Robust statistics for signal processing /:
Understand the benefits of robust statistics for signal processing using this unique and authoritative text.
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY, USA :
Cambridge University Press,
2018.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Understand the benefits of robust statistics for signal processing using this unique and authoritative text. |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9781108582759 1108582753 9781139084291 1139084291 |
Internformat
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | ZDB-4-EBA-on1060524627 | ||
| 003 | OCoLC | ||
| 005 | 20241004212047.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 181101s2018 nyu ob 001 0 eng d | ||
| 040 | |a N$T |b eng |e rda |e pn |c N$T |d N$T |d YDX |d EBLCP |d CAMBR |d UIU |d OTZ |d U3W |d OCLCQ |d UKAHL |d OCLCO |d OCLCQ |d OCLCO |d OCLCQ |d OCLCO |d OCLCL | ||
| 066 | |c (S | ||
| 020 | |a 9781108582759 |q (electronic bk.) | ||
| 020 | |a 1108582753 |q (electronic bk.) | ||
| 020 | |a 9781139084291 | ||
| 020 | |a 1139084291 | ||
| 020 | |z 9781107017412 | ||
| 020 | |z 1107017416 | ||
| 035 | |a (OCoLC)1060524627 | ||
| 050 | 4 | |a QA276 |b .Z68 2018eb | |
| 072 | 7 | |a MAT |x 003000 |2 bisacsh | |
| 072 | 7 | |a MAT |x 029000 |2 bisacsh | |
| 082 | 7 | |a 519.5 |2 23 | |
| 049 | |a MAIN | ||
| 100 | 1 | |a Zoubir, Abdelhak M., |e author. |0 http://id.loc.gov/authorities/names/n94076263 | |
| 245 | 1 | 0 | |a Robust statistics for signal processing / |c Abdelhak M. Zoubir, Technische Universität, Darmstadt, Germany, Visa Koivunen, Aalto University, Finland, Esa Ollila Aalto University, Finland, Michael Muma Technische Universität, Darmstadt, Germany. |
| 264 | 1 | |a New York, NY, USA : |b Cambridge University Press, |c 2018. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO, viewed November 1, 2018). | |
| 505 | 0 | |a Cover; Half-title; Title page; Copyright information; Contents; Preface; Abbreviations; List of Symbols; 1 Introduction and Foundations; 1.1 History of Robust Statistics; 1.2 Robust M-estimators for Single-Channel Data; 1.2.1 Location and Scale Estimation; Maximum Likelihood Estimation of Location and Scale; M-estimation of Location and Scale; 1.3 Measures of Robustness; 1.3.1 The Influence Function and Qualitative Robustness; Sensitivity Curve; The Influence Function; Qualitative Robustness of an Estimator; 1.3.2 The Breakdown Point and Quantitative Robustness; The Breakdown Point | |
| 505 | 8 | |a The Maximum-Bias Curve1.4 Concluding Remarks; 2 Robust Estimation: The Linear Regression Model; 2.1 Complex Derivatives and Optimization; 2.2 The Linear Model and Organization of the Chapter; 2.3 The Least Squares Estimator; 2.4 Least Absolute Deviation and Rank-Least Absolute Deviation Regression; 2.4.1 Simple Linear Regression without an Intercept; Weighted Median Regression: The Real-Valued Case; Weighted Median Regression: The Complex-Valued Case; 2.4.2 Simple Linear Regression with Intercept; 2.4.3 Computation of Least Absolute Deviation and Rank-Least Absolute Deviation Estimates | |
| 505 | 8 | |a 2.5 ML- and M-estimates of Regression with an Auxiliary Scale Estimate2.5.1 Objective Function Approach vs. Estimating Equation Approach; 2.5.2 Examples of Loss Functions; 2.5.3 Computation Using the Iteratively Reweighted Least Squares Algorithm; 2.6 Joint M-estimation of Regression and Scale Using Huber's Criterion; 2.6.1 Minimization-Majorization Algorithm; 2.6.2 Minimization-Majorization Algorithm for Huber's Criterion; 2.7 Measures of Robustness; 2.7.1 Outliers in the Linear Regression Model; 2.7.2 (p+1)-dimensional Influence Function; 2.7.3 Breakdown Point | |
| 505 | 8 | |a 3.3.2 Subgradient Equations for the Lasso/Elastic Net3.3.3 Computation of the Lasso/Elastic Net; Cyclic Coordinate Descent Algorithm; Pathwise Coordinate Descent; 3.4 The Least Absolute Deviation-Lasso and the Rank-Lasso; 3.4.1 Simple Linear Regression (p = 1); 3.4.2 The Computation of Least Absolute Deviation-Lasso and Rank-Lasso Estimates: p> 1 Case; 3.4.3 The Fused Rank-Lasso; Image Denoising Example; 3.5 Joint Penalized M-estimation of Regression and Scale; 3.5.1 Algorithm; 3.6 Penalty Parameter Selection; 3.7 Application Example: Prostate Cancer; 3.8 Concluding Remarks | |
