Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA)
Abstract Among the many tools suited to detect local clusters in group-level data, Kulldorff–Nagarwalla’s spatial scan statistic gained wide popularity (Kulldorff and Nagarwalla in Stat Med 14(8):799–810, 1995). The underlying assumptions needed for making statistical inference feasible are quite st...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bilancia, Massimo [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2013 |
|---|
| Schlagwörter: |
Kulldorff–Nagarwalla’s spatial scan statistic |
|---|
| Anmerkung: |
© Springer-Verlag Berlin Heidelberg 2013 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Statistical methods & applications - Springer Berlin Heidelberg, 2001, 23(2013), 1 vom: 02. Okt., Seite 71-94 |
|---|---|
| Übergeordnetes Werk: |
volume:23 ; year:2013 ; number:1 ; day:02 ; month:10 ; pages:71-94 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10260-013-0241-8 |
|---|
| Katalog-ID: |
OLC2071248430 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2071248430 | ||
| 003 | DE-627 | ||
| 005 | 20230516151607.0 | ||
| 007 | tu | ||
| 008 | 200820s2013 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10260-013-0241-8 |2 doi | |
| 035 | |a (DE-627)OLC2071248430 | ||
| 035 | |a (DE-He213)s10260-013-0241-8-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 510 |q VZ |
| 082 | 0 | 4 | |a 510 |q VZ |
| 084 | |a 24 |2 ssgn | ||
| 084 | |a 31.73$jMathematische Statistik |2 bkl | ||
| 100 | 1 | |a Bilancia, Massimo |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA) |
| 264 | 1 | |c 2013 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer-Verlag Berlin Heidelberg 2013 | ||
| 520 | |a Abstract Among the many tools suited to detect local clusters in group-level data, Kulldorff–Nagarwalla’s spatial scan statistic gained wide popularity (Kulldorff and Nagarwalla in Stat Med 14(8):799–810, 1995). The underlying assumptions needed for making statistical inference feasible are quite strong, as counts in spatial units are assumed to be independent Poisson distributed random variables. Unfortunately, outcomes in spatial units are often not independent of each other, and risk estimates of areas that are close to each other will tend to be positively correlated as they share a number of spatially varying characteristics. We therefore introduce a Bayesian model-based algorithm for cluster detection in the presence of spatially autocorrelated relative risks. Our approach has been made possible by the recent development of new numerical methods based on integrated nested Laplace approximation, by which we can directly compute very accurate approximations of posterior marginals within short computational time (Rue et al. in JRSS B 71(2):319–392, 2009). Simulated data and a case study show that the performance of our method is at least comparable to that of Kulldorff–Nagarwalla’s statistic. | ||
| 650 | 4 | |a Spatial clustering | |
| 650 | 4 | |a Kulldorff–Nagarwalla’s spatial scan statistic | |
| 650 | 4 | |a Bayesian statistics | |
| 650 | 4 | |a Model-based spatial scan statistic | |
| 650 | 4 | |a Integrated nested Laplace approximation (INLA) | |
| 700 | 1 | |a Demarinis, Giacomo |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Statistical methods & applications |d Springer Berlin Heidelberg, 2001 |g 23(2013), 1 vom: 02. Okt., Seite 71-94 |w (DE-627)350258929 |w (DE-600)2082218-2 |w (DE-576)09971888X |x 1618-2510 |7 nnns |
| 773 | 1 | 8 | |g volume:23 |g year:2013 |g number:1 |g day:02 |g month:10 |g pages:71-94 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s10260-013-0241-8 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OLC-WIW | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a SSG-OLC-DE-84 | ||
| 912 | |a SSG-OPC-BBI | ||
| 912 | |a SSG-OPC-MAT | ||
| 912 | |a GBV_ILN_26 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_267 | ||
| 912 | |a GBV_ILN_2018 | ||
| 936 | b | k | |a 31.73$jMathematische Statistik |q VZ |0 106418998 |0 (DE-625)106418998 |
| 951 | |a AR | ||
| 952 | |d 23 |j 2013 |e 1 |b 02 |c 10 |h 71-94 | ||
| author_variant |
m b mb g d gd |
|---|---|
| matchkey_str |
article:16182510:2013----::aeincnigfptadsaeaewtitgaenselp |
| hierarchy_sort_str |
2013 |
| bklnumber |
31.73$jMathematische Statistik |
| publishDate |
2013 |
| allfields |
