A Path Algorithm for the Fused Lasso Signal Approximator
The Lasso is a very well-known penalized regression model, which adds an L 1 penalty with parameter λ 1 on the coefficients to the squared error loss function. The Fused Lasso extends this model by also putting an L 1 penalty with parameter λ 2 on the difference of neighboring coefficients, assuming...
Saved in:
| Published in | Journal of computational and graphical statistics Vol. 19; no. 4; pp. 984 - 1006 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Alexandria
Taylor & Francis
01.12.2010
JCGS Management Committee of the American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America Taylor & Francis Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1061-8600 1537-2715 |
| DOI | 10.1198/jcgs.2010.09208 |
Cover
| Abstract | The Lasso is a very well-known penalized regression model, which adds an L
1
penalty with parameter λ
1
on the coefficients to the squared error loss function. The Fused Lasso extends this model by also putting an L
1
penalty with parameter λ
2
on the difference of neighboring coefficients, assuming there is a natural ordering. In this article, we develop a path algorithm for solving the Fused Lasso Signal Approximator that computes the solutions for all values of λ
1
and λ
2
. We also present an approximate algorithm that has considerable speed advantages for a moderate trade-off in accuracy. In the Online Supplement for this article, we provide proofs and further details for the methods developed in the article. |
|---|---|
| AbstractList | The Lasso is a very well-known penalized regression model, which adds an L
1
penalty with parameter λ
1
on the coefficients to the squared error loss function. The Fused Lasso extends this model by also putting an L
1
penalty with parameter λ
2
on the difference of neighboring coefficients, assuming there is a natural ordering. In this article, we develop a path algorithm for solving the Fused Lasso Signal Approximator that computes the solutions for all values of λ
1
and λ
2
. We also present an approximate algorithm that has considerable speed advantages for a moderate trade-off in accuracy. In the Online Supplement for this article, we provide proofs and further details for the methods developed in the article. The Lasso is a very well-known penalized regression model, which adds an L^sub 1^ penalty with parameter ...^sub 1^ on the coefficients to the squared error loss function. The Fused Lasso extends this model by also putting an L^sub 1^ penalty with parameter ...^sub 2^ on the difference of neighboring coefficients, assuming there is a natural ordering. This article develops a path algorithm for solving the Fused Lasso Signal Approximator that computes the solutions for all values of ...^sub 1^ and ...