| 520 | |a Understand the benefits of robust statistics for signal processing using this unique and authoritative text. | ||
| 650 | 0 | |a Robust statistics. |0 http://id.loc.gov/authorities/subjects/sh85114641 | |
| 650 | 0 | |a Signal processing |x Mathematics. | |
| 650 | 6 | |a Statistiques robustes. | |
| 650 | 6 | |a Traitement du signal |x Mathématiques. | |
| 650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Robust statistics |2 fast | |
| 650 | 7 | |a Signal processing |x Mathematics |2 fast | |
| 700 | 1 | |a Koivunen, Visa, |e author. |0 http://id.loc.gov/authorities/names/n2018028127 | |
| 700 | 1 | |a Ollila, Esa, |d 1974- |e author. |1 https://id.oclc.org/worldcat/entity/E39PCjKYhBTXhmfmkpRy7QctXb |0 http://id.loc.gov/authorities/names/n2018028119 | |
| 700 | 1 | |a Muma, Michael, |d 1981- |e author. |1 https://id.oclc.org/worldcat/entity/E39PCjKkJxhGxYgJ8VVMPRG3jP |0 http://id.loc.gov/authorities/names/n2018028123 | |
| 758 | |i has work: |a Robust statistics for signal processing (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGmg4GgrfRjcKMbbVWJGXm |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 856 | 4 | 0 | |l FWS01 |p ZDB-4-EBA |q FWS_PDA_EBA |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1875046 |3 Volltext |
| 880 | 8 | |6 505-00/(S |a 2.8 Positive Breakdown Point Regression Estimators2.8.1 Least-Median of Squares and Least Trimmed Squares Estimator; 2.8.2 S-Estimators and τ-Estimators; 2.8.3 MM-Estimators; 2.9 Simulation Studies; 2.9.1 Study 1: Randomly Flipped Measurements; 2.9.2 Study 2: Localization of Mobile User Equipment; 2.10 Concluding Remarks; 3 Robust Penalized Regression in the Linear Model; 3.1 Sparse Regression and Outline of the Chapter; 3.2 Extensions of the Lasso Penalty; 3.3 The Lasso and the Elastic Net; 3.3.1 Simple Linear Regression and Soft-Thresholding | |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH35343653 | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL5569616 | ||
| 938 | |a YBP Library Services |b YANK |n 15820356 | ||
| 938 | |a YBP Library Services |b YANK |n 15818566 | ||
| 938 | |a EBSCOhost |b EBSC |n 1875046 | ||
| 994 | |a 92 |b GEBAY | ||
| 912 | |a ZDB-4-EBA | ||
| 049 | |a DE-863 | ||
Datensatz im Suchindex
| DE-BY-FWS_katkey | ZDB-4-EBA-on1060524627 |
|---|---|
| _version_ | 1816882476514017280 |
| adam_text | |
| any_adam_object | |
| author | Zoubir, Abdelhak M. Koivunen, Visa Ollila, Esa, 1974- Muma, Michael, 1981- |
| author_GND | http://id.loc.gov/authorities/names/n94076263 http://id.loc.gov/authorities/names/n2018028127 http://id.loc.gov/authorities/names/n2018028119 http://id.loc.gov/authorities/names/n2018028123 |
| author_facet | Zoubir, Abdelhak M. Koivunen, Visa Ollila, Esa, 1974- Muma, Michael, 1981- |
| author_role | aut aut aut aut |
| author_sort | Zoubir, Abdelhak M. |
| author_variant | a m z am amz v k vk e o eo m m mm |
| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | Q - Science |
| callnumber-label | QA276 |
| callnumber-raw | QA276 .Z68 2018eb |
| callnumber-search | QA276 .Z68 2018eb |
| callnumber-sort | QA 3276 Z68 42018EB |
| callnumber-subject | QA - Mathematics |
| collection | ZDB-4-EBA |
| contents | Cover; Half-title; Title page; Copyright information; Contents; Preface; Abbreviations; List of Symbols; 1 Introduction and Foundations; 1.1 History of Robust Statistics; 1.2 Robust M-estimators for Single-Channel Data; 1.2.1 Location and Scale Estimation; Maximum Likelihood Estimation of Location and Scale; M-estimation of Location and Scale; 1.3 Measures of Robustness; 1.3.1 The Influence Function and Qualitative Robustness; Sensitivity Curve; The Influence Function; Qualitative Robustness of an Estimator; 1.3.2 The Breakdown Point and Quantitative Robustness; The Breakdown Point The Maximum-Bias Curve1.4 Concluding Remarks; 2 Robust Estimation: The Linear Regression Model; 2.1 Complex Derivatives and Optimization; 2.2 The Linear Model and Organization of the Chapter; 2.3 The Least Squares Estimator; 2.4 Least Absolute Deviation and