Fundamentals of high-dimensional statistics with exercises and R labs
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| Sprache: | English |
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[2022]
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| Schriftenreihe: | Springer texts in statistics
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| 020 | |a 9783030737917 |c hardcover |9 978-3-030-73791-7 | ||
| 020 | |a 9783030737948 |c softcover |9 978-3-030-73794-8 | ||
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| 245 | 1 | 0 | |a Fundamentals of high-dimensional statistics |b with exercises and R labs |c Johannes Lederer |
| 264 | 1 | |a Cham |b Springer |c [2022] | |
| 264 | 4 | |c © 2022 | |
| 300 | |a XIV, 355 Seiten |b Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Springer texts in statistics | |
| 650 | 4 | |a Statistical Theory and Methods | |
| 650 | 4 | |a Big Data | |
| 650 | 4 | |a Data Structures and Information Theory | |
| 650 | 4 | |a Artificial Intelligence | |
| 650 | 4 | |a Statistics and Computing/Statistics Programs | |
| 650 | 4 | |a Machine Learning | |
| 650 | 4 | |a Statistics | |
| 650 | 4 | |a Big data | |
| 650 | 4 | |a Data structures (Computer science) | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Machine learning | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-030-73792-4 |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033026971&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-033026971 | |
Datensatz im Suchindex
| _version_ | 1819740533102215168 |
|---|---|
| adam_text | XIII Contents 1 Introduction........................................................................................................ 1.1 Embracing High-Dimensionality..................................... 1.2 1.3 1.4 1.5 Statistical Limitations of Classical Estimators................................................................... 1.6 1.7 Exercises.................................................................................................................................. R Lab: Least-Squares vs. Ridge Estimation........................................................................ 19 25 1.8 Notes and References........................................................................................................... 34 2 Incorporating Prior Information......................................................................................... Regularization for Increasing the Numerical Stability*................................... Outlook................................................................................................................................... Linear Regression......................... ;........ 1 2 5 10 13 18 37 38 41 45 48 53 63 71 77 2.1 2.2 Overview.................................................. 2.3 Post-Processing Methods..................................................................................................... 2.4 2.5 2.6 2.7 2.8 Hôlder Inequality*................ . .................................................................................. 3 Graphical
Models.......................................................................... 81 3.1 Overview................................................................................................................................ 82 3.2 3.3 Gaussian Graphical Models............................................ 85 87 3.4 3.5 3.6 Neighborhood Selection..................................................................................................... 91 Exercises.................................................................................................................................. 95 R Lab: Estimating a Gene-Gene Coactivation Network................................................. 98 3.7 Notes and References........................................................................................................... 1°7 4 Tuning-Parameter Calibration............................... 1θ9 4.1 Overview................................................................................................................................ 4.2 4.3 4.4 Bounds on the Lasso s Effective Noise............................................................................... HO 113 HO 1շ1 4.5 Exercises.................................................................................................................................. 127 Sparsity-Inducing Prior Functions..................................................................................... Optimality Conditions*........................................................................................................
Exercises............................................................... R Lab: Overfitting.................................................................................................................. Notes and References................................................................................................... —· Maximum Regularized Likelihood Estimation............................................................ Cross-Validation................... . ............................................................................................... Adaptive Validation.............................................................................................................