10.1007/s10260-013-0241-8 doi (DE-627)OLC2071248430 (DE-He213)s10260-013-0241-8-p DE-627 ger DE-627 rakwb eng 510 VZ 510 VZ 24 ssgn 31.73$jMathematische Statistik bkl Bilancia, Massimo verfasserin aut Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA) 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Abstract Among the many tools suited to detect local clusters in group-level data, Kulldorff–Nagarwalla’s spatial scan statistic gained wide popularity (Kulldorff and Nagarwalla in Stat Med 14(8):799–810, 1995). The underlying assumptions needed for making statistical inference feasible are quite strong, as counts in spatial units are assumed to be independent Poisson distributed random variables. Unfortunately, outcomes in spatial units are often not independent of each other, and risk estimates of areas that are close to each other will tend to be positively correlated as they share a number of spatially varying characteristics. We therefore introduce a Bayesian model-based algorithm for cluster detection in the presence of spatially autocorrelated relative risks. Our approach has been made possible by the recent development of new numerical methods based on integrated nested Laplace approximation, by which we can directly compute very accurate approximations of posterior marginals within short computational time (Rue et al. in JRSS B 71(2):319–392, 2009). Simulated data and a case study show that the performance of our method is at least comparable to that of Kulldorff–Nagarwalla’s statistic. Spatial clustering Kulldorff–Nagarwalla’s spatial scan statistic Bayesian statistics Model-based spatial scan statistic Integrated nested Laplace approximation (INLA) Demarinis, Giacomo aut Enthalten in Statistical methods & applications Springer Berlin Heidelberg, 2001 23(2013), 1 vom: 02. Okt., Seite 71-94 (DE-627)350258929 (DE-600)2082218-2 (DE-576)09971888X 1618-2510 nnns volume:23 year:2013 number:1 day:02 month:10 pages:71-94 https://doi.org/10.1007/s10260-013-0241-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_26 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 31.73$jMathematische Statistik VZ 106418998 (DE-625)106418998 AR 23 2013 1 02 10 71-94 |
| spelling |
10.1007/s10260-013-0241-8 doi (DE-627)OLC2071248430 (DE-He213)s10260-013-0241-8-p DE-627 ger DE-627 rakwb eng 510 VZ 510 VZ 24 ssgn 31.73$jMathematische Statistik bkl Bilancia, Massimo verfasserin aut Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA) 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Abstract Among the many tools suited to detect local clusters in group-level data, Kulldorff–Nagarwalla’s spatial scan statistic gained wide popularity (Kulldorff and Nagarwalla in Stat Med 14(8):799–810, 1995). The underlying assumptions needed for making statistical inference feasible are quite strong, as counts in spatial units are assumed to be independent Poisson distributed random variables. Unfortunately, outcomes in spatial units are often not independent of each other, and risk estimates of areas that are close to each other will tend to be positively correlated as they share a number of spatially varying characteristics. We therefore introduce a Bayesian model-based algorithm for cluster detection in the presence of spatially autocorrelated relative risks. Our approach has been made possible by the recent development of new numerical methods based on integrated nested Laplace approximation, by which we can directly compute very accurate approximations of posterior marginals within short computational time (Rue et al. in JRSS B 71(2):319–392, 2009). Simulated data and a case study show that the performance of our method is at least comparable to that of Kulldorff–Nagarwalla’s statistic. Spatial clustering Kulldorff–Nagarwalla’s spatial scan statistic Bayesian statistics Model-based spatial scan statistic Integrated nested Laplace approximation (INLA) Demarinis, Giacomo aut Enthalten in Statistical methods & applications Springer Berlin Heidelberg, 2001 23(2013), 1 vom: 02. Okt., Seite 71-94 (DE-627)350258929 (DE-600)2082218-2 (DE-576)09971888X 1618-2510 nnns volume:23 year:2013 number:1 day:02 month:10 pages:71-94 https://doi.org/10.1007/s10260-013-0241-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_26 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 31.73$jMathematische Statistik VZ 106418998 (DE-625)106418998 AR 23 2013 1 02 10 71-94 |
| allfields_unstemmed |
