Robust statistical methods were developed to supplement the classical procedures when the data violate classical assumptions. They are ideally suited to applied research across a broad spectrum of study, yet most books on the subject are narrowly focused, overly theoretical, or simply outdated. Robu
Robust Statistical Methods With R
β Scribed by Jana Jureckova, Jan Picek
- Publisher
- Chapman & Hall/CRC
- Year
- 2006
- Tongue
- English
- Leaves
- 188
- Category
- Library
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
The second edition of Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on new developments and on the computational aspects. There are many numerical examples and notes on the R environment, and the updated chapter on the multivariate model cont
The second edition of Robust Statistical Methods with R provides a systematic treatment of robust procedures with an emphasis on new developments and on the computational aspects. There are many numerical examples and notes on the R environment, and the updated chapter on the multivariate model cont
Developed at Charles University in Prague, this graduate textbook explains the mathematical theory behind maximum likelihood estimators (M-estimators), L-estimators based on order statistics, R-estimators based on the ranks of their residuals, the multivariate location model, and some goodness-of-fi
<p><b>A new edition of this popular text on robust statistics, thoroughly updated to include new and improved methods and focus on implementation of methodology using the increasingly popular open-source software R.</b></p> <p>Classical statistics fail to cope well with outliers associated with devi