invGauss

invGauss: Threshold regression that fits the (randomized drift) inverse Gaussian distribution to survival data. invGauss fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described in Aalen OO, Borgan O, Gjessing HK. Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based on describing time to event as the barrier hitting time of a Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution. The model allows covariates to influence starting values of the Wiener process and/or average drift towards a barrier, with a user-defined choice of link functions.


References in zbMATH (referenced in 52 articles , 1 standard article )

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  1. Rocha, Ricardo; Nadarajah, Saralees; Tomazella, Vera; Louzada, Francisco: Two new defective distributions based on the Marshall-Olkin extension (2016)
  2. Yang, Hanfang; Liu, Shen; Zhao, Yichuan: Jackknife empirical likelihood for linear transformation models with right censoring (2016)
  3. Aalen, Odd O.; Cook, Richard J.; Røysland, Kjetil: Does Cox analysis of a randomized survival study yield a causal treatment effect? (2015)
  4. Borgan, Ørnulf; Keogh, Ruth: Nested case-control studies: should one break the matching? (2015)
  5. Breslow, Norman E.; Hu, Jie; Wellner, Jon A.: Z-estimation and stratified samples: application to survival models (2015)
  6. Erich, Roger; Pennell, Michael L.: Ornstein-Uhlenbeck threshold regression for time-to-event data with and without a cure fraction (2015)
  7. Lawless, J.F.; Rad, N.Nazeri: Estimation and assessment of Markov multistate models with intermittent observations on individuals (2015)
  8. Li, Xiaohu; Fang, Rui: Ordering properties of order statistics from random variables of Archimedean copulas with applications (2015)
  9. Brinks, Ralph; Landwehr, Sandra: Age- and time-dependent model of the prevalence of non-communicable diseases and application to dementia in Germany (2014)
  10. Enki, Doyo G.; Noufaily, Angela; Farrington, C.Paddy: A time-varying shared frailty model with application to infectious diseases (2014)
  11. Farewell, Vernon T.; Tom, Brian D.M.: The versatility of multi-state models for the analysis of longitudinal data with unobservable features (2014)
  12. Baraldo, Stefano; Ieva, Francesca; Paganoni, Anna Maria; Vitelli, Valeria: Outcome prediction for heart failure telemonitoring via generalized linear models with functional covariates (2013)
  13. Bijwaard, Govert E.; Ridder, Geert; Woutersen, Tiemen: A simple GMM estimator for the semiparametric mixed proportional hazard model (2013)
  14. Commenges, Daniel; Hejblum, Boris P.: Evidence synthesis through a degradation model applied to myocardial infarction (2013)
  15. Cook, R.J.; Lawless, J.F.: Concepts and tests for trend in recurrent event processes (2013)
  16. Diao, Liqun; Cook, Richard J.; Lee, Ker-Ai: A copula model for marked point processes (2013)
  17. Ditlevsen, Susanne; Greenwood, Priscilla: The Morris-Lecar neuron model embeds a leaky integrate-and-fire model (2013)
  18. DuBois, Christopher; Butts, Carter T.; McFarland, Daniel; Smyth, Padhraic: Hierarchical models for relational event sequences (2013)
  19. Mandelbaum, Avishai; Zeltyn, Sergey: Data-stories about (im)patient customers in tele-queues (2013)
  20. Oakes, David: An introduction to survival models: in honor of Ross Prentice (2013)

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