Functional central limit theorems for the Nelson–Aalen and Kaplan–Meier estimators for dependent stationary data
We derive process limit distribution results for the Nelson–Aalen estimator of a hazard function and for the Kaplan–Meier estimator of a distribution function, under different dependence assumptions. The data are assumed to be right censored observations of a stationary time series. We treat weakly...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Anevski, Dragi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Umfang: |
9 |
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| Übergeordnetes Werk: |
Enthalten in: Third dose of COVID-19 mRNA vaccine appears to overcome vaccine hyporesponsiveness in patients with cirrhosis - John, Binu V. ELSEVIER, 2022, Amsterdam |
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| Übergeordnetes Werk: |
volume:124 ; year:2017 ; pages:83-91 ; extent:9 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.spl.2017.01.005 |
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ELV020015488 |
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| 520 | |a We derive process limit distribution results for the Nelson–Aalen estimator of a hazard function and for the Kaplan–Meier estimator of a distribution function, under different dependence assumptions. The data are assumed to be right censored observations of a stationary time series. We treat weakly dependent as well as long range dependent data, and allow for qualitative differences in the dependence for the censoring times versus the time of interest. | ||
| 650 | 7 | |a Survival analysis |2 Elsevier | |
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| 650 | 7 | |a Functional central limit theorem |2 Elsevier | |
| 650 | 7 | |a Kaplan–Meier estimator |2 Elsevier | |
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10.1016/j.spl.2017.01.005 doi GBVA2017004000009.pica (DE-627)ELV020015488 (ELSEVIER)S0167-7152(17)30012-3 DE-627 ger DE-627 rakwb eng 510 510 DE-600 610 VZ 44.87 bkl Anevski, Dragi verfasserin aut Functional central limit theorems for the Nelson–Aalen and Kaplan–Meier estimators for dependent stationary data 2017 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We derive process limit distribution results for the Nelson–Aalen estimator of a hazard function and for the Kaplan–Meier estimator of a distribution function, under different dependence assumptions. The data are assumed to be right censored observations of a stationary time series. We treat weakly dependent as well as long range dependent data, and allow for qualitative differences in the dependence for the censoring times versus the time of interest. Survival analysis Elsevier Limit distribution Elsevier Nelson–Aalen estimator Elsevier Functional central limit theorem Elsevier Kaplan–Meier estimator Elsevier Stationary process Elsevier Enthalten in Elsevier Science John, Binu V. ELSEVIER Third dose of COVID-19 mRNA vaccine appears to overcome vaccine hyporesponsiveness in patients with cirrhosis 2022 Amsterdam (DE-627)ELV008609586 volume:124 year:2017 pages:83-91 extent:9 https://doi.org/10.1016/j.spl.2017.01.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 124 2017 83-91 9 045F 510 |
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10.1016/j.spl.2017.01.005 doi GBVA2017004000009.pica (DE-627)ELV020015488 (ELSEVIER)S0167-7152(17)30012-3 DE-627 ger DE-627 rakwb eng 510 510 DE-600 610 VZ 44.87 bkl Anevski, Dragi verfasserin aut Functional central limit theorems for the Nelson–Aalen and Kaplan–Meier estimators for dependent stationary data 2017 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We derive process limit distribution results for the Nelson–Aalen estimator of a hazard function and for the Kaplan–Meier estimator of a distribution function, under different dependence assumptions. The data are assumed to be right censored observations of a stationary time series. We treat weakly dependent as well as long range dependent data, and allow for qualitative differences in the dependence for the censoring times versus the time of interest. Survival analysis Elsevier Limit distribution Elsevier Nelson–Aalen estimator Elsevier Functional central limit theorem Elsevier Kaplan–Meier estimator Elsevier Stationary process Elsevier Enthalten in Elsevier Science John, Binu V. ELSEVIER Third dose of COVID-19 mRNA vaccine appears to overcome vaccine hyporesponsiveness in patients with cirrhosis 2022 Amsterdam (DE-627)ELV008609586 volume:124 year:2017 pages:83-91 extent:9 https://doi.org/10.1016/j.spl.2017.01.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 124 2017 83-91 9 045F 510 |
