Analysis of Failure Time Using Threshold Regression with Semi-Parametric Varying Coefficients

Details

• Personal Author:
  Lee M-LT ; Li J
• Description:
  Many new statistical models may enjoy better interpretability and numerical stability than traditional models in survival data analysis. Specifically, the threshold regression (TR) technique based on the inverse Gaussian distribution is a useful alternative to the Cox proportional hazards model to analyse lifetime data. In this article we consider a semi-parametric modelling approach for TR and contribute implementational and theoretical details for model fitting and statistical inferences. Extensive simulations are carried out to examine the finite sample performance of the parametric and non-parametric estimates. A real example is analysed to illustrate our methods, along with a careful diagnosis of model assumptions. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety
• Keywords:
  Author Keywords: Threshold Regression Biostatistics Bootstrap Inverse Gaussian Distribution Mathematical Models Models Risk Assessment Statistical Analysis Varying Coefficients Model Wiener Process
• ISSN:
  0039-0402
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Maryland ; OSHA Region 3
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  164-182
• Volume:
  65
• Issue:
  2
• NIOSHTIC Number:
  nn:20058658
• Citation:
  Stat Neerl 2011 May; 65(2):164-182
• Email:
  stalj@nus.edu.sg
• Federal Fiscal Year:
  2011
• Performing Organization:
  University of Maryland - College Park
• Peer Reviewed:
  True
• Start Date:
  20060901
• Source Full Name:
  Statistica Neerlandica
• End Date:
  20090831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:fa4c84005d422f1d1149c7f4cc8da4fd8a5c116efefa2b669c6380165e739455fcd75e4782c534b938bbbb2a5c3a1590d9edecac099af15b7d8f6dd6fe9d81f1
• Download URL:
  https://stacks.cdc.gov/view/cdc/221698/cdc_221698_DS1.pdf
• File Type:
  [PDF - 419.10 KB ]