| 000 | 03596nam a22005775i 4500 | ||
|---|---|---|---|
| 001 | 978-3-030-95864-0 | ||
| 003 | DE-He213 | ||
| 005 | 20240508090327.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 221004s2022 sz | s |||| 0|eng d | ||
| 020 |
_a9783030958640 _9978-3-030-95864-0 |
||
| 024 | 7 |
_a10.1007/978-3-030-95864-0 _2doi |
|
| 050 | 4 | _aQA276-280 | |
| 072 | 7 |
_aPBT _2bicssc |
|
| 072 | 7 |
_aMAT029000 _2bisacsh |
|
| 072 | 7 |
_aPBT _2thema |
|
| 082 | 0 | 4 |
_a519.5 _223 |
| 100 | 1 |
_aMathai, Arak M. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 245 | 1 | 0 |
_aMultivariate Statistical Analysis in the Real and Complex Domains _h[electronic resource] / _cby Arak M. Mathai, Serge B. Provost, Hans J. Haubold. |
| 250 | _a1st ed. 2022. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022. |
|
| 300 |
_aXXVII, 921 p. 3 illus. _bonline resource. |
||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _a1. Mathematical Preliminaries -- 2. The Univariate Gaussian and Related Distribution -- 3. Multivariate Gaussian and Related Distributions -- 4. The Matrix-variate Gaussian Distribution -- 5. Matrix-variate Gamma and Beta Distributions -- 6. Hypothesis Testing and Null Distributions -- 7. Rectangular Matrix-variate Distributions -- 8. Distributions of Eigenvalues and Eigenvectors -- 9. Principal Component Analysis -- 10. Canonical Correlation Analysis -- 11. Factor Analysis -- 12. Classification Problems -- 13. Multivariate Analysis of Variance (MANOVA) -- 14. Profile Analysis and Growth Curves -- 15. Cluster Analysis and Correspondence Analysis. | |
| 506 | 0 | _aOpen Access | |
| 520 | _aThis book explores topics in multivariate statistical analysis, relevant in the real and complex domains. It utilizes simplified and unified notations to render the complex subject matter both accessible and enjoyable, drawing from clear exposition and numerous illustrative examples. The book features an in-depth treatment of theory with a fair balance of applied coverage, and a classroom lecture style so that the learning process feels organic. It also contains original results, with the goal of driving research conversations forward. This will be particularly useful for researchers working in machine learning, biomedical signal processing, and other fields that increasingly rely on complex random variables to model complex-valued data. It can also be used in advanced courses on multivariate analysis. Numerous exercises are included throughout. | ||
| 650 | 0 | _aMathematical statistics. | |
| 650 | 0 | _aStatistics . | |
| 650 | 0 | _aMultivariate analysis. | |
| 650 | 0 | _aSystem theory. | |
| 650 | 1 | 4 | _aMathematical Statistics. |
| 650 | 2 | 4 | _aStatistical Theory and Methods. |
| 650 | 2 | 4 | _aMultivariate Analysis. |
| 650 | 2 | 4 | _aComplex Systems. |
| 700 | 1 |
_aProvost, Serge B. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 700 | 1 |
_aHaubold, Hans J. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030958633 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030958657 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030958664 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-95864-0 |
| 912 | _aZDB-2-SMA | ||
| 912 | _aZDB-2-SXMS | ||
| 912 | _aZDB-2-SOB | ||
| 999 |
_c37766 _d37766 |
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