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10.1016/j.spl.2017.01.005 doi GBVA2017004000009.pica (DE-627)ELV020015488 (ELSEVIER)S0167-7152(17)30012-3 DE-627 ger DE-627 rakwb eng 510 510 DE-600 610 VZ 44.87 bkl Anevski, Dragi verfasserin aut Functional central limit theorems for the Nelson–Aalen and Kaplan–Meier estimators for dependent stationary data 2017 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We derive process limit distribution results for the Nelson–Aalen estimator of a hazard function and for the Kaplan–Meier estimator of a distribution function, under different dependence assumptions. The data are assumed to be right censored observations of a stationary time series. We treat weakly dependent as well as long range dependent data, and allow for qualitative differences in the dependence for the censoring times versus the time of interest. Survival analysis Elsevier Limit distribution Elsevier Nelson–Aalen estimator Elsevier Functional central limit theorem Elsevier Kaplan–Meier estimator Elsevier Stationary process Elsevier Enthalten in Elsevier Science John, Binu V. ELSEVIER Third dose of COVID-19 mRNA vaccine appears to overcome vaccine hyporesponsiveness in patients with cirrhosis 2022 Amsterdam (DE-627)ELV008609586 volume:124 year:2017 pages:83-91 extent:9 https://doi.org/10.1016/j.spl.2017.01.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 124 2017 83-91 9 045F 510 |
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10.1016/j.spl.2017.01.005 doi GBVA2017004000009.pica (DE-627)ELV020015488 (ELSEVIER)S0167-7152(17)30012-3 DE-627 ger DE-627 rakwb eng 510 510 DE-600 610 VZ 44.87 bkl Anevski, Dragi verfasserin aut Functional central limit theorems for the Nelson–Aalen and Kaplan–Meier estimators for dependent stationary data 2017 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We derive process limit distribution results for the Nelson–Aalen estimator of a hazard function and for the Kaplan–Meier estimator of a distribution function, under different dependence assumptions. The data are assumed to be right censored observations of a stationary time series. We treat weakly dependent as well as long range dependent data, and allow for qualitative differences in the dependence for the censoring times versus the time of interest. Survival analysis Elsevier Limit distribution Elsevier Nelson–Aalen estimator Elsevier Functional central limit theorem Elsevier Kaplan–Meier estimator Elsevier Stationary process Elsevier Enthalten in Elsevier Science John, Binu V. ELSEVIER Third dose of COVID-19 mRNA vaccine appears to overcome vaccine hyporesponsiveness in patients with cirrhosis 2022 Amsterdam (DE-627)ELV008609586 volume:124 year:2017 pages:83-91 extent:9 https://doi.org/10.1016/j.spl.2017.01.005 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.87 Gastroenterologie VZ AR 124 2017 83-91 9 045F 510 |
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Functional central limit theorems for the Nelson–Aalen and Kaplan–Meier estimators for dependent stationary data |
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We derive process limit distribution results for the Nelson–Aalen estimator of a hazard function and for the Kaplan–Meier estimator of a distribution function, under different dependence assumptions. The data are assumed to be right censored observations of a stationary time series. We treat weakly dependent as well as long range dependent data, and allow for qualitative differences in the dependence for the censoring times versus the time of interest. |
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We derive process limit distribution results for the Nelson–Aalen estimator of a hazard function and for the Kaplan–Meier estimator of a distribution function, under different dependence assumptions. The data are assumed to be right censored observations of a stationary time series. We treat weakly dependent as well as long range dependent data, and allow for qualitative differences in the dependence for the censoring times versus the time of interest. |
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We derive process limit distribution results for the Nelson–Aalen estimator of a hazard function and for the Kaplan–Meier estimator of a distribution function, under different dependence assumptions. The data are assumed to be right censored observations of a stationary time series. We treat weakly dependent as well as long range dependent data, and allow for qualitative differences in the dependence for the censoring times versus the time of interest. |
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Functional central limit theorems for the Nelson–Aalen and Kaplan–Meier estimators for dependent stationary data |
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