical engineering libraries, discussing their applications for chemical engineering numerical analysis. R programming language, which...
Establishing an Optimal Online Phishing Detection Method: Evaluating Topological NLP Transformers on Text Message Data
This research establishes an optimal classification model for online SMS spam detection by utilizing topological sentence transformer methodologies. The study is a response to the increasing sophisticated and disruptive...
Correlation Filters in Machine Learning Algorithms to Select Demographic and Individual Features for Autism Spectrum Disorder Diagnosis
Autism spectrum disorder is currently considered one of the main neurodevelopmental disorders with predominant characteristics of difficulty in social communication and cognitive skills, and limited and repetitive patter...
The Evolving Landscape of Oil and Gas Chemicals: Convergence of Artificial Intelligence and Chemical-Enhanced Oil Recovery in the Energy Transition Toward Sustainable Energy Systems and Net-Zero Emissions
Chemical-enhanced oil recovery (EOR) is a field of study that can gain significantly from artificial intelligence (AI), addressing uncertainties such as mobility control, interfacial tension reduction, wettability altera...