Literature DB >> 9384619

Multiple time scales and the lifetime coefficient of variation: engineering applications.

K B Kordonsky1, I Gertsbakh.   

Abstract

We consider linear combinations of "natural" time scales and choose the "best" one which provides the minimum coefficient of variation of the lifetime. Our time scale is in fact a generalized Miner time scale because the latter is based on an appropriate weighting of the times spent on low and high level loadings. The suggested modus operandi for finding the "best" time scale has many features in common with the approach suggested by Farewell and Cox (1979) and Oakes (1995) which is devoted to multiple time scales in survival analysis.

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Year:  1997        PMID: 9384619     DOI: 10.1023/a:1009657101784

Source DB:  PubMed          Journal:  Lifetime Data Anal        ISSN: 1380-7870            Impact factor:   1.588


  2 in total

1.  Multiple time scales in survival analysis.

Authors:  D Oakes
Journal:  Lifetime Data Anal       Date:  1995       Impact factor: 1.588

2.  Methods for the estimation of failure distributions and rates from automobile warranty data.

Authors:  J Lawless; J Hu; J Cao
Journal:  Lifetime Data Anal       Date:  1995       Impact factor: 1.588

  2 in total
  9 in total

1.  Alternative time scales and failure time models.

Authors:  T Duchesne; J Lawless
Journal:  Lifetime Data Anal       Date:  2000-06       Impact factor: 1.588

2.  Semiparametric inference methods for general time scale models.

Authors:  Thierry Duchesne; Jerry Lawless
Journal:  Lifetime Data Anal       Date:  2002-09       Impact factor: 1.588

3.  Modeling the agreement of discrete bivariate survival times using kappa coefficient.

Authors:  Ying Guo; Amita K Manatunga
Journal:  Lifetime Data Anal       Date:  2005-09       Impact factor: 1.588

4.  Variability of waiting times for the 4 most prevalent cancer types in Ontario: a retrospective population-based analysis.

Authors:  Amir Rastpour; Mehmet A Begen; Alexander V Louie; Gregory S Zaric
Journal:  CMAJ Open       Date:  2018-06-07

5.  Measuring agreement of multivariate discrete survival times using a modified weighted kappa coefficient.

Authors:  Ying Guo; Amita K Manatunga
Journal:  Biometrics       Date:  2008-05-23       Impact factor: 2.571

6.  Proportional hazards and threshold regression: their theoretical and practical connections.

Authors:  Mei-Ling Ting Lee; G A Whitmore
Journal:  Lifetime Data Anal       Date:  2009-12-04       Impact factor: 1.588

7.  Models and estimation for systems with recurrent events and usage processes.

Authors:  Jerald F Lawless; Martin J Crowder
Journal:  Lifetime Data Anal       Date:  2010-03-11       Impact factor: 1.588

8.  Threshold regression for survival data with time-varying covariates.

Authors:  Mei-Ling Ting Lee; G A Whitmore; Bernard A Rosner
Journal:  Stat Med       Date:  2010-03-30       Impact factor: 2.373

9.  A case-control study relating railroad worker mortality to diesel exhaust exposure using a threshold regression model.

Authors:  Mei-Ling Ting Lee; G A Whitmore; Francine Laden; Jaime E Hart; Eric Garshick
Journal:  J Stat Plan Inference       Date:  2009       Impact factor: 1.111

  9 in total

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