Optimizing the maximum reported cluster size for the multinomial-based spatial scan statistic
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
1 Online-Ressource.
- Sprache
-
Englisch
- Erschienen in
-
Optimizing the maximum reported cluster size for the multinomial-based spatial scan statistic ; volume:22 ; number:1 ; day:8 ; month:11 ; year:2023 ; pages:1-14 ; date:12.2023
International journal of health geographics ; 22, Heft 1 (8.11.2023), 1-14, 12.2023
- Klassifikation
-
Wirtschaft
- Urheber
-
Moon, Jisu
Kim, Minseok
Jung, Inkyung
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1186/s12942-023-00353-4
- URN
-
urn:nbn:de:101:1-2024012910182887974625
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 15.08.2025, 07:29 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Moon, Jisu
- Kim, Minseok
- Jung, Inkyung
- SpringerLink (Online service)
Ähnliche Objekte (12)
Optimizing the maximum reported cluster size in the spatial scan statistic for survival data
A flexibly shaped spatial scan statistic for detecting clusters
A log-Weibull spatial scan statistic for time to event data
Detecting multiple spatial disease clusters: information criterion and scan statistic approach
A Shared‐Frailty Spatial Scan Statistic Model for Time‐to‐Event Data
A genome-wide scan statistic framework for whole-genome sequence data analysis
UK Statistic
Asymptotic properties for the volume of excursion sets and for a scan statistic
Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters
Geographic prediction of tuberculosis clusters in Fukuoka, Japan, using the space-time scan statistic
Multinomial Restricted Unfolding
Quantitative phenotype scan statistic (QPSS) reveals rare variant associations with Alzheimer’s disease endophenotypes
Optimizing the maximum reported cluster size in the spatial scan statistic for survival data
A flexibly shaped spatial scan statistic for detecting clusters
A log-Weibull spatial scan statistic for time to event data
Detecting multiple spatial disease clusters: information criterion and scan statistic approach
A Shared‐Frailty Spatial Scan Statistic Model for Time‐to‐Event Data
A genome-wide scan statistic framework for whole-genome sequence data analysis
UK Statistic
Asymptotic properties for the volume of excursion sets and for a scan statistic
Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters
Geographic prediction of tuberculosis clusters in Fukuoka, Japan, using the space-time scan statistic
Multinomial Restricted Unfolding
Quantitative phenotype scan statistic (QPSS) reveals rare variant associations with Alzheimer’s disease endophenotypes
Optimizing the maximum reported cluster size in the spatial scan statistic for survival data
A flexibly shaped spatial scan statistic for detecting clusters
A log-Weibull spatial scan statistic for time to event data
Detecting multiple spatial disease clusters: information criterion and scan statistic approach
A Shared‐Frailty Spatial Scan Statistic Model for Time‐to‐Event Data
A genome-wide scan statistic framework for whole-genome sequence data analysis
UK Statistic
Asymptotic properties for the volume of excursion sets and for a scan statistic
Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters
Geographic prediction of tuberculosis clusters in Fukuoka, Japan, using the space-time scan statistic
Multinomial Restricted Unfolding