Rank-Least Absolute Deviation Regression; 2.4.1 Simple Linear Regression without an Intercept; Weighted Median Regression: The Real-Valued Case; Weighted Median Regression: The Complex-Valued Case; 2.4.2 Simple Linear Regression with Intercept; 2.4.3 Computation of Least Absolute Deviation and Rank-Least Absolute Deviation Estimates 2.5 ML- and M-estimates of Regression with an Auxiliary Scale Estimate2.5.1 Objective Function Approach vs. Estimating Equation Approach; 2.5.2 Examples of Loss Functions; 2.5.3 Computation Using the Iteratively Reweighted Least Squares Algorithm; 2.6 Joint M-estimation of Regression and Scale Using Huber's Criterion; 2.6.1 Minimization-Majorization Algorithm; 2.6.2 Minimization-Majorization Algorithm for Huber's Criterion; 2.7 Measures of Robustness; 2.7.1 Outliers in the Linear Regression Model; 2.7.2 (p+1)-dimensional Influence Function; 2.7.3 Breakdown Point 3.3.2 Subgradient Equations for the Lasso/Elastic Net3.3.3 Computation of the Lasso/Elastic Net; Cyclic Coordinate Descent Algorithm; Pathwise Coordinate Descent; 3.4 The Least Absolute Deviation-Lasso and the Rank-Lasso; 3.4.1 Simple Linear Regression (p = 1); 3.4.2 The Computation of Least Absolute Deviation-Lasso and Rank-Lasso Estimates: p> 1 Case; 3.4.3 The Fused Rank-Lasso; Image Denoising Example; 3.5 Joint Penalized M-estimation of Regression and Scale; 3.5.1 Algorithm; 3.6 Penalty Parameter Selection; 3.7 Application Example: Prostate Cancer; 3.8 Concluding Remarks |
| ctrlnum | (OCoLC)1060524627 |
| 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 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>06032cam a2200673 i 4500</leader><controlfield tag="001">ZDB-4-EBA-on1060524627</controlfield><controlfield tag="003">OCoLC</controlfield><controlfield tag="005">20241004212047.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr cnu---unuuu</controlfield><controlfield tag="008">181101s2018 nyu ob 001 0 eng d</controlfield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">N$T</subfield><subfield code="b">eng</subfield><subfield code="e">rda</subfield><subfield code="e">pn</subfield><subfield code="c">N$T</subfield><subfield code="d">N$T</subfield><subfield code="d">YDX</subfield><subfield code="d">EBLCP</subfield><subfield code="d">CAMBR</subfield><subfield code="d">UIU</subfield><subfield code="d">OTZ</subfield><subfield code="d">U3W</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">UKAHL</subfield><subfield code="d">OCLCO</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">OCLCO</subfield><subfield code="d">OCLCQ</subfield><subfield code="d">OCLCO</subfield><subfield code="d">OCLCL</subfield></datafield><datafield tag="066" ind1=" " ind2=" "><subfield code="c">(S</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781108582759</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1108582753</subfield><subfield code="q">(electronic bk.)</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781139084291</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1139084291</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">9781107017412</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="z">1107017416</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1060524627</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">QA276</subfield><subfield code="b">.Z68 2018eb</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">MAT</subfield><subfield code="x">003000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">MAT</subfield><subfield code="x">029000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="082" ind1="7" ind2=" "><subfield code="a">519.5</subfield><subfield code="2">23</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">MAIN</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zoubir, Abdelhak M.,</subfield><subfield code="e">author.</subfield><subfield code="0">http://id.loc.gov/authorities/names/n94076263</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Robust statistics for signal processing /</subfield><subfield code="c">Abdelhak M. Zoubir, Technische Universität, Darmstadt, Germany, Visa Koivunen, Aalto University, Finland, Esa Ollila Aalto University, Finland, Michael Muma Technische Universität, Darmstadt, Germany.