AVERAGE DERIVATIVES FOR HAZARD FUNCTIONS

DOI10.1017/S0266466604203012zbMath1061.62158MaRDI QIDQ4653556

Tue Gørgens

Publication date: 7 March 2005

Published in: Econometric Theory (Search for Journal in Brave)

zbMATH Keywords

index coefficient estimationvariace estimation

Mathematics Subject Classification ID

Density estimation (62G07) Monte Carlo methods (65C05) Estimation in survival analysis and censored data (62N02)

Related Items (11)

An additive Cox model for coronary heart disease study ⋮ Empirical likelihood for the class of single index hazard regression models ⋮ Randomly censored partially linear single-index models ⋮ New estimation and inference procedures for a single-index conditional distribution model ⋮ Semiparametric estimation for weighted average derivatives with responses missing at random ⋮ Partially varying coefficient single index proportional hazards regression models ⋮ Versatile estimation in censored single-index hazards regression ⋮ Asymptotic distributions of two ``synthetic data estimators for censored single-index models ⋮ Empirical likelihood for average derivatives of hazard regression functions ⋮ Semiparametric estimation of single‐index hazard functions without proportional hazards ⋮ Polynomial spline estimation of partially linear single-index proportional hazards regression models

Cites Work

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• An approach to nonparametric regression for life history data using local linear fitting
• Kernel estimation in a nonparametric marker dependent hazard model
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• Investigating Smooth Multiple Regression by the Method of Average Derivatives
• The Non-Parametric Identification of Generalized Accelerated Failure-Time Models
• A Generalized Moments Specification Test of the Proportional Hazards Model
• Partial likelihood
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• A Semiparametric Maximum Likelihood Estimator
• Semiparametric Estimation of a Proportional Hazard Model with Unobserved Heterogeneity
• Semiparametric Estimation of Index Coefficients
• Boundary and Bias Correction in Kernel Hazard Estimation
• The Limiting Distribution of the Maximum Rank Correlation Estimator

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