10.1007/s10260-013-0241-8 doi (DE-627)OLC2071248430 (DE-He213)s10260-013-0241-8-p DE-627 ger DE-627 rakwb eng 510 VZ 510 VZ 24 ssgn 31.73$jMathematische Statistik bkl Bilancia, Massimo verfasserin aut Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA) 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Abstract Among the many tools suited to detect local clusters in group-level data, Kulldorff–Nagarwalla’s spatial scan statistic gained wide popularity (Kulldorff and Nagarwalla in Stat Med 14(8):799–810, 1995). The underlying assumptions needed for making statistical inference feasible are quite strong, as counts in spatial units are assumed to be independent Poisson distributed random variables. Unfortunately, outcomes in spatial units are often not independent of each other, and risk estimates of areas that are close to each other will tend to be positively correlated as they share a number of spatially varying characteristics. We therefore introduce a Bayesian model-based algorithm for cluster detection in the presence of spatially autocorrelated relative risks. Our approach has been made possible by the recent development of new numerical methods based on integrated nested Laplace approximation, by which we can directly compute very accurate approximations of posterior marginals within short computational time (Rue et al. in JRSS B 71(2):319–392, 2009). Simulated data and a case study show that the performance of our method is at least comparable to that of Kulldorff–Nagarwalla’s statistic. Spatial clustering Kulldorff–Nagarwalla’s spatial scan statistic Bayesian statistics Model-based spatial scan statistic Integrated nested Laplace approximation (INLA) Demarinis, Giacomo aut Enthalten in Statistical methods & applications Springer Berlin Heidelberg, 2001 23(2013), 1 vom: 02. Okt., Seite 71-94 (DE-627)350258929 (DE-600)2082218-2 (DE-576)09971888X 1618-2510 nnns volume:23 year:2013 number:1 day:02 month:10 pages:71-94 https://doi.org/10.1007/s10260-013-0241-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_26 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 31.73$jMathematische Statistik VZ 106418998 (DE-625)106418998 AR 23 2013 1 02 10 71-94 |
| allfieldsGer |
10.1007/s10260-013-0241-8 doi (DE-627)OLC2071248430 (DE-He213)s10260-013-0241-8-p DE-627 ger DE-627 rakwb eng 510 VZ 510 VZ 24 ssgn 31.73$jMathematische Statistik bkl Bilancia, Massimo verfasserin aut Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA) 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Abstract Among the many tools suited to detect local clusters in group-level data, Kulldorff–Nagarwalla’s spatial scan statistic gained wide popularity (Kulldorff and Nagarwalla in Stat Med 14(8):799–810, 1995). The underlying assumptions needed for making statistical inference feasible are quite strong, as counts in spatial units are assumed to be independent Poisson distributed random variables. Unfortunately, outcomes in spatial units are often not independent of each other, and risk estimates of areas that are close to each other will tend to be positively correlated as they share a number of spatially varying characteristics. We therefore introduce a Bayesian model-based algorithm for cluster detection in the presence of spatially autocorrelated relative risks. Our approach has been made possible by the recent development of new numerical methods based on integrated nested Laplace approximation, by which we can directly compute very accurate approximations of posterior marginals within short computational time (Rue et al. in JRSS B 71(2):319–392, 2009). Simulated data and a case study show that the performance of our method is at least comparable to that of Kulldorff–Nagarwalla’s statistic. Spatial clustering Kulldorff–Nagarwalla’s spatial scan statistic Bayesian statistics Model-based spatial scan statistic Integrated nested Laplace approximation (INLA) Demarinis, Giacomo aut Enthalten in Statistical methods & applications Springer Berlin Heidelberg, 2001 23(2013), 1 vom: 02. Okt., Seite 71-94 (DE-627)350258929 (DE-600)2082218-2 (DE-576)09971888X 1618-2510 nnns volume:23 year:2013 number:1 day:02 month:10 pages:71-94 https://doi.org/10.1007/s10260-013-0241-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_26 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 31.73$jMathematische Statistik VZ 106418998 (DE-625)106418998 AR 23 2013 1 02 10 71-94 |
| allfieldsSound |
