Semiparametric transformation models and the missing information principle

DOI10.1016/S0378-3758(02)00154-4zbMath1023.62102MaRDI QIDQ1395893

Sundarraman Subramanian

Publication date: 1 July 2003

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

zbMATH Keywords

square integrable martingalesCounting processesLenglart's inequalityDuhamel equationlocal Kaplan-Meier estimatorpredictable variation processes

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05) Nonparametric estimation (62G05) Censored data models (62N01) Estimation in survival analysis and censored data (62N02)

Related Items (1)

Semiparametric inference for transformation models via empirical likelihood

Cites Work

• Cox's regression model for counting processes: A large sample study
• Rank regression
• Regression analysis with randomly right-censored data
• Analysis of transformation models with censored data
• Regression analysis for long-term survival rate
• Linear regression with censored data
• Miscellanea. On the linear transformation model for censored data
• Predicting Survival Probabilities With Semiparametric Transformation Models
• Maximum Likelihood Estimation in the Proportional Odds Model
• Median regression and the missing information principle
• Survival Analysis with Median Regression Models
• Analysing Competing Risks Data With Transformation Models
• Statistical models based on counting processes
• Parameter estimation in regression for long-term survival rate from censored data
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