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York, NY, USA :</subfield><subfield code="b">Cambridge University Press,</subfield><subfield code="c">2018.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Online resource; title from PDF title page (EBSCO, viewed November 1, 2018).</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Cover; Half-title; Title page; Copyright information; Contents; Preface; Abbreviations; List of Symbols; 1 Introduction and Foundations; 1.1 History of Robust Statistics; 1.2 Robust M-estimators for Single-Channel Data; 1.2.1 Location and Scale Estimation; Maximum Likelihood Estimation of Location and Scale; M-estimation of Location and Scale; 1.3 Measures of Robustness; 1.3.1 The Influence Function and Qualitative Robustness; Sensitivity Curve; The Influence Function; Qualitative Robustness of an Estimator; 1.3.2 The Breakdown Point and Quantitative Robustness; The Breakdown Point</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">The Maximum-Bias Curve1.4 Concluding Remarks; 2 Robust Estimation: The Linear Regression Model; 2.1 Complex Derivatives and Optimization; 2.2 The Linear Model and Organization of the Chapter; 2.3 The Least Squares Estimator; 2.4 Least Absolute Deviation and Rank-Least Absolute Deviation Regression; 2.4.1 Simple Linear Regression without an Intercept; Weighted Median Regression: The Real-Valued Case; Weighted Median Regression: The Complex-Valued Case; 2.4.2 Simple Linear Regression with Intercept; 2.4.3 Computation of Least Absolute Deviation and Rank-Least Absolute Deviation Estimates</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">2.5 ML- and M-estimates of Regression with an Auxiliary Scale Estimate2.5.1 Objective Function Approach vs. Estimating Equation Approach; 2.5.2 Examples of Loss Functions; 2.5.3 Computation Using the Iteratively Reweighted Least Squares Algorithm; 2.6 Joint M-estimation of Regression and Scale Using Huber's Criterion; 2.6.1 Minimization-Majorization Algorithm; 2.6.2 Minimization-Majorization Algorithm for Huber's Criterion; 2.7 Measures of Robustness; 2.7.1 Outliers in the Linear Regression Model; 2.7.2 (p+1)-dimensional Influence Function; 2.7.3 Breakdown Point</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.3.2 Subgradient Equations for the Lasso/Elastic Net3.3.3 Computation of the Lasso/Elastic Net; Cyclic Coordinate Descent Algorithm; Pathwise Coordinate Descent; 3.4 The Least Absolute Deviation-Lasso and the Rank-Lasso; 3.4.1 Simple Linear Regression (p = 1); 3.4.2 The Computation of Least Absolute Deviation-Lasso and Rank-Lasso Estimates: p> 1 Case; 3.4.3 The Fused Rank-Lasso; Image Denoising Example; 3.5 Joint Penalized M-estimation of Regression and Scale; 3.5.1 Algorithm; 3.6 Penalty Parameter Selection; 3.7 Application Example: Prostate Cancer; 3.8 Concluding Remarks</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Understand the benefits of robust statistics for signal processing using this unique and authoritative text.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Robust statistics.</subfield><subfield code="0">http://id.loc.gov/authorities/subjects/sh85114641</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Signal processing</subfield><subfield code="x">Mathematics.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Statistiques robustes.</subfield></datafield><datafield tag="650" ind1=" " ind2="6"><subfield code="a">Traitement du signal</subfield><subfield code="x">Mathématiques.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MATHEMATICS</subfield><subfield code="x">Applied.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MATHEMATICS</subfield><subfield code="x">Probability & Statistics</subfield><subfield code="x">General.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Robust statistics</subfield><subfield code="2">fast</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Signal processing</subfield><subfield code="x">Mathematics</subfield><subfield code="2">fast</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Koivunen, Visa,</subfield><subfield code="e">author.