10.1007/s10260-013-0241-8 doi (DE-627)OLC2071248430 (DE-He213)s10260-013-0241-8-p DE-627 ger DE-627 rakwb eng 510 VZ 510 VZ 24 ssgn 31.73$jMathematische Statistik bkl Bilancia, Massimo verfasserin aut Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA) 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag Berlin Heidelberg 2013 Abstract Among the many tools suited to detect local clusters in group-level data, Kulldorff–Nagarwalla’s spatial scan statistic gained wide popularity (Kulldorff and Nagarwalla in Stat Med 14(8):799–810, 1995). The underlying assumptions needed for making statistical inference feasible are quite strong, as counts in spatial units are assumed to be independent Poisson distributed random variables. Unfortunately, outcomes in spatial units are often not independent of each other, and risk estimates of areas that are close to each other will tend to be positively correlated as they share a number of spatially varying characteristics. We therefore introduce a Bayesian model-based algorithm for cluster detection in the presence of spatially autocorrelated relative risks. Our approach has been made possible by the recent development of new numerical methods based on integrated nested Laplace approximation, by which we can directly compute very accurate approximations of posterior marginals within short computational time (Rue et al. in JRSS B 71(2):319–392, 2009). Simulated data and a case study show that the performance of our method is at least comparable to that of Kulldorff–Nagarwalla’s statistic. Spatial clustering Kulldorff–Nagarwalla’s spatial scan statistic Bayesian statistics Model-based spatial scan statistic Integrated nested Laplace approximation (INLA) Demarinis, Giacomo aut Enthalten in Statistical methods & applications Springer Berlin Heidelberg, 2001 23(2013), 1 vom: 02. Okt., Seite 71-94 (DE-627)350258929 (DE-600)2082218-2 (DE-576)09971888X 1618-2510 nnns volume:23 year:2013 number:1 day:02 month:10 pages:71-94 https://doi.org/10.1007/s10260-013-0241-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_26 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 31.73$jMathematische Statistik VZ 106418998 (DE-625)106418998 AR 23 2013 1 02 10 71-94 |
| language |
English |
| source |
Enthalten in Statistical methods & applications 23(2013), 1 vom: 02. Okt., Seite 71-94 volume:23 year:2013 number:1 day:02 month:10 pages:71-94 |
| sourceStr |
Enthalten in Statistical methods & applications 23(2013), 1 vom: 02. Okt., Seite 71-94 volume:23 year:2013 number:1 day:02 month:10 pages:71-94 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Spatial clustering Kulldorff–Nagarwalla’s spatial scan statistic Bayesian statistics Model-based spatial scan statistic Integrated nested Laplace approximation (INLA) |
| dewey-raw |
510 |
| isfreeaccess_bool |
false |
| container_title |
Statistical methods & applications |
| authorswithroles_txt_mv |
Bilancia, Massimo @@aut@@ Demarinis, Giacomo @@aut@@ |
| publishDateDaySort_date |
2013-10-02T00:00:00Z |
| hierarchy_top_id |
350258929 |
| dewey-sort |
3510 |
| id |
OLC2071248430 |
| 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">OLC2071248430</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230516151607.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2013 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10260-013-0241-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2071248430</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10260-013-0241-8-p</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="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">24</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.73$jMathematische Statistik</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bilancia, Massimo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA)</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag Berlin Heidelberg 2013</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Among the many tools suited to detect local clusters in group-level data, Kulldorff–Nagarwalla’s spatial scan statistic gained wide popularity (Kulldorff and Nagarwalla in Stat Med 14(8):799–810, 1995). The underlying assumptions needed for making statistical inference feasible are quite strong, as counts in spatial units are assumed to be independent Poisson distributed random variables. Unfortunately, outcomes in spatial units are often not independent of each other, and risk estimates of areas that are close to each other will tend to be positively correlated as they share a number of spatially varying characteristics. We therefore introduce a Bayesian model-based algorithm for cluster detection in the presence of spatially autocorrelated relative risks. Our approach has been made possible by the recent development of new numerical methods based on integrated nested Laplace approximation, by which we can directly compute very accurate approximations of posterior marginals within short computational time (Rue et al. in JRSS B 71(2):319–392, 2009). Simulated data and a case study show that the performance of our method is at least comparable to that of Kulldorff–Nagarwalla’s statistic.