Semiparametric multi-parameter regression survival modelling

Research output: Contribution to journalJournal articleResearchpeer-review

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Semiparametric multi-parameter regression survival modelling. / Burke, Kevin; Eriksson, Frank; Pipper, C. B.

In: Scandinavian Journal of Statistics, Vol. 47, No. 2, 2020, p. 555-571.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Burke, K, Eriksson, F & Pipper, CB 2020, 'Semiparametric multi-parameter regression survival modelling', Scandinavian Journal of Statistics, vol. 47, no. 2, pp. 555-571. https://doi.org/10.1111/sjos.12416

APA

Burke, K., Eriksson, F., & Pipper, C. B. (2020). Semiparametric multi-parameter regression survival modelling. Scandinavian Journal of Statistics, 47(2), 555-571. https://doi.org/10.1111/sjos.12416

Vancouver

Burke K, Eriksson F, Pipper CB. Semiparametric multi-parameter regression survival modelling. Scandinavian Journal of Statistics. 2020;47(2):555-571. https://doi.org/10.1111/sjos.12416

Author

Burke, Kevin ; Eriksson, Frank ; Pipper, C. B. / Semiparametric multi-parameter regression survival modelling. In: Scandinavian Journal of Statistics. 2020 ; Vol. 47, No. 2. pp. 555-571.

Bibtex

@article{ad80f06de5da455c84198f1e3d424ae0,
title = "Semiparametric multi-parameter regression survival modelling",
abstract = "We consider a log-linear model for survival data, where both the location and scale parameters depend on covariates and the baseline hazard function is completely unspecified. This model provides the flexibility needed to capture many interesting features of survival data at a relatively low cost in model complexity. Estimation procedures are developed and asymptotic properties of the resulting estimators are derived using empirical process theory. Finally, a resampling procedure is developed to estimate the limiting variances of the estimators. The finite sample properties of the estimators are investigated by way of a simulation study, and a practical application to lung cancer data is illustrated.",
keywords = "stat.ME, 62N01, 62N02, 62N03",
author = "Kevin Burke and Frank Eriksson and Pipper, {C. B.}",
year = "2020",
doi = "10.1111/sjos.12416",
language = "English",
volume = "47",
pages = "555--571",
journal = "Scandinavian Journal of Statistics",
issn = "0303-6898",
publisher = "Wiley-Blackwell",
number = "2",

}

RIS

TY - JOUR

T1 - Semiparametric multi-parameter regression survival modelling

AU - Burke, Kevin

AU - Eriksson, Frank

AU - Pipper, C. B.

PY - 2020

Y1 - 2020

N2 - We consider a log-linear model for survival data, where both the location and scale parameters depend on covariates and the baseline hazard function is completely unspecified. This model provides the flexibility needed to capture many interesting features of survival data at a relatively low cost in model complexity. Estimation procedures are developed and asymptotic properties of the resulting estimators are derived using empirical process theory. Finally, a resampling procedure is developed to estimate the limiting variances of the estimators. The finite sample properties of the estimators are investigated by way of a simulation study, and a practical application to lung cancer data is illustrated.

AB - We consider a log-linear model for survival data, where both the location and scale parameters depend on covariates and the baseline hazard function is completely unspecified. This model provides the flexibility needed to capture many interesting features of survival data at a relatively low cost in model complexity. Estimation procedures are developed and asymptotic properties of the resulting estimators are derived using empirical process theory. Finally, a resampling procedure is developed to estimate the limiting variances of the estimators. The finite sample properties of the estimators are investigated by way of a simulation study, and a practical application to lung cancer data is illustrated.

KW - stat.ME

KW - 62N01, 62N02, 62N03

U2 - 10.1111/sjos.12416

DO - 10.1111/sjos.12416

M3 - Journal article

VL - 47

SP - 555

EP - 571

JO - Scandinavian Journal of Statistics

JF - Scandinavian Journal of Statistics

SN - 0303-6898

IS - 2

ER -

ID: 212421262