</subfield><subfield code="0">http://id.loc.gov/authorities/names/n2018028127</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ollila, Esa,</subfield><subfield code="d">1974-</subfield><subfield code="e">author.</subfield><subfield code="1">https://id.oclc.org/worldcat/entity/E39PCjKYhBTXhmfmkpRy7QctXb</subfield><subfield code="0">http://id.loc.gov/authorities/names/n2018028119</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Muma, Michael,</subfield><subfield code="d">1981-</subfield><subfield code="e">author.</subfield><subfield code="1">https://id.oclc.org/worldcat/entity/E39PCjKkJxhGxYgJ8VVMPRG3jP</subfield><subfield code="0">http://id.loc.gov/authorities/names/n2018028123</subfield></datafield><datafield tag="758" ind1=" " ind2=" "><subfield code="i">has work:</subfield><subfield code="a">Robust statistics for signal processing (Text)</subfield><subfield code="1">https://id.oclc.org/worldcat/entity/E39PCGmg4GgrfRjcKMbbVWJGXm</subfield><subfield code="4">https://id.oclc.org/worldcat/ontology/hasWork</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="l">FWS01</subfield><subfield code="p">ZDB-4-EBA</subfield><subfield code="q">FWS_PDA_EBA</subfield><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1875046</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="880" ind1="8" ind2=" "><subfield code="6">505-00/(S</subfield><subfield code="a">2.8 Positive Breakdown Point Regression Estimators2.8.1 Least-Median of Squares and Least Trimmed Squares Estimator; 2.8.2 S-Estimators and τ-Estimators; 2.8.3 MM-Estimators; 2.9 Simulation Studies; 2.9.1 Study 1: Randomly Flipped Measurements; 2.9.2 Study 2: Localization of Mobile User Equipment; 2.10 Concluding Remarks; 3 Robust Penalized Regression in the Linear Model; 3.1 Sparse Regression and Outline of the Chapter; 3.2 Extensions of the Lasso Penalty; 3.3 The Lasso and the Elastic Net; 3.3.1 Simple Linear Regression and Soft-Thresholding</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">Askews and Holts Library Services</subfield><subfield code="b">ASKH</subfield><subfield code="n">AH35343653</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">ProQuest Ebook Central</subfield><subfield code="b">EBLB</subfield><subfield code="n">EBL5569616</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">YBP Library Services</subfield><subfield code="b">YANK</subfield><subfield code="n">15820356</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">YBP Library Services</subfield><subfield code="b">YANK</subfield><subfield code="n">15818566</subfield></datafield><datafield tag="938" ind1=" " ind2=" "><subfield code="a">EBSCOhost</subfield><subfield code="b">EBSC</subfield><subfield code="n">1875046</subfield></datafield><datafield tag="994" ind1=" " ind2=" "><subfield code="a">92</subfield><subfield code="b">GEBAY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-EBA</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-863</subfield></datafield></record></collection> |
| id | ZDB-4-EBA-on1060524627 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:29:12Z |
| institution | BVB |
| isbn | 9781108582759 1108582753 9781139084291 1139084291 |
| language | English |
| oclc_num | 1060524627 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource |
| psigel | ZDB-4-EBA |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Cambridge University Press, |
| record_format | marc |
| spelling | Zoubir, Abdelhak M., author. http://id.loc.gov/authorities/names/n94076263 Robust statistics for signal processing / Abdelhak M. Zoubir, Technische Universität, Darmstadt, Germany, Visa Koivunen, Aalto University, Finland, Esa Ollila Aalto University, Finland, Michael Muma Technische Universität, Darmstadt, Germany. New York, NY, USA : Cambridge University Press, 2018. