Efficient estimation of regression coefficients and baseline hazard under proportionality of conditional hazards
From MaRDI portal
Publication:1972160
DOI10.1016/S0378-3758(99)00153-6zbMath1131.62323OpenAlexW2002338364WikidataQ126780503 ScholiaQ126780503MaRDI QIDQ1972160
Publication date: 23 March 2000
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0378-3758(99)00153-6
Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Estimation in survival analysis and censored data (62N02)
Related Items
Semiparametric censorship model with covariates, Model assisted Cox regression, Proportional hazards model for competing risks data with missing cause of failure, Additive hazards regression with censoring indicators missing at random, A kernel-assisted imputation estimating method for the additive hazards model with missing censoring indicator, A Bayesian adaptive design for two-stage clinical trials with survival data, Regression analysis of right-censored failure time data with missing censoring indicators
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Cox's regression model for counting processes: A large sample study
- Information and asymptotic efficiency in parametric-nonparametric models
- Maximum likelihood estimation of a survival function under the Koziol- Green proportional hazards model
- Estimating a survival function with incomplete cause-of-death data
- Ignorability and coarse data
- Efficient estimation from right-censored data when failure indicators are missing at random
- Coarsening at random in general sample spaces and random censoring in continuous time
- A survey of product-integration with a view toward application in survival analysis
- Estimating a Distribution Function Based on Nomination Sampling
- Inference and missing data
- Product‐limit Estimators and Cox Regression with Missing Censoring Information
- Statistical models based on counting processes