J Fernando Vera
Biographic Data
| ID | 8867144 |
|---|---|
| NAME | J Fernando Vera |
| GIVEN NAMES | J Fernando |
| FAMILY NAME | Vera |
| SIGNATURE | VERA J F |
| AFFILIATIONS | Universidad de Granada |
| ORCID | 0000-0002-6499-7132 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2022 |
| H-INDEX | 0 |
Avoiding Degeneracies in Ordinal Unfolding Using Kemeny-Equivalent Dissimilarities for Two-Way Two-Mode Preference Rank Data
In this paper a simple but effective procedure to avoid degeneracies in ordinal Unfolding for preference rank data based on the Kemeny distance is proposed. Considering Unfolding as a particular MDS procedure with missing within-set proximities, unknown proximities are first estimated using correlations related to the Kemeny distance, and then the complete proximity matrix is analyzed in a standard MDS framework. A simulation study shows that our…
A Latent Block Distance-Association Model for Profile by Profile Cross-Classified Categorical Data
Distance association models constitute a useful tool for the analysis and graphical representation of cross-classified data in which distances between points inversely describe the association between two categorical variables. When the number of cells is large and the data counts result in sparse tables, the combination of clustering and representation reduces the number of parameters to be estimated and facilitates interpretation. In this artic…
No prominent works on this page.
A Latent Block Distance-Association Model for Profile by Profile Cross-Classified Categorical Data
Distance association models constitute a useful tool for the analysis and graphical representation of cross-classified data in which distances between points inversely describe the association between two categorical variables. When the number of cells is large and the data counts result in sparse tables, the combination of clustering and representation reduces the number of parameters to be estimated and facilitates interpretation. In this artic…
Avoiding Degeneracies in Ordinal Unfolding Using Kemeny-Equivalent Dissimilarities for Two-Way Two-Mode Preference Rank Data
In this paper a simple but effective procedure to avoid degeneracies in ordinal Unfolding for preference rank data based on the Kemeny distance is proposed. Considering Unfolding as a particular MDS procedure with missing within-set proximities, unknown proximities are first estimated using correlations related to the Kemeny distance, and then the complete proximity matrix is analyzed in a standard MDS framework. A simulation study shows that our…
Computer Science (2 works) · Data mining (2 works) · Mathematics (2 works) · Statistics (2 works) · Algorithm (1 work) · Artificial Intelligence (1 work) · Artificial Intelligence (1 work) · Bayesian Modeling and Causal Inference (1 work) · Block (permutation group theory) (1 work) · Categorical variable (1 work)