A Doubly Robust Censoring Unbiased Transformation
We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
The Berkeley Electronic Press ; 2007 |
|---|
| Schlagwörter: |
censoring unbiased transformations |
|---|
| Reproduktion: |
Berkeley Electronic Press Academic Journals |
|---|---|
| Übergeordnetes Werk: |
In: The international journal of biostatistics - Berkeley, Calif. : BePress, 2005, 3(2007), 1, Seite 4 |
| Übergeordnetes Werk: |
volume:3 ; year:2007 ; number:1 ; pages:4 |
| Links: |
|---|
| Katalog-ID: |
NLEJ219555508 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLEJ219555508 | ||
| 003 | DE-627 | ||
| 005 | 20210707085713.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 090716s2007 xxu|||||o 00| ||eng c | ||
| 035 | |a (DE-627)NLEJ219555508 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 044 | |c XD-US | ||
| 245 | 1 | 0 | |a A Doubly Robust Censoring Unbiased Transformation |
| 264 | 1 | |b The Berkeley Electronic Press |c 2007 | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation with a beneficial double robustness property, in that it can be used for nonparametric regression if either of these two conditional distributions are estimated accurately. Advantages of using this transformation for smoothing are illustrated in simulations and on the Stanford heart transplant data. | ||
| 533 | |f Berkeley Electronic Press Academic Journals | ||
| 650 | 4 | |a survival analysis | |
| 650 | 4 | |a censoring unbiased transformations | |
| 650 | 4 | |a nonparametric regression | |
| 650 | 4 | |a imputation | |
| 650 | 4 | |a Statistical Theory and Methods | |
| 650 | 4 | |a Survival Analysis | |
| 700 | 1 | |a Rubin, Daniel |4 oth | |
| 700 | 1 | |a van der Laan, Mark J. |4 oth | |
| 773 | 0 | 8 | |i In |t The international journal of biostatistics |d Berkeley, Calif. : BePress, 2005 |g 3(2007), 1, Seite 4 |h Online-Ressource |w (DE-627)NLEJ219537038 |w (DE-600)2239443-6 |x 1557-4679 |7 nnns |
| 773 | 1 | 8 | |g volume:3 |g year:2007 |g number:1 |g pages:4 |
| 856 | 4 | 0 | |u http://www.bepress.com/ijb/vol3/iss1/4 |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a ZDB-1-BEP | ||
| 912 | |a GBV_NL_ARTICLE | ||
| 951 | |a AR | ||
| 952 | |d 3 |j 2007 |e 1 |h 4 | ||
| matchkey_str |
article:15574679:2007----::dulrbscnoignisdr |
|---|---|
| hierarchy_sort_str |
2007 |
| publishDate |
2007 |
| allfields |
(DE-627)NLEJ219555508 DE-627 ger DE-627 rakwb eng XD-US A Doubly Robust Censoring Unbiased Transformation The Berkeley Electronic Press 2007 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation with a beneficial double robustness property, in that it can be used for nonparametric regression if either of these two conditional distributions are estimated accurately. Advantages of using this transformation for smoothing are illustrated in simulations and on the Stanford heart transplant data. Berkeley Electronic Press Academic Journals survival analysis censoring unbiased transformations nonparametric regression imputation Statistical Theory and Methods Survival Analysis Rubin, Daniel oth van der Laan, Mark J. oth In The international journal of biostatistics Berkeley, Calif. : BePress, 2005 3(2007), 1, Seite 4 Online-Ressource (DE-627)NLEJ219537038 (DE-600)2239443-6 1557-4679 nnns volume:3 year:2007 number:1 pages:4 http://www.bepress.com/ijb/vol3/iss1/4 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 3 2007 1 4 |
| spelling |
(DE-627)NLEJ219555508 DE-627 ger DE-627 rakwb eng XD-US A Doubly Robust Censoring Unbiased Transformation The Berkeley Electronic Press 2007 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation with a beneficial double robustness property, in that it can be used for nonparametric regression if either of these two conditional distributions are estimated accurately. Advantages of using this transformation for smoothing are illustrated in simulations and on the Stanford heart transplant data. Berkeley Electronic Press Academic Journals survival analysis censoring unbiased transformations nonparametric regression imputation Statistical Theory and Methods Survival Analysis Rubin, Daniel oth van der Laan, Mark J. oth In The international journal of biostatistics Berkeley, Calif. : BePress, 2005 3(2007), 1, Seite 4 Online-Ressource (DE-627)NLEJ219537038 (DE-600)2239443-6 1557-4679 nnns volume:3 year:2007 number:1 pages:4 http://www.bepress.com/ijb/vol3/iss1/4 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 3 2007 1 4 |
| allfields_unstemmed |
(DE-627)NLEJ219555508 DE-627 ger DE-627 rakwb eng XD-US A Doubly Robust Censoring Unbiased Transformation The Berkeley Electronic Press 2007 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation with a beneficial double robustness property, in that it can be used for nonparametric regression if either of these two conditional distributions are estimated accurately. Advantages of using this transformation for smoothing are illustrated in simulations and on the Stanford heart transplant data. Berkeley Electronic Press Academic Journals survival analysis censoring unbiased transformations nonparametric regression imputation Statistical Theory and Methods Survival Analysis Rubin, Daniel oth van der Laan, Mark J. oth In The international journal of biostatistics Berkeley, Calif. : BePress, 2005 3(2007), 1, Seite 4 Online-Ressource (DE-627)NLEJ219537038 (DE-600)2239443-6 1557-4679 nnns volume:3 year:2007 number:1 pages:4 http://www.bepress.com/ijb/vol3/iss1/4 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 3 2007 1 4 |
| allfieldsGer |
