| dc.date.accessioned | 8/28/2019 14:29 | |
| dc.date.accessioned | 2022-09-23T13:53:22Z | |
| dc.date.available | 8/28/2019 14:29 | |
| dc.date.available | 2022-09-23T13:53:22Z | |
| dc.date.created | 8/28/2019 14:29 | |
| dc.date.issued | 2011-09 | |
| dc.identifier | 0747-5632 | |
| dc.identifier | https://www.sciencedirect.com/science/article/pii/S0747563210003080 | |
| dc.identifier | http://hdl.handle.net/10818/36915 | |
| dc.identifier | 10.1016/j.chb.2010.06.026 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/3471857 | |
| dc.description.abstract | This paper presents a proposal based on an evolutionary algorithm to impute missing observations in multivariate data. A genetic algorithm based on the minimization of an error function derived from their covariance matrix and vector of means is presented.
All methodological aspects of the genetic structure are presented. An extended explanation of the design of the fitness function is provided. An application example is solved by the proposed method. | |
| dc.language | eng | |
| dc.publisher | Computers in Human Behavior | |
| dc.relation | Computers in Human Behavior
Volume 27, Issue 5, September 2011, Pages 1468-1474 | |
| dc.rights | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.rights | openAccess | |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
| dc.source | Universidad de La Sabana | |
| dc.source | Intellectum Repositorio Universidad de La Sabana | |
| dc.subject | Missing data | |
| dc.subject | Evolutionary optimization | |
| dc.subject | Multivariate analysis | |
| dc.subject | Multiple data imputation | |
| dc.title | Missing data imputation in multivariate data by evolutionary algorithms | |
| dc.type | journal article | |