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spatial clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Kulldorff–Nagarwalla’s spatial scan statistic</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayesian statistics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Model-based spatial scan statistic</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Integrated nested Laplace approximation (INLA)</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Demarinis, Giacomo</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Statistical methods & applications</subfield><subfield code="d">Springer Berlin Heidelberg, 2001</subfield><subfield code="g">23(2013), 1 vom: 02. Okt., Seite 71-94</subfield><subfield code="w">(DE-627)350258929</subfield><subfield code="w">(DE-600)2082218-2</subfield><subfield code="w">(DE-576)09971888X</subfield><subfield code="x">1618-2510</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2013</subfield><subfield code="g">number:1</subfield><subfield code="g">day:02</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:71-94</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10260-013-0241-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-DE-84</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-BBI</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_267</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">31.73$jMathematische Statistik</subfield><subfield code="q">VZ</subfield><subfield code="0">106418998</subfield><subfield code="0">(DE-625)106418998</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2013</subfield><subfield code="e">1</subfield><subfield code="b">02</subfield><subfield code="c">10</subfield><subfield code="h">71-94</subfield></datafield></record></collection>
|
| author |
Bilancia, Massimo |
| spellingShingle |
Bilancia, Massimo ddc 510 ssgn 24 bkl 31.73$jMathematische Statistik misc Spatial clustering misc Kulldorff–Nagarwalla’s spatial scan statistic misc Bayesian statistics misc Model-based spatial scan statistic misc Integrated nested Laplace approximation (INLA) Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA) |
| authorStr |
Bilancia, Massimo |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)350258929 |
| format |
Article |
| dewey-ones |
510 - Mathematics |
| delete_txt_mv |
keep |
| author_role |
aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1618-2510 |
| topic_title |
510 VZ 24 ssgn 31.73$jMathematische Statistik bkl Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA) Spatial clustering Kulldorff–Nagarwalla’s spatial scan statistic Bayesian statistics Model-based spatial scan statistic Integrated nested Laplace approximation (INLA) |
| topic |
ddc 510 ssgn 24 bkl 31.73$jMathematische Statistik misc Spatial clustering misc Kulldorff–Nagarwalla’s spatial scan statistic misc Bayesian statistics misc Model-based spatial scan statistic misc Integrated nested Laplace approximation (INLA) |
| topic_unstemmed |
ddc 510 ssgn 24 bkl 31.73$jMathematische Statistik misc Spatial clustering misc Kulldorff–Nagarwalla’s spatial scan statistic misc Bayesian statistics misc Model-based spatial scan statistic misc Integrated nested Laplace approximation (INLA) |
| topic_browse |
ddc 510 ssgn 24 bkl 31.73$jMathematische Statistik misc Spatial clustering misc Kulldorff–Nagarwalla’s spatial scan statistic misc Bayesian statistics misc Model-based spatial scan statistic misc Integrated nested Laplace approximation (INLA) |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Statistical methods & applications |
| hierarchy_parent_id |
350258929 |
| dewey-tens |
510 - Mathematics |
| hierarchy_top_title |
Statistical methods & applications |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)350258929 (DE-600)2082218-2 (DE-576)09971888X |
| title |
Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA) |
| ctrlnum |
(DE-627)OLC2071248430 (DE-He213)s10260-013-0241-8-p |
| title_full |
Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA) |
| author_sort |
Bilancia, Massimo |
| journal |
Statistical methods & applications |
| journalStr |
Statistical methods & applications |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2013 |