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. Online resource; title from PDF title page (EBSCO, viewed November 1, 2018). Cover; Half-title; Title page; Copyright information; Contents; Preface; Abbreviations; List of Symbols; 1 Introduction and Foundations; 1.1 History of Robust Statistics; 1.2 Robust M-estimators for Single-Channel Data; 1.2.1 Location and Scale Estimation; Maximum Likelihood Estimation of Location and Scale; M-estimation of Location and Scale; 1.3 Measures of Robustness; 1.3.1 The Influence Function and Qualitative Robustness; Sensitivity Curve; The Influence Function; Qualitative Robustness of an Estimator; 1.3.2 The Breakdown Point and Quantitative Robustness; The Breakdown Point The Maximum-Bias Curve1.4 Concluding Remarks; 2 Robust Estimation: The Linear Regression Model; 2.1 Complex Derivatives and Optimization; 2.2 The Linear Model and Organization of the Chapter; 2.3 The Least Squares Estimator; 2.4 Least Absolute Deviation and Rank-Least Absolute Deviation Regression; 2.4.1 Simple Linear Regression without an Intercept; Weighted Median Regression: The Real-Valued Case; Weighted Median Regression: The Complex-Valued Case; 2.4.2 Simple Linear Regression with Intercept; 2.4.3 Computation of Least Absolute Deviation and Rank-Least Absolute Deviation Estimates 2.5 ML- and M-estimates of Regression with an Auxiliary Scale Estimate2.5.1 Objective Function Approach vs. Estimating Equation Approach; 2.5.2 Examples of Loss Functions; 2.5.3 Computation Using the Iteratively Reweighted Least Squares Algorithm; 2.6 Joint M-estimation of Regression and Scale Using Huber's Criterion; 2.6.1 Minimization-Majorization Algorithm; 2.6.2 Minimization-Majorization Algorithm for Huber's Criterion; 2.7 Measures of Robustness; 2.7.1 Outliers in the Linear Regression Model; 2.7.2 (p+1)-dimensional Influence Function; 2.7.3 Breakdown Point 3.3.2 Subgradient Equations for the Lasso/Elastic Net3.3.3 Computation of the Lasso/Elastic Net; Cyclic Coordinate Descent Algorithm; Pathwise Coordinate Descent; 3.4 The Least Absolute Deviation-Lasso and the Rank-Lasso; 3.4.1 Simple Linear Regression (p = 1); 3.4.2 The Computation of Least Absolute Deviation-Lasso and Rank-Lasso Estimates: p> 1 Case; 3.4.3 The Fused Rank-Lasso; Image Denoising Example; 3.5 Joint Penalized M-estimation of Regression and Scale; 3.5.1 Algorithm; 3.6 Penalty Parameter Selection; 3.7 Application Example: Prostate Cancer; 3.8 Concluding Remarks Understand the benefits of robust statistics for signal processing using this unique and authoritative text. Robust statistics. http://id.loc.gov/authorities/subjects/sh85114641 Signal processing Mathematics. Statistiques robustes. Traitement du signal Mathématiques. MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Robust statistics fast Signal processing Mathematics fast Koivunen, Visa, author. http://id.loc.gov/authorities/names/n2018028127 Ollila, Esa, 1974- author. https://id.oclc.org/worldcat/entity/E39PCjKYhBTXhmfmkpRy7QctXb http://id.loc.gov/authorities/names/n2018028119 Muma, Michael, 1981- author. https://id.oclc.org/worldcat/entity/E39PCjKkJxhGxYgJ8VVMPRG3jP http://id.loc.gov/authorities/names/n2018028123 has work: Robust statistics for signal processing (Text) https://id.oclc.org/worldcat/entity/E39PCGmg4GgrfRjcKMbbVWJGXm https://id.oclc.org/worldcat/ontology/hasWork FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1875046 Volltext 505-00/(S 2.8 Positive Breakdown Point Regression Estimators2.8.1 Least-Median of Squares and Least Trimmed Squares Estimator; 2.8.2 S-Estimators and τ-Estimators; 2.8.3 MM-Estimators; 2.9 Simulation Studies; 2.9.1 Study 1: Randomly Flipped Measurements; 2.9.2 Study 2: Localization of Mobile User Equipment; 2.10 Concluding Remarks; 3 Robust Penalized Regression in the Linear Model; 3.1 Sparse Regression and Outline of the Chapter; 3.2 Extensions of the Lasso Penalty; 3.3 The Lasso and the Elastic Net; 3.3.1 Simple Linear Regression and Soft-Thresholding |
