| dc.contributor.author | Misztal, Małgorzata | |
| dc.date.accessioned | 2015-06-23T12:40:07Z | |
| dc.date.available | 2015-06-23T12:40:07Z | |
| dc.date.issued | 2013 | |
| dc.identifier.issn | 0208-6018 | |
| dc.identifier.uri | http://hdl.handle.net/11089/10081 | |
| dc.description.abstract | Missing data are quite common in practical applications of statistical methods and
imputation is a general statistical method for the analysis of incomplete data sets.
Stekhoven and Bühlmann (2012) proposed an iterative imputation method (called
“missForest”) based on Random Forests (Breiman 2001) to cope with missing values.
In the paper a short description of “missForest” is presented and some selected missing data
techniques are compared with “missForest” by artificially simulating different proportions and
mechanisms of missing data using complete data sets from the UCI repository of machine learning
databases. | pl_PL |
| dc.description.abstract | W pracy Stekhovena i Bühlmanna (2012) zaproponowano nową iteracyjną metodę imputacji
(nazwaną „missForest”) opartą na metodzie Random Forests Breimana (2001).
W niniejszym artykule omówiono metodę „missForest” i porównano kilka wybranych
technik postępowania w sytuacji występowania braków danych z metodą „missForest”. W tym
celu wykorzystano podejście symulacyjne generując różne proporcje i mechanizmy powstawania
braków danych w zbiorach danych pochodzących głównie z repozytorium baz danych na
Uniwersytecie Kalifornijskim w Irvine. | pl_PL |
| dc.language.iso | en | pl_PL |
| dc.publisher | Wydawnictwo Uniwersytetu Łódzkiego | pl_PL |
| dc.relation.ispartofseries | Acta Universitatis Lodziensis, Folia Oeconomica;285 | |
| dc.subject | missing values | pl_PL |
| dc.subject | single and multiple imputation | pl_PL |
| dc.subject | random forests | pl_PL |
| dc.subject | missForest | pl_PL |
| dc.title | Some Remarks on the Data Imputation Using “missForest” Method | pl_PL |
| dc.title.alternative | Kilka uwag o imputacji danych z wykorzystaniem metody "missforest" | pl_PL |
| dc.type | Article | pl_PL |
| dc.page.number | [169]-179 | pl_PL |
| dc.contributor.authorAffiliation | University of Lodz, Department of Statistical Methods | pl_PL |
| dc.references | Allison P. D. (2002), Missing data, Series: Quantitative Applications in the Social Sciences 07–136, SAGE Publications, Thousand Oaks, London, New Delhi | pl_PL |
| dc.references | Blake C., Keogh E., Merz C. J. (1988), UCI Repository of Machine Learning Datasets, Department of Information and Computer Science, University of California, Irvine | pl_PL |
| dc.references | Breiman, L. (2001), Random Forests, “Machine learning” 45(1): 5–32 | pl_PL |
| dc.references | Little R. J. A., Rubin D. B. (2002), Statistical Analysis with Missing Data, Second Edition, Wiley, New Jersey | pl_PL |
| dc.references | Oba S., Sato M., Takemasa I., Monden M., Matsubara K., Ishii S. (2003), A Bayesian Missing Value Estimation Method for Gene Expression Profile Data, “Bioinformatics” 19(16): 2088–2096 | pl_PL |
| dc.references | Städler N., Bühlmann P. (2010), Pattern Alternating Maximization Algorithm for High- Dimensional Missing Data, Arxiv preprint arXiv:1005.0366 | pl_PL |
| dc.references | Stekhoven D. J., Bühlmann P. (2012), MissForest – Nonparametric Missing Value Imputation for Mixed-Type Data, “Bioinformatics” 28(1): 112–118 | pl_PL |
| dc.references | Troyanskaya O., Cantor M., Sherlock G., Brown P., Hastie T., Tibshirani R., Botstein D., Altman R. (2001), Missing Value Estimation Methods for DNA Microarrays, “Bioinformatics” 17(6): 520–525 | pl_PL |
| dc.references | van Buuren S., Groothuis-Oudshoorn K. (2011), MICE: Multivariate Imputation by Chained Equations in R, „Journal of Statistical Software”, 45(3): 1–67 | pl_PL |