| contenttype_str_mv |
txt |
| container_start_page |
71 |
| author_browse |
Bilancia, Massimo Demarinis, Giacomo |
| container_volume |
23 |
| class |
510 VZ 24 ssgn 31.73$jMathematische Statistik bkl |
| format_se |
Aufsätze |
| author-letter |
Bilancia, Massimo |
| doi_str_mv |
10.1007/s10260-013-0241-8 |
| normlink |
106418998 |
| normlink_prefix_str_mv |
106418998 (DE-625)106418998 |
| dewey-full |
510 |
| title_sort |
bayesian scanning of spatial disease rates with integrated nested laplace approximation (inla) |
| title_auth |
Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA) |
| abstract |
Abstract Among the many tools suited to detect local clusters in group-level data, Kulldorff–Nagarwalla’s spatial scan statistic gained wide popularity (Kulldorff and Nagarwalla in Stat Med 14(8):799–810, 1995). The underlying assumptions needed for making statistical inference feasible are quite strong, as counts in spatial units are assumed to be independent Poisson distributed random variables. Unfortunately, outcomes in spatial units are often not independent of each other, and risk estimates of areas that are close to each other will tend to be positively correlated as they share a number of spatially varying characteristics. We therefore introduce a Bayesian model-based algorithm for cluster detection in the presence of spatially autocorrelated relative risks. Our approach has been made possible by the recent development of new numerical methods based on integrated nested Laplace approximation, by which we can directly compute very accurate approximations of posterior marginals within short computational time (Rue et al. in JRSS B 71(2):319–392, 2009). Simulated data and a case study show that the performance of our method is at least comparable to that of Kulldorff–Nagarwalla’s statistic. © Springer-Verlag Berlin Heidelberg 2013 |
| abstractGer |
Abstract Among the many tools suited to detect local clusters in group-level data, Kulldorff–Nagarwalla’s spatial scan statistic gained wide popularity (Kulldorff and Nagarwalla in Stat Med 14(8):799–810, 1995). The underlying assumptions needed for making statistical inference feasible are quite strong, as counts in spatial units are assumed to be independent Poisson distributed random variables. Unfortunately, outcomes in spatial units are often not independent of each other, and risk estimates of areas that are close to each other will tend to be positively correlated as they share a number of spatially varying characteristics. We therefore introduce a Bayesian model-based algorithm for cluster detection in the presence of spatially autocorrelated relative risks. Our approach has been made possible by the recent development of new numerical methods based on integrated nested Laplace approximation, by which we can directly compute very accurate approximations of posterior marginals within short computational time (Rue et al. in JRSS B 71(2):319–392, 2009). Simulated data and a case study show that the performance of our method is at least comparable to that of Kulldorff–Nagarwalla’s statistic. © Springer-Verlag Berlin Heidelberg 2013 |
| abstract_unstemmed |
Abstract Among the many tools suited to detect local clusters in group-level data, Kulldorff–Nagarwalla’s spatial scan statistic gained wide popularity (Kulldorff and Nagarwalla in Stat Med 14(8):799–810, 1995). The underlying assumptions needed for making statistical inference feasible are quite strong, as counts in spatial units are assumed to be independent Poisson distributed random variables. Unfortunately, outcomes in spatial units are often not independent of each other, and risk estimates of areas that are close to each other will tend to be positively correlated as they share a number of spatially varying characteristics. We therefore introduce a Bayesian model-based algorithm for cluster detection in the presence of spatially autocorrelated relative risks. Our approach has been made possible by the recent development of new numerical methods based on integrated nested Laplace approximation, by which we can directly compute very accurate approximations of posterior marginals within short computational time (Rue et al. in JRSS B 71(2):319–392, 2009). Simulated data and a case study show that the performance of our method is at least comparable to that of Kulldorff–Nagarwalla’s statistic. © Springer-Verlag Berlin Heidelberg 2013 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OLC-PHA SSG-OLC-DE-84 SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_26 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 |