^sub 2^. It also presents an approximate algorithm that has considerable speed advantages for a moderate trade-off in accuracy. (ProQuest: ... denotes formulae/symbols omitted.) The Lasso is a very well-known penalized regression model, which adds an L₁ penalty with parameter λ₁ on the coefficients to the squared error loss function. The Fused Lasso extends this model by also putting an L₁ penalty with parameter λ₂ on the difference of neighboring coefficients, assuming there is a natural ordering. In this article, we develop a path algorithm for solving the Fused Lasso Signal Approximator that computes the solutions for all values of λ₁ and λ₂. We also present an approximate algorithm that has considerable speed advantages for a moderate trade-off in accuracy. In the Online Supplement for this article, we provide proofs and further details for the methods developed in the article. |
| Author | Hoefling, Holger |
| Author_xml | – sequence: 1 givenname: Holger surname: Hoefling fullname: Hoefling, Holger |
| BookMark | eNp9kE1LAzEQhoNUsK2ePQmL920nu5uP9VaKVaGgoJ5Dms22WbabmqRo_72pKx4EPc0MM--8M88IDTrbaYQuMUwwLvm0UWs_ySCWUGbAT9AQk5ylGcNkEHOgOOUU4AyNvG8AANOSDRGfJU8ybJJZu7bOhM02qa1LwkYni73XVbKU3tvk2aw72Saz3c7ZD7OVwbpzdFrL1uuL7zhGr4vbl_l9uny8e5jPlqkqgIVoX8MqL4isuCoZU5RiyFUOEleYqkIVq0pjxUHyTFe1JgxDRmstCSs01aXOx-i63xut3_baB9HYvYvXeMGL-FsGJYlD035IOeu907XYuXimOwgM4khHHOmIIx3xRScqyC-FMkEGY7vgpGn_0V31usZHCD82GWGU5LyI_Zu-b7oIcivfrWsrEeShta52slPGi_yv5Z9JT4gO |
| CitedBy_id | crossref_primary_10_1016_j_acha_2017_12_004 crossref_primary_10_1109_TMI_2022_3161828 crossref_primary_10_1177_13694332231218763 crossref_primary_10_1080_10618600_2015_1008638 crossref_primary_10_1016_j_jmva_2017_08_001 crossref_primary_10_1111_insr_12521 crossref_primary_10_1371_journal_pcbi_1006436 crossref_primary_10_1007_s12532_022_00229_x crossref_primary_10_1111_biom_13632 crossref_primary_10_1080_10618600_2017_1341323 crossref_primary_10_1186_1471_2105_13_205 crossref_primary_10_1093_biomet_asz071 crossref_primary_10_1016_j_chemolab_2015_01_006 crossref_primary_10_1016_j_sigpro_2016_02_015 crossref_primary_10_1016_j_jspi_2018_10_003 crossref_primary_10_1186_s12859_015_0835_2 crossref_primary_10_1007_s42952_021_00128_2 crossref_primary_10_1007_s42952_023_00250_3 crossref_primary_10_1093_bioinformatics_btz529 crossref_primary_10_1177_1176935117690778 crossref_primary_10_1038_s41467_019_09907_2 crossref_primary_10_1214_23_BJPS577 crossref_primary_10_1111_j_1467_9868_2011_00783_x crossref_primary_10_1007_s10898_021_01013_2 crossref_primary_10_1016_j_eswa_2013_08_095 crossref_primary_10_1080_10618600_2019_1660178 crossref_primary_10_1214_24_EJS2322 crossref_primary_10_1109_LSP_2018_2867800 crossref_primary_10_7465_jkdi_2013_24_5_959 crossref_primary_10_1093_bib_bbab270 crossref_primary_10_1214_17_EJS1270 crossref_primary_10_1007_s11222_024_10513_6 crossref_primary_10_1109_TMI_2019_2918839 crossref_primary_10_1080_10618600_2015_1073155 crossref_primary_10_1214_16_AOAS998 crossref_primary_10_1093_biostatistics_kxy035 crossref_primary_10_1002_sta4_198 crossref_primary_10_1016_j_csda_2015_08_013 crossref_primary_10_1002_qre_2192 crossref_primary_10_1080_10618600_2013_878662 crossref_primary_10_1007_s11071_023_09098_y crossref_primary_10_1186_s12859_019_2749_x crossref_primary_10_1214_17_AOS1656 crossref_primary_10_1111_rssb_12033 