(DE-627)NLEJ219555508 DE-627 ger DE-627 rakwb eng XD-US A Doubly Robust Censoring Unbiased Transformation The Berkeley Electronic Press 2007 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation with a beneficial double robustness property, in that it can be used for nonparametric regression if either of these two conditional distributions are estimated accurately. Advantages of using this transformation for smoothing are illustrated in simulations and on the Stanford heart transplant data. Berkeley Electronic Press Academic Journals survival analysis censoring unbiased transformations nonparametric regression imputation Statistical Theory and Methods Survival Analysis Rubin, Daniel oth van der Laan, Mark J. oth In The international journal of biostatistics Berkeley, Calif. : BePress, 2005 3(2007), 1, Seite 4 Online-Ressource (DE-627)NLEJ219537038 (DE-600)2239443-6 1557-4679 nnns volume:3 year:2007 number:1 pages:4 http://www.bepress.com/ijb/vol3/iss1/4 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 3 2007 1 4 |
| allfieldsSound |
(DE-627)NLEJ219555508 DE-627 ger DE-627 rakwb eng XD-US A Doubly Robust Censoring Unbiased Transformation The Berkeley Electronic Press 2007 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation with a beneficial double robustness property, in that it can be used for nonparametric regression if either of these two conditional distributions are estimated accurately. Advantages of using this transformation for smoothing are illustrated in simulations and on the Stanford heart transplant data. Berkeley Electronic Press Academic Journals survival analysis censoring unbiased transformations nonparametric regression imputation Statistical Theory and Methods Survival Analysis Rubin, Daniel oth van der Laan, Mark J. oth In The international journal of biostatistics Berkeley, Calif. : BePress, 2005 3(2007), 1, Seite 4 Online-Ressource (DE-627)NLEJ219537038 (DE-600)2239443-6 1557-4679 nnns volume:3 year:2007 number:1 pages:4 http://www.bepress.com/ijb/vol3/iss1/4 GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE AR 3 2007 1 4 |
| language |
English |
| source |
In The international journal of biostatistics 3(2007), 1, Seite 4 volume:3 year:2007 number:1 pages:4 |
| sourceStr |
In The international journal of biostatistics 3(2007), 1, Seite 4 volume:3 year:2007 number:1 pages:4 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
survival analysis censoring unbiased transformations nonparametric regression imputation Statistical Theory and Methods Survival Analysis |
| isfreeaccess_bool |
false |
| container_title |
The international journal of biostatistics |
| authorswithroles_txt_mv |
Rubin, Daniel @@oth@@ van der Laan, Mark J. @@oth@@ |
| publishDateDaySort_date |
2007-01-01T00:00:00Z |
| hierarchy_top_id |
NLEJ219537038 |
| id |
NLEJ219555508 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ219555508</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210707085713.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">090716s2007 xxu|||||o 00| ||eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ219555508</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="c">XD-US</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A Doubly Robust Censoring Unbiased Transformation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="b">The Berkeley Electronic Press</subfield><subfield code="c">2007</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation with a beneficial double robustness property, in that it can be used for nonparametric regression if either of these two conditional distributions are estimated accurately. Advantages of using this transformation for smoothing are illustrated in simulations and on the Stanford heart transplant data.</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="f">Berkeley Electronic Press Academic Journals</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">survival analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">censoring unbiased transformations</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">nonparametric regression</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">imputation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistical Theory and Methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Survival Analysis</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rubin, Daniel</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">van der Laan, Mark J.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">The international journal of biostatistics</subfield><subfield code="d">Berkeley, Calif. : BePress, 2005</subfield><subfield code="g">3(2007), 1, Seite 4</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ219537038</subfield><subfield code="w">(DE-600)2239443-6</subfield><subfield code="x">1557-4679</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:3</subfield><subfield code="g">year:2007</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:4</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.bepress.com/ijb/vol3/iss1/4</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-BEP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">3</subfield><subfield code="j">2007</subfield><subfield code="e">1</subfield><subfield code="h">4</subfield></datafield></record></collection>
|
| series2 |
Berkeley Electronic Press Academic Journals |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)NLEJ219537038 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| collection |
NL |
| remote_str |
true |
| illustrated |
Not Illustrated |
| issn |
1557-4679 |
| topic_title |
A Doubly Robust Censoring Unbiased Transformation survival analysis censoring unbiased transformations nonparametric regression imputation Statistical Theory and Methods Survival Analysis |
| publisher |
The Berkeley Electronic Press |
| publisherStr |
The Berkeley Electronic Press |
| topic |
misc survival analysis misc censoring unbiased transformations misc nonparametric regression misc imputation misc Statistical Theory and Methods misc Survival Analysis |
| spellingShingle |
misc survival analysis misc censoring unbiased transformations misc nonparametric regression misc imputation misc Statistical Theory and Methods misc Survival Analysis A Doubly Robust Censoring Unbiased Transformation |
| topic_unstemmed |
misc survival analysis misc censoring unbiased transformations misc nonparametric regression misc imputation misc Statistical Theory and Methods misc Survival Analysis |