XIV Contents 46 R Lab: Cross-Validation.................................................................................................. 4.7 Notes and References.................................................................................................... 5 Inference............................................................................................................ 5.1 5.2 One-Step Estimators..................................................................................................... Confidence Intervals..................................................................................................... 5.3 5.4 5.5 Exercises......................................................................................................................... R Lab: Confidence Intervals in Low and High Dimensions........................................... 157 Notes and References.................................................................................................... 166 6 Theory I: Prediction........................................................................................... 169 6.1 6.2 6.3 6.4 Overview........................................................................................................................ Basic Inequalities........................................................................................................... Prediction Guarantees................................................................................................... Prediction Guarantees for Sparse and Weakly Correlated
Models.............................. 170 175 182 192 6.5 6.6 Exercises.......................................................................................................................... 204 Notes and References..................................................................................................... 208 7 Theory 11: Estimation and Support Recovery................................................. 211 7.1 7.2 7.3 7.4 7.5 7.6 Overview......................................................................................................................... Estimation Guarantees in tL·Ioss.................................................................................... Estimation Guarantees in Հ-Խտտ.................................................................................... Support Recovery Guarantees........................................................................................ Exercises........................................................................................................................... Notes and References..................................................................................................... 212 213 218 231 235 237 A Supplementary Information............................................................................ 239 Solutions................................................................................................................ 240 A.l A.2 A.3 A.4 A.5 A.6 A.7 Solutions for ► Chap. 1 ................................................................................................... Solutions
for ► Chap. 2.................................................................................................... Solutions for ► Chap. 3.................................................................................................... Solutions for ► Chap. 4.................................................................................................... Solutions for ► Chap. 5.......................................................................................... Solutions for ► Chap. 6................................................................................. Solutions for ► Chap. 7............................... ................................................................... 240 257 271 2g5 2gg 295 300 В Mathematical Background.................................................................. 3θ9 B.1 B.2 Analysis............................................................................................................................. 3og Matrix Algebra ........................................................................ 131 135 139 140 146 153 Bibliography.................................................................... ........... „„ Index............................ ............... ........................................................................................ 345
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| any_adam_object | 1 |
| author | Lederer, Johannes 1984- |
| author_GND | (DE-588)1055925775 |
| author_facet | Lederer, Johannes 1984- |
| author_role | aut |
| author_sort | Lederer, Johannes 1984- |
| author_variant | j l jl |
| building | Verbundindex |
| bvnumber | BV047642767 |
| classification_rvk | SK 850 QH 230 |
| ctrlnum | (OCoLC)1299306894 (DE-599)BVBBV047642767 |
| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 |
| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV047642767 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-24T09:02:22Z |
| institution | BVB |
| isbn | 9783030737917 9783030737948 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033026971 |
| oclc_num | 1299306894 |
| open_access_boolean | |
| owner | DE-11 DE-473 DE-BY-UBG DE-521 |
| owner_facet | DE-11 DE-473 DE-BY-UBG DE-521 |
| physical | XIV, 355 Seiten Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Springer |
| record_format | marc |
| series2 | Springer texts in statistics |
| spellingShingle | Lederer, Johannes 1984- Fundamentals of high-dimensional statistics with exercises and R labs Statistical Theory and Methods Big Data Data Structures and Information Theory Artificial Intelligence Statistics and Computing/Statistics Programs Machine Learning Statistics Big data Data structures (Computer science) Artificial intelligence Machine learning |
| title | Fundamentals of high-dimensional statistics with exercises and R labs |
| title_auth | Fundamentals of high-dimensional statistics with exercises and R labs |
| title_exact_search | Fundamentals of high-dimensional statistics with exercises and R labs |
| title_full | Fundamentals of high-dimensional statistics with exercises and R labs Johannes Lederer |
| title_fullStr | Fundamentals of high-dimensional statistics with exercises and R labs Johannes Lederer |
| title_full_unstemmed | Fundamentals of high-dimensional statistics with exercises and R labs Johannes Lederer |
| title_short | Fundamentals of high-dimensional statistics |
| title_sort | fundamentals of high dimensional statistics with exercises and r labs |
| title_sub | with exercises and R labs |
| topic | Statistical Theory and Methods Big Data Data Structures and Information Theory Artificial Intelligence Statistics and Computing/Statistics Programs Machine Learning Statistics Big data Data structures (Computer science) Artificial intelligence Machine learning |
| topic_facet | Statistical Theory and Methods Big Data Data Structures and Information Theory Artificial Intelligence Statistics and Computing/Statistics Programs Machine Learning Statistics Big data Data structures (Computer science) Artificial intelligence Machine learning |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033026971&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT ledererjohannes fundamentalsofhighdimensionalstatisticswithexercisesandrlabs |