crossref_primary_10_1080_10618600_2014_948181 crossref_primary_10_1109_LSP_2013_2278339 crossref_primary_10_1214_23_AOS2283 crossref_primary_10_1007_s42081_024_00269_8 crossref_primary_10_1016_j_apm_2024_115694 crossref_primary_10_1214_11_AOS878 crossref_primary_10_1109_TCYB_2018_2845838 crossref_primary_10_1093_jrsssc_qlaf015 crossref_primary_10_1007_s11634_020_00424_5 crossref_primary_10_1631_FITEE_1500148 crossref_primary_10_1016_j_neuroimage_2014_07_033 crossref_primary_10_1214_23_AOS2297 crossref_primary_10_1007_s00180_012_0373_6 crossref_primary_10_1631_FITEE_1601489 crossref_primary_10_1109_LSENS_2018_2884735 crossref_primary_10_1145_3241063 crossref_primary_10_3390_computers13040093 crossref_primary_10_1214_19_EJS1569 crossref_primary_10_1016_j_chemolab_2021_104268 crossref_primary_10_1016_j_jprocont_2016_04_002 crossref_primary_10_1190_geo2015_0151_1 crossref_primary_10_1214_17_EJS1363 crossref_primary_10_1080_00207543_2015_1109723 crossref_primary_10_3892_ijo_2016_3349 crossref_primary_10_1080_10618600_2015_1096789 crossref_primary_10_1016_j_ygeno_2015_07_008 crossref_primary_10_1093_genetics_iyae187 crossref_primary_10_1016_j_csda_2014_05_017 crossref_primary_10_1177_09622802241267355 crossref_primary_10_1214_12_AOAS539 crossref_primary_10_22331_q_2025_03_25_1674 crossref_primary_10_1177_1471082X16652780 crossref_primary_10_1016_j_ejor_2020_09_010 crossref_primary_10_1038_s41467_018_03017_1 crossref_primary_10_1016_j_csda_2022_107581 crossref_primary_10_1016_j_neucom_2022_02_041 crossref_primary_10_1109_TCBB_2016_2599867 crossref_primary_10_1007_s11517_021_02439_2 crossref_primary_10_1111_rssc_12514 crossref_primary_10_1093_bioinformatics_btw485 crossref_primary_10_3389_fgene_2019_00623 crossref_primary_10_1007_s13253_023_00591_w crossref_primary_10_1016_j_sigpro_2016_03_009 crossref_primary_10_1080_10618600_2017_1328363 crossref_primary_10_1007_s11222_016_9654_1 crossref_primary_10_1049_iet_spr_2018_5011 crossref_primary_10_1016_j_sigpro_2016_07_013 crossref_primary_10_1080_03610918_2013_824096 crossref_primary_10_3390_e23121623 crossref_primary_10_1109_TKDE_2014_2373411 crossref_primary_10_1016_j_cie_2018_09_001 crossref_primary_10_1111_rssb_12226 crossref_primary_10_1016_j_csda_2010_10_021 crossref_primary_10_1371_journal_pcbi_1010758 crossref_primary_10_1016_j_chemolab_2015_09_004 crossref_primary_10_1007_s40305_020_00336_3 crossref_primary_10_1016_j_csda_2023_107908 crossref_primary_10_1109_TCYB_2020_2982445 crossref_primary_10_1111_biom_13004 crossref_primary_10_1142_S0217595923500252 crossref_primary_10_1109_TCBB_2022_3172289 crossref_primary_10_1080_10618600_2018_1442343 crossref_primary_10_1007_s11222_024_10432_6 crossref_primary_10_1145_3432312 crossref_primary_10_1007_s10548_017_0615_6 crossref_primary_10_1109_TGRS_2022_3176936 |
| Cites_doi | 10.1109/TSP.2004.840786 10.1214/009053606000001370 10.1111/j.1467-9868.2005.00490.x 10.1111/j.1467-9868.2007.00607.x 10.1214/009053604000000067 10.1093/imanum/20.3.389 10.1111/j.2517-6161.1996.tb02080.x 10.1016/0022-0000(83)90006-5 10.1214/07-AOAS131 |