| container_issue |
1 |
| title_short |
Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA) |
| url |
https://doi.org/10.1007/s10260-013-0241-8 |
| remote_bool |
false |
| author2 |
Demarinis, Giacomo |
| author2Str |
Demarinis, Giacomo |
| ppnlink |
350258929 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10260-013-0241-8 |
| up_date |
2024-07-04T03:15:44.150Z |
| _version_ |
1803616721741807616 |
| 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">OLC2071248430</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230516151607.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2013 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10260-013-0241-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2071248430</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10260-013-0241-8-p</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="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">510</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">24</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.73$jMathematische Statistik</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bilancia, Massimo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA)</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag Berlin Heidelberg 2013</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Among the many tools suited to detect local clusters in group-level data, Kulldorff–Nagarwalla’s spatial scan statistic gained wide popularity (Kulldorff and Nagarwalla in Stat Med 14(8):799–810, 1995). The underlying assumptions needed for making statistical inference feasible are quite strong, as counts in spatial units are assumed to be independent Poisson distributed random variables. Unfortunately, outcomes in spatial units are often not independent of each other, and risk estimates of areas that are close to each other will tend to be positively correlated as they share a number of spatially varying characteristics. We therefore introduce a Bayesian model-based algorithm for cluster detection in the presence of spatially autocorrelated relative risks. Our approach has been made possible by the recent development of new numerical methods based on integrated nested Laplace approximation, by which we can directly compute very accurate approximations of posterior marginals within short computational time (Rue et al. in JRSS B 71(2):319–392, 2009). Simulated data and a case study show that the performance of our method is at least comparable to that of Kulldorff–Nagarwalla’s statistic.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Spatial clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Kulldorff–Nagarwalla’s spatial scan statistic</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bayesian statistics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Model-based spatial scan statistic</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Integrated nested Laplace approximation (INLA)</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Demarinis, Giacomo</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Statistical methods & applications</subfield><subfield code="d">Springer Berlin Heidelberg, 2001</subfield><subfield code="g">23(2013), 1 vom: 02. Okt., Seite 71-94</subfield><subfield code="w">(DE-627)350258929</subfield><subfield code="w">(DE-600)2082218-2</subfield><subfield code="w">(DE-576)09971888X</subfield><subfield code="x">1618-2510</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:23</subfield><subfield code="g">year:2013</subfield><subfield code="g">number:1</subfield><subfield code="g">day:02</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:71-94</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10260-013-0241-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-DE-84</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-BBI</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_267</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">31.73$jMathematische Statistik</subfield><subfield code="q">VZ</subfield><subfield code="0">106418998</subfield><subfield code="0">(DE-625)106418998</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">23</subfield><subfield code="j">2013</subfield><subfield code="e">1</subfield><subfield code="b">02</subfield><subfield code="c">10</subfield><subfield code="h">71-94</subfield></datafield></record></collection>
|
| score |
7.398141 |