Hybrid Bayesian Estimation in the additive hazards model
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910969824804864 |
|---|---|
| author | Álvarez, Enrique Ernesto Riddick, Maximiliano Luis |
| author_facet | Álvarez, Enrique Ernesto Riddick, Maximiliano Luis |
| contents | Hereby we propose a Bayesian method of estimation for the semiparametric Additive Hazards Model (AHM) from Survival Analysis under right-censoring. With this aim, we review the AHM revisiting the likelihood function, so as to comment on the challenges posed by Bayesian estimation from the full likelihood. Through an algorithmic reformulation of that likelihood, we present an alternative method based on a hybrid Bayesian treatment that exploits Lin and Ying (1994) estimating equation approach and which chooses tractable priors for the parameters. We obtain the estimators from the posterior distributions in closed form, we perform a small simulation experiment, and lastly, we illustrate our method with the classical Nickels Miners dataset and a brief simulation experiment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20681 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Bayesian Estimation in the additive hazards model Álvarez, Enrique Ernesto Riddick, Maximiliano Luis Methodology Statistics Theory Hereby we propose a Bayesian method of estimation for the semiparametric Additive Hazards Model (AHM) from Survival Analysis under right-censoring. With this aim, we review the AHM revisiting the likelihood function, so as to comment on the challenges posed by Bayesian estimation from the full likelihood. Through an algorithmic reformulation of that likelihood, we present an alternative method based on a hybrid Bayesian treatment that exploits Lin and Ying (1994) estimating equation approach and which chooses tractable priors for the parameters. We obtain the estimators from the posterior distributions in closed form, we perform a small simulation experiment, and lastly, we illustrate our method with the classical Nickels Miners dataset and a brief simulation experiment. |
| title | Hybrid Bayesian Estimation in the additive hazards model |
| topic | Methodology Statistics Theory |
| url | https://arxiv.org/abs/2505.20681 |