| ContentType | Journal Article |
| Copyright | 2010 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America
2010 2010 American Statistical Association, the Institute of Mathematical Statistics, and the Interface Foundat Copyright American Statistical Association Dec 2010 |
| Copyright_xml | – notice: 2010 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America 2010 – notice: 2010 American Statistical Association, the Institute of Mathematical Statistics, and the Interface Foundat – notice: Copyright American Statistical Association Dec 2010 |
| DBID | AAYXX CITATION JQ2 |
| DOI | 10.1198/jcgs.2010.09208 |
| DatabaseName | CrossRef ProQuest Computer Science Collection |
| DatabaseTitle | CrossRef ProQuest Computer Science Collection |
| DatabaseTitleList | ProQuest Computer Science Collection |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Statistics Mathematics |
| EISSN | 1537-2715 |
| EndPage | 1006 |
| ExternalDocumentID | 2247845451 10_1198_jcgs_2010_09208 25765384 10712075 |
| Genre | Research Article Feature |
| GroupedDBID | -~X .4S .7F .DC .QJ 0BK 0R~ 2AX 30N 4.4 5GY AAENE AAGDL AAHIA AAJMT AALDU AAMIU AAPUL AAQRR AAWIL ABAWQ ABBHK ABCCY ABFAN ABFIM ABJNI ABLIJ ABLJU ABPAQ ABPEM ABQDR ABTAI ABXSQ ABXUL ABXYU ABYWD ACDIW ACGFO ACGFS ACHJO ACIWK ACMTB ACTCW ACTIO ACTMH ADCVX ADGTB ADODI ADULT ADXHL AEGXH AELLO AENEX AEOZL AEPSL AEUPB AEYOC AFRVT AFVYC AGCQS AGDLA AGLNM AGMYJ AHDZW AIAGR AIHAF AIJEM AKBRZ AKBVH AKOOK ALMA_UNASSIGNED_HOLDINGS ALQZU ALRMG AQRUH AQTUD AVBZW AWYRJ BLEHA CCCUG CS3 D0L DGEBU DKSSO DQDLB DSRWC DU5 EBS ECEWR EJD E~A E~B F5P GTTXZ H13 HQ6 HZ~ H~P IPSME J.P JAA JAAYA JBMMH JBZCM JENOY JHFFW JKQEH JLEZI JLXEF JMS JPL JST KYCEM M4Z MS~ NA5 NY~ O9- P2P PQQKQ RIG RNANH ROSJB RTWRZ RWL RXW S-T SA0 SNACF TAE TASJS TBQAZ TDBHL TEJ TFL TFT TFW TN5 TTHFI TUROJ UT5 UU3 WZA XWC ZGOLN ~S~ ADYSH AMPGV 07G 29K AAIKQ AAKBW AAYXX ACAGQ ACGEE AEUMN AGLEN AGROQ AHMOU ALCKM AMATQ AMEWO AMVHM AMXXU ARCSS BCCOT BPLKW C06 CITATION CRFIH D-I DMQIW DWIFK FEDTE GIFXF HF~ HGD HVGLF IPNFZ IVXBP LJTGL NUSFT QCRFL RNS TAQ TFMCV TOXWX TUS UB9 JQ2 |
| ID | FETCH-LOGICAL-c407t-27f0b345ad8c977c66103c30a1d16c4c4bde1c80a82edfe571026fea574e6e9e3 |
| ISSN | 1061-8600 |
| IngestDate | Wed Aug 13 04:27:13 EDT 2025 Wed Oct 01 06:45:40 EDT 2025 Thu Apr 24 23:08:24 EDT 2025 Thu May 29 08:45:23 EDT 2025 Mon Oct 20 23:48:16 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 4 |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c407t-27f0b345ad8c977c66103c30a1d16c4c4bde1c80a82edfe571026fea574e6e9e3 |
| Notes | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 |
| PQID | 847152095 |
| PQPubID | 29738 |
| PageCount | 23 |
| ParticipantIDs | crossref_primary_10_1198_jcgs_2010_09208 crossref_citationtrail_10_1198_jcgs_2010_09208 informaworld_taylorfrancis_310_1198_jcgs_2010_09208 jstor_primary_25765384 proquest_journals_847152095 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2010-12-01 |
| PublicationDateYYYYMMDD | 2010-12-01 |