| topic_browse |
misc survival analysis misc censoring unbiased transformations misc nonparametric regression misc imputation misc Statistical Theory and Methods misc Survival Analysis |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
d r dr d l m j v dlmj dlmjv |
| hierarchy_parent_title |
The international journal of biostatistics |
| hierarchy_parent_id |
NLEJ219537038 |
| hierarchy_top_title |
The international journal of biostatistics |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)NLEJ219537038 (DE-600)2239443-6 |
| title |
A Doubly Robust Censoring Unbiased Transformation |
| ctrlnum |
(DE-627)NLEJ219555508 |
| title_full |
A Doubly Robust Censoring Unbiased Transformation |
| journal |
The international journal of biostatistics |
| journalStr |
The international journal of biostatistics |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2007 |
| contenttype_str_mv |
zzz |
| container_start_page |
4 |
| container_volume |
3 |
| format_se |
Elektronische Aufsätze |
| countryofpublication_str_mv |
XD-US |
| title_sort |
a doubly robust censoring unbiased transformation |
| title_auth |
A Doubly Robust Censoring Unbiased Transformation |
| abstract |
We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation with a beneficial double robustness property, in that it can be used for nonparametric regression if either of these two conditional distributions are estimated accurately. Advantages of using this transformation for smoothing are illustrated in simulations and on the Stanford heart transplant data. |
| abstractGer |
We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation with a beneficial double robustness property, in that it can be used for nonparametric regression if either of these two conditional distributions are estimated accurately. Advantages of using this transformation for smoothing are illustrated in simulations and on the Stanford heart transplant data. |
| abstract_unstemmed |
We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation with a beneficial double robustness property, in that it can be used for nonparametric regression if either of these two conditional distributions are estimated accurately. Advantages of using this transformation for smoothing are illustrated in simulations and on the Stanford heart transplant data. |
| collection_details |
GBV_USEFLAG_U ZDB-1-BEP GBV_NL_ARTICLE |
| container_issue |
1 |
| title_short |
A Doubly Robust Censoring Unbiased Transformation |
| url |
http://www.bepress.com/ijb/vol3/iss1/4 |
| remote_bool |
true |
| author2 |
Rubin, Daniel van der Laan, Mark J. |
| author2Str |
Rubin, Daniel van der Laan, Mark J. |
| ppnlink |
NLEJ219537038 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| up_date |
2024-07-06T05:29:04.242Z |
| _version_ |
1803806304363347968 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ219555508</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20210707085713.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">090716s2007 xxu|||||o 00| ||eng c</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ219555508</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="c">XD-US</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A Doubly Robust Censoring Unbiased Transformation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="b">The Berkeley Electronic Press</subfield><subfield code="c">2007</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">We consider random design nonparametric regression when the response variable is subject to right censoring. Following the work of Fan and Gijbels (1994), a common approach to this problem is to apply what has been termed a censoring unbiased transformation to the data to obtain surrogate responses, and then enter these surrogate responses with covariate data into standard smoothing algorithms. Existing censoring unbiased transformations generally depend on either the conditional survival function of the response of interest, or that of the censoring variable. We show that a mapping introduced in another statistical context is in fact a censoring unbiased transformation with a beneficial double robustness property, in that it can be used for nonparametric regression if either of these two conditional distributions are estimated accurately. Advantages of using this transformation for smoothing are illustrated in simulations and on the Stanford heart transplant data.</subfield></datafield><datafield tag="533" ind1=" " ind2=" "><subfield code="f">Berkeley Electronic Press Academic Journals</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">survival analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">censoring unbiased transformations</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">nonparametric regression</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">imputation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistical Theory and Methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Survival Analysis</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rubin, Daniel</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">van der Laan, Mark J.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">The international journal of biostatistics</subfield><subfield code="d">Berkeley, Calif. : BePress, 2005</subfield><subfield code="g">3(2007), 1, Seite 4</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ219537038</subfield><subfield code="w">(DE-600)2239443-6</subfield><subfield code="x">1557-4679</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:3</subfield><subfield code="g">year:2007</subfield><subfield code="g">number:1</subfield><subfield code="g">pages:4</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://www.bepress.com/ijb/vol3/iss1/4</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-BEP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">3</subfield><subfield code="j">2007</subfield><subfield code="e">1</subfield><subfield code="h">4</subfield></datafield></record></collection>
|
| score |
7.3980455 |