Počet záznamů: 1
Comparison of Parametric and Semiparametric Survival Regression Models with Kernel Estimation
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SYSNO ASEP 0541888 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Comparison of Parametric and Semiparametric Survival Regression Models with Kernel Estimation Tvůrce(i) Selingerová, I. (CZ)
Katina, Stanislav (UIVT-O) SAI, ORCID, RID
Horová, I. (CZ)Celkový počet autorů 3 Zdroj.dok. Journal of Statistical Computation and Simulation. - : Taylor & Francis - ISSN 0094-9655
Roč. 91, č. 13 (2021), s. 2717-2739Poč.str. 23 s. Forma vydání Tištěná - P Jazyk dok. eng - angličtina Země vyd. GB - Velká Británie Klíč. slova Survival analysis ; hazard function ; Kernel estimation ; simulations ; Cox model Vědní obor RIV BB - Aplikovaná statistika, operační výzkum Obor OECD Statistics and probability Způsob publikování Open access Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000638231000001 EID SCOPUS 85104074971 DOI https://doi.org/10.1080/00949655.2021.1906875 Anotace The modelling of censored survival data is based on different estimations of the conditional hazard function. When survival time follows a known distribution, parametric models are useful. This strong assumption is replaced by a weaker in the case of semiparametric models. For instance, the frequently used model suggested by Cox is based on the proportionality of hazards. These models use non-parametric methods to estimate some baseline hazard and parametric methods to estimate the influence of a covariate. An alternative approach is to use smoothing that is more flexible. In this paper, two types of kernel smoothing and some bandwidth selection techniques are introduced. Application to real data shows different interpretations for each approach. The extensive simulation study is aimed at comparing different approaches and assessing their benefits. Kernel estimation is demonstrated to be very helpful for verifying assumptions of parametric or semiparametric models and is able to capture changes in the hazard function in both time and covariate directions. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2022 Elektronická adresa http://dx.doi.org/10.1080/00949655.2021.1906875
Počet záznamů: 1