| PublicationDate_xml | – month: 12 year: 2010 text: 2010-12-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Alexandria |
| PublicationPlace_xml | – name: Alexandria |
| PublicationTitle | Journal of computational and graphical statistics |
| PublicationYear | 2010 |
| Publisher | Taylor & Francis JCGS Management Committee of the American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America Taylor & Francis Ltd |
| Publisher_xml | – name: Taylor & Francis – name: JCGS Management Committee of the American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America – name: Taylor & Francis Ltd |
| References | Hastie T. (p_7) 2004; 5 p_4 p_12 p_3 p_13 Tibshirani R. (p_14) 1996; 58 p_15 p_10 p_9 p_11 |
| References_xml | – volume: 5 start-page: 1391 year: 2004 ident: p_7 publication-title: The Journal of Machine Learning Research – ident: p_11 doi: 10.1109/TSP.2004.840786 – ident: p_12 doi: 10.1214/009053606000001370 – ident: p_15 doi: 10.1111/j.1467-9868.2005.00490.x – ident: p_10 doi: 10.1111/j.1467-9868.2007.00607.x – ident: p_3 doi: 10.1214/009053604000000067 – ident: p_9 doi: 10.1093/imanum/20.3.389 – volume: 58 start-page: 267 year: 1996 ident: p_14 publication-title: Journal of the Royal Statistical Society, Ser. B doi: 10.1111/j.2517-6161.1996.tb02080.x – ident: p_13 doi: 10.1016/0022-0000(83)90006-5 – ident: p_4 doi: 10.1214/07-AOAS131 |
| SSID | ssj0001697 |
| Score | 2.3751125 |
| Snippet | The Lasso is a very well-known penalized regression model, which adds an L
1
penalty with parameter λ
1
on the coefficients to the squared error loss function.... The Lasso is a very well-known penalized regression model, which adds an L₁ penalty with parameter λ₁ on the coefficients to the squared error loss function.... The Lasso is a very well-known penalized regression model, which adds an L^sub 1^ penalty with parameter ...^sub 1^ on the coefficients to the squared error... |
| SourceID | proquest crossref jstor informaworld |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 984 |
| SubjectTerms | Accuracy Algorithms Approximation Chromosomes Coefficients Comparative genomic hybridization Componentwise operations Convex optimization Datasets Identifiability Image reconstruction Lasso Lasso, LARS, and L₁ Regularization Mathematical independent variables Mathematical models Mathematical problems Penalized regression Regression analysis Studies Vertices |
| Title | A Path Algorithm for the Fused Lasso Signal Approximator |
| URI | https://www.tandfonline.com/doi/abs/10.1198/jcgs.2010.09208 https://www.jstor.org/stable/25765384 https://www.proquest.com/docview/847152095 |
| Volume | 19 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVLSH databaseName: aylor and Francis Online customDbUrl: mediaType: online eissn: 1537-2715 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001697 issn: 1061-8600 databaseCode: AHDZW dateStart: 19970301 isFulltext: true providerName: Library Specific Holdings – providerCode: PRVAWR databaseName: Taylor & Francis Science and Technology Library-DRAA customDbUrl: eissn: 1537-2715 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001697 