A Guide to Robust Statistical Methods
Robust statistical methods are now being used in a wide range of disciplines. The appeal of these methods is that they are designed to perform about as well as classic techniques when standard assumptions are true—but they continue to perform well in situations where classic methods perform poorly....
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| Formaat: | Elektronisch E-boek |
| Taal: | Engels |
| Gepubliceerd in: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2023.
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| Editie: | 1st ed. 2023. |
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| Online toegang: | https://doi.org/10.1007/978-3-031-41713-9 |
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| 100 | 1 | |a Wilcox, Rand R. |e author. |0 (orcid)0000-0002-2524-2976 |1 https://orcid.org/0000-0002-2524-2976 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 2 | |a A Guide to Robust Statistical Methods |h [electronic resource] / |c by Rand R. Wilcox. |
| 250 | |a 1st ed. 2023. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2023. | |
| 300 | |a 1 online resource (XVII, 326 p. 57 illus.). | ||
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| 505 | 0 | |a 1. Introduction -- 2. The one-sample case -- 3. Comparing two independent groups -- 4. Comparing two dependent groups -- 5. Comparing multiple independent groups -- 6. Comparing multiple dependent groups -- 7. Robust regression estimators -- 8. Inferential methods based on robust regression estimators -- 9. Measures of association -- 10. Comparing groups when there is a covariate. | |
| 520 | |a Robust statistical methods are now being used in a wide range of disciplines. The appeal of these methods is that they are designed to perform about as well as classic techniques when standard assumptions are true—but they continue to perform well in situations where classic methods perform poorly. This book provides a relatively non-technical guide to modern methods. The focus is on applying modern methods using R, understanding when and why classic methods can be unsatisfactory, and fostering a conceptual understanding of the relative merits of different techniques. A recurring theme is that no single method reveals everything one would like to know about the population under study. An appeal of robust methods is that under general conditions they provide much higher power than conventional techniques. Perhaps more importantly, they help provide a deeper and more nuanced understanding of data. The book is for readers who had at least one semester of statistics, aimed at non-statisticians. | ||
| 532 | 8 | |a Accessibility summary: This PDF does Not fully comply with PDF/UA standards but does include limited screen reader support, descriptions for Non-text content (such as images and graphs), bookmarks for easy navigation, and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognise the importance of accessibility and welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us via https://www.springernature.com/gp/info/accessibility | |
| 532 | 8 | |a Inaccessible, or known limited accessibility | |
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| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Statistics . | |
| 650 | 0 | |a Sampling (Statistics). | |
| 650 | 1 | 4 | |a Statistical Theory and Methods. |
| 650 | 2 | 4 | |a Applied Statistics. |
| 650 | 2 | 4 | |a Methodology of Data Collection and Processing. |
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