Keywords
HierarchyComputer scienceFactor (programming language)Profiling (computer programming)Set (abstract data type)Hierarchical database modelPerspective (graphical)Data miningStatisticsMathematicsArtificial intelligence
Affiliated Institutions
Related Publications
Further analysis of the data by Akaike's information criterion and the finite corrections
Using Akaike's information criterion, three examples of statistical data are reanalyzed and show reasonably definite conclusions. One is concerned with the multiple comparison p...
Principal component analysis
Abstract Principal component analysis (PCA) is a multivariate technique that analyzes a data table in which observations are described by several inter‐correlated quantitative d...
Factor Analysis and AIC
The information criterion AIC was introduced to extend the method of maximum likelihood to the multimodel situation. It was obtained by relating the successful experience of the...
A Reliability Coefficient for Maximum Likelihood Factor Analysis
Maximum likelihood factor analysis provides an effective method for estimation of factor matrices and a useful test statistic in the likelihood ratio for rejection of overly sim...
Correspondence Analysis in Practice
Preface Scatterplots and Maps Profiles and the Profile Space Masses and Centroids Chi-Square Distance and Inertia Plotting Chi-Square Distances Reduction of Dimensionality Optim...
Publication Info
- Year
- 2003
- Type
- article
- Volume
- 14
- Issue
- 5-6
- Pages
- 397-403
- Citations
- 87
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
87
OpenAlex
Cite This
S Le Dien,
Jacques Pagès
(2003).
Hierarchical Multiple Factor Analysis: application to the comparison of sensory profiles.
Food Quality and Preference
, 14
(5-6)
, 397-403.
https://doi.org/10.1016/s0950-3293(03)00027-2
Identifiers
- DOI
- 10.1016/s0950-3293(03)00027-2