issn: 1061-8600 databaseCode: 30N dateStart: 19970101 isFulltext: true titleUrlDefault: http://www.tandfonline.com/page/title-lists providerName: Taylor & Francis |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3Nb9MwFLdgXMYBwWCiDJAPTEKaMtLY-fAxglUV6sphqdSb5ThON9Q1g2YS4q_nPSdxU7oh4BJVzmf9e37vZ_t9EPJOqcgIESgvyAXzuIEhlQuuvNIEmukgCpTG9Y7zaTSe8c_zcL7x07XRJXV-qn_eGVfyP6hCG-CKUbL_gKx7KDTAb8AXjoAwHP8K4xQz7F-epMtFBXP8y2vnMzi6XQORnAAxrk4urhbIOFNMHv7j6lo16YXvYqTaVnjoVgdxRd2ms24CJ7Hd5nR2glCZctlWRBlXy0Xr5tuuIOx4Y2Q7xTx6HkWoE8Hke0nkN9snptOTsRfETSSmU6SiJzC8pxVFUwVuV1sLjED4qhfr1sdOBH6yMUzdZvz0ixzNJhOZnc2z7bPWDgMJiRMOTHB4zEY33zysKIY778fsUwP5Q_IoAJ2PhT2YP3V2etiW3un-Xpv4CT7qw2-ftMVZtjLadl6sO5bc0pPsKXnSokjTRkiekQdmdUAen7ukvOsDsn_hMHxOkpSi7FAnOxReR-FqamWHWtmhjezQvuy8ILPRWfZx7LVVNDwNk_UaUCr9nPFQFYkGsq-BkPlMM18Ni2GkueZ5YYY68VUSmKI0IVLOqDQqjLmBcWzYIdlbVSvzktCcF1obHhemVEi8la-E4bnPYhEXUeEPyGnXT1K3Keax0slS2qmmSCR2rMSOlbZjB-S9u-Gmya5y_6Ws3_GytlJbNgIr2b13HVp83NNxfg02ng_IUQeYbEf0WiJTQ7ew8NUfzx6R_c0wek326u-35g1Q0zp_ayXsF6Z_jqY |
| linkProvider | Taylor & Francis |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT-MwEB4h9gAc2OUlCuziAwcuKantPCxxqVZbdaGtkACJC7Ic2ymF0iKaSohfjycv8bxwju0k9oy_b6zxNwAHSoVWCKo8mgjmcetcKhFceamlmmkaUqXxvKM_CLuX_OQquFqA4-ouDKZVYgydFkIR-V6Nzo2H0YWHi_joVg9nZVaWoHjT90fgaD7WL2D-oN6HW2VpFYdYXuxwvRT2-WSAN5j0RrG0ylL8sFPn8NP5CdfVhxdZJ3fNeZY09fM7Tcfv_tkvWC15KWkXhrQGC3ayDiv9WtR1tg7LSEwLXecNiNvkzD0k7fFw-jjKbu6J-x3iWpPOfGYN6TlSPiXno2E-KgqXP43uMcDfhMvOv4u_Xa-swuBpF-xlHo1SP2E8UCbWjixqB-g-08xXLdMKNdc8MbalY1_F1JrUBkhZwtSqIOLW2YFlW7A4mU7sNpCEG60tj4xNFRI35StheeKzSEQmNH4DmtU6SF1KlGOljLHMQxURS5wfifMj8_lpwGHd4aFQ5_i6KXu9sDLLD0TSonqJZF_22srXvx4d4zOHEbwBu5VByNLrZxKRHtOKgp1vvWwflroX_Z7s_R-c7sIyrRNn9mAxe5zb347-ZMmf3MpfAN2F_rg |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LbxMxEB5FqYTSQ1tKI9JS8IEDlw0b2_uwOEW0UYA0igSVekCyvLY3BPJSs5EQvx7PvkSAXHJee3Ztz_ib8Y6_AXitVGiFoMqjiWAet86kEsGVl1qqmaYhVRrPO-7G4fCef3wIHhrwrroLg2mVGEOnBVFEvlejca9NWhi4iN9-19NNmZQlKF70PQrxfxje3_DH9TbcKyurOMDyYgfrJa_PfwTsQNIOYWmVpPjPRp2jz-AUvlbfXSSd_Ohus6Srf_1F6XjgwM7gpPRKSb9Qo6fQsMtzOL6rKV0359BCt7RgdX4GcZ9M3EPSn09Xj7Ps24K40RDXmgy2G2vIyLnkK_J5Ns2lIm35z9kCw_sLuB_cfnk_9MoaDJ52oV7m0Sj1E8YDZWLtXEXt4NxnmvmqZ3qh5ponxvZ07KuYWpPaAB2WMLUqiLh1WmBZG5rL1dI-B5Jwo7XlkbGpQrdN-UpYnvgsEpEJjd-BbrUMUpcE5VgnYy7zQEXEEudH4vzIfH468KbusC64OfY3ZX-uq8zy45C0qF0i2d5e7Xz5a-kYnTmE4B24qvRBlja_kYjzmFQUXB70slfwZHIzkKMP409X0KJ11swLaGaPW3vtfJ8seZnr-G_Gnv1c |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Path+Algorithm+for+the+Fused+Lasso+Signal+Approximator&rft.jtitle=Journal+of+computational+and+graphical+statistics&rft.au=Hoefling%2C+Holger&rft.date=2010-12-01&rft.pub=Taylor+%26+Francis+Ltd&rft.issn=1061-8600&rft.eissn=1537-2715&rft.volume=19&rft.issue=4&rft.spage=984&rft_id=info:doi/10.1198%2Fjcgs.2010.09208&rft.externalDBID=NO_FULL_TEXT&rft.externalDocID=2247845451 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1061-8600&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1061-8600&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1061-8600&client=summon |