Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.3K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.3K
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

312
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
312
Biostatistics: Overview01:20

Biostatistics: Overview

230
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
230
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

121
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
121
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

690
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
690
Overview of Minitab01:11

Overview of Minitab

116
Minitab is a statistical software package designed for data analysis. With its origins in the 1970s and development at Pennsylvania State University, Minitab has grown significantly in its capabilities and applications. It plays a crucial role in quality management projects, especially in Six Sigma initiatives, by offering tools for process improvement and statistical analysis. Minitab's significance lies in its user-friendly interface, making complex statistical analysis accessible to...
116

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Mammogram geometric projection localization (MaGPL) for surgical body-surface localization of nonpalpable breast calcifications.

Gland surgery·2026
Same author

Structure-guided protein engineering and immobilization of an amidohydrolase for efficient ochratoxin A detoxification.

International journal of biological macromolecules·2026
Same author

Generalized entropy calibration for analyzing voluntary survey data.

Biometrics·2026
Same author

Temporal association of diffuse large B-cell lymphoma with PD-1 inhibitor therapy in a patient with gastric adenocarcinoma: a case report.

Frontiers in immunology·2026
Same author

Molecular mechanisms and therapies for tumor inhibition through the arginine metabolism pathway.

Frontiers in oncology·2026
Same author

Short-Wavelength Infrared Organic Light-Emitting Diodes from Solution-Processable Aggregation-Induced Emission Molecules.

ACS applied materials & interfaces·2026
Same journal

A Bayesian method for analyzing combinations of continuous, ordinal, and nominal categorical data with missing values.

Journal of multivariate analysis·2026
Same journal

Hierarchical structure-guided high-dimensional multi-view clustering.

Journal of multivariate analysis·2026
Same journal

Quadratic inference with dense functional responses.

Journal of multivariate analysis·2025
Same journal

Graph-constrained Analysis for Multivariate Functional Data.

Journal of multivariate analysis·2025
Same journal

Modeling the Cholesky factors of covariance matrices of multivariate longitudinal data.

Journal of multivariate analysis·2024
Same journal

Nonlinear sufficient dimension reduction for distribution-on-distribution regression.

Journal of multivariate analysis·2024
See all related articles
  1. Home
  3. From Multivariate To Functional Data Analysis: Fundamentals, Recent Developments, And Emerging Areas.
  1. Home
  3. From Multivariate To Functional Data Analysis: Fundamentals, Recent Developments, And Emerging Areas.

Related Experiment Video

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas.

Yehua Li1, Yumou Qiu2, Yuhang Xu3

  • 1University of California - Riverside, Riverside, CA 92521, USA.

Journal of Multivariate Analysis
|July 23, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

Functional data analysis (FDA) models infinite-dimensional data. This review covers FDA

Keywords:
Functional data analysisHigh-dimensional statisticsMulti-level modelingSpatial dependence

More Related Videos

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.2K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

Related Experiment Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.2K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

Area of Science:

  • Statistics
  • Multivariate Analysis
  • Functional Data Analysis

Background:

  • Functional data analysis (FDA) is a statistical field for modeling infinite-dimensional random vectors.
  • It has gained prominence in the Journal of Multivariate Analysis.
  • Understanding FDA's origins and connections to multivariate analysis is crucial.

Purpose of the Study:

  • To review fundamental concepts of functional data analysis.
  • To explore recent advancements in FDA, including multi-level, high-dimensional, and dependent functional data.
  • To discuss the impact of these developments across various scientific and business domains.

Main Methods:

  • Review of fundamental concepts in functional data analysis.
  • Exploration of recent developments: multi-level FDA, high-dimensional functional regression, dependent functional data analysis.
  • Discussion of applications with real data examples.

    • Main Results:

    • Functional data analysis offers advanced methods for complex data.
    • Recent developments extend FDA to multi-level, high-dimensional, and dependent data structures.
    • FDA methodologies have significant impact on diverse fields like genetics and wearable device data.

    Conclusions:

    • Functional data analysis is a rapidly evolving statistical discipline.
    • New methodologies are expanding the scope and applicability of FDA.
    • FDA provides powerful tools for analyzing complex data in science and industry.