Flexible parametric estimation technique for a competing risks model with unobserved heterogeneity: a Monte Carlo study

DOI10.1080/00949655.2014.926363zbMath1457.62374OpenAlexW2072177017MaRDI QIDQ5220858

Pål Børing

Publication date: 27 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2014.926363

zbMATH Keywords

unobserved heterogeneityduration analysisdiscrete duration timeflexible parametric estimationlocal mixture competing risks model

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Computational methods for problems pertaining to statistics (62-08)

Cites Work

• Unnamed Item
• Semi-Nonparametric Maximum Likelihood Estimation
• Time and causality: a Monte Carlo assessment of the timing-of-events approach
• The (mis)specification of discrete duration models with unobserved heterogeneity: a Monte Carlo study
• The geometry of mixture likelihoods: A general theory
• Semiparametric estimation of count regression models
• Duration dependence and nonparametric heterogeneity: A Monte Carlo study
• Estimating a semi-parametric duration model without specifying heterogeneity
• A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data
• Testing for Neglected Heterogeneity
• The identifiability of the competing risks model
• Econometric Methods for the Duration of Unemployment
• The Identifiability of the Mixed Proportional Hazards Competing Risks Model
• Semiparametric competing risks analysis
• The Singularity of the Information Matrix of the Mixed Proportional Hazard Model

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