Massive Data Sets – Is Data Quality Still an Issue?

Author:

Filzmoser PeterORCID,Mazak-Huemer AlexandraORCID

Publisher

Springer Berlin Heidelberg

Reference15 articles.

1. Alfons, A., Croux, C., Gelper, S.: Sparse least trimmed squares regression for analyzing high-dimensional large data sets. The Annals of Applied Statistics 7(1), 226–248 (2013)

2. Borowski, M., Fried, R.: Online signal extraction by robust regression in moving windows with data-adaptive width selection. Statistics and Computing 24(4), 597–613 (2014)

3. Friedman, J., Hastie, T., Simon, N., Tibshirani, R.: glmnet: Lasso and Elastic Net Regularized Generalized Linear Models. R Foundation for Statistical Computing, Vienna, Austria (2016). http://CRAN.R-project.org/package=glmnet. R package version 2.0-5

4. Heritier, S., Cantoni, E., Copt, S., Victoria-Feser, P.M.: Robust Methods in Biostatistics. John Wiley & Sons, Chichester (2009)

5. Johnson, R., Wichern, D.: Applied Multivariate Statistical Analysis, 7th edn. Prentice Hall, Upper Saddle River, NJ (2007)

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