| spellingShingle | Zoubir, Abdelhak M. Koivunen, Visa Ollila, Esa, 1974- Muma, Michael, 1981- Robust statistics for signal processing / Cover; Half-title; Title page; Copyright information; Contents; Preface; Abbreviations; List of Symbols; 1 Introduction and Foundations; 1.1 History of Robust Statistics; 1.2 Robust M-estimators for Single-Channel Data; 1.2.1 Location and Scale Estimation; Maximum Likelihood Estimation of Location and Scale; M-estimation of Location and Scale; 1.3 Measures of Robustness; 1.3.1 The Influence Function and Qualitative Robustness; Sensitivity Curve; The Influence Function; Qualitative Robustness of an Estimator; 1.3.2 The Breakdown Point and Quantitative Robustness; The Breakdown Point The Maximum-Bias Curve1.4 Concluding Remarks; 2 Robust Estimation: The Linear Regression Model; 2.1 Complex Derivatives and Optimization; 2.2 The Linear Model and Organization of the Chapter; 2.3 The Least Squares Estimator; 2.4 Least Absolute Deviation and Rank-Least Absolute Deviation Regression; 2.4.1 Simple Linear Regression without an Intercept; Weighted Median Regression: The Real-Valued Case; Weighted Median Regression: The Complex-Valued Case; 2.4.2 Simple Linear Regression with Intercept; 2.4.3 Computation of Least Absolute Deviation and Rank-Least Absolute Deviation Estimates 2.5 ML- and M-estimates of Regression with an Auxiliary Scale Estimate2.5.1 Objective Function Approach vs. Estimating Equation Approach; 2.5.2 Examples of Loss Functions; 2.5.3 Computation Using the Iteratively Reweighted Least Squares Algorithm; 2.6 Joint M-estimation of Regression and Scale Using Huber's Criterion; 2.6.1 Minimization-Majorization Algorithm; 2.6.2 Minimization-Majorization Algorithm for Huber's Criterion; 2.7 Measures of Robustness; 2.7.1 Outliers in the Linear Regression Model; 2.7.2 (p+1)-dimensional Influence Function; 2.7.3 Breakdown Point 3.3.2 Subgradient Equations for the Lasso/Elastic Net3.3.3 Computation of the Lasso/Elastic Net; Cyclic Coordinate Descent Algorithm; Pathwise Coordinate Descent; 3.4 The Least Absolute Deviation-Lasso and the Rank-Lasso; 3.4.1 Simple Linear Regression (p = 1); 3.4.2 The Computation of Least Absolute Deviation-Lasso and Rank-Lasso Estimates: p> 1 Case; 3.4.3 The Fused Rank-Lasso; Image Denoising Example; 3.5 Joint Penalized M-estimation of Regression and Scale; 3.5.1 Algorithm; 3.6 Penalty Parameter Selection; 3.7 Application Example: Prostate Cancer; 3.8 Concluding Remarks Robust statistics. http://id.loc.gov/authorities/subjects/sh85114641 Signal processing Mathematics. Statistiques robustes. Traitement du signal Mathématiques. MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Robust statistics fast Signal processing Mathematics fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85114641 |
| title | Robust statistics for signal processing / |
| title_auth | Robust statistics for signal processing / |
| title_exact_search | Robust statistics for signal processing / |
| title_full | Robust statistics for signal processing / Abdelhak M. Zoubir, Technische Universität, Darmstadt, Germany, Visa Koivunen, Aalto University, Finland, Esa Ollila Aalto University, Finland, Michael Muma Technische Universität, Darmstadt, Germany. |
| title_fullStr | Robust statistics for signal processing / Abdelhak M. Zoubir, Technische Universität, Darmstadt, Germany, Visa Koivunen, Aalto University, Finland, Esa Ollila Aalto University, Finland, Michael Muma Technische Universität, Darmstadt, Germany. |
| title_full_unstemmed | Robust statistics for signal processing / Abdelhak M. Zoubir, Technische Universität, Darmstadt, Germany, Visa Koivunen, Aalto University, Finland, Esa Ollila Aalto University, Finland, Michael Muma Technische Universität, Darmstadt, Germany. |
| title_short | Robust statistics for signal processing / |
| title_sort | robust statistics for signal processing |
| topic | Robust statistics. http://id.loc.gov/authorities/subjects/sh85114641 Signal processing Mathematics. Statistiques robustes. Traitement du signal Mathématiques. MATHEMATICS Applied. bisacsh MATHEMATICS Probability & Statistics General. bisacsh Robust statistics fast Signal processing Mathematics fast |
| topic_facet | Robust statistics. Signal processing Mathematics. Statistiques robustes. Traitement du signal Mathématiques. MATHEMATICS Applied. MATHEMATICS Probability & Statistics General. Robust statistics Signal processing Mathematics |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1875046 |
| work_keys_str_mv | AT zoubirabdelhakm robuststatisticsforsignalprocessing AT koivunenvisa robuststatisticsforsignalprocessing AT ollilaesa robuststatisticsforsignalprocessing AT mumamichael robuststatisticsforsignalprocessing |