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  • Wiley  (2)
  • 2005-2009  (2)
  • 2005  (2)
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  • Wiley  (2)
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  • 2005-2009  (2)
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  • 2005  (2)
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  • 1
    Online Resource
    Online Resource
    Bayesian proportional hazards model with time‐varying regression coefficients: a penalized Poisson regression approach
    Wiley ; 2005
    In:  Statistics in Medicine Vol. 24, No. 24 ( 2005-12-30), p. 3977-3989
    Details
    In: Statistics in Medicine, Wiley, Vol. 24, No. 24 ( 2005-12-30), p. 3977-3989
    Abstract: One can fruitfully approach survival problems without covariates in an actuarial way. In narrow time bins, the number of people at risk is counted together with the number of events. The relationship between time and probability of an event can then be estimated with a parametric or semi‐parametric model. The number of events observed in each bin is described using a Poisson distribution with the log mean specified using a flexible penalized B‐splines model with a large number of equidistant knots. Regression on pertinent covariates can easily be performed using the same log‐linear model, leading to the classical proportional hazard model. We propose to extend that model by allowing the regression coefficients to vary in a smooth way with time. Penalized B‐splines models will be proposed for each of these coefficients. We show how the regression parameters and the penalty weights can be estimated efficiently using Bayesian inference tools based on the Metropolis‐adjusted Langevin algorithm. Copyright © 2005 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    URL: Article
    DOI: 10.1002/sim.v24:24
    DOI: 10.1002/sim.2396
    RVK:
    XA 10000
    Language: English
    Publisher: Wiley
    Publication Date: 2005
    detail.hit.zdb_id: 1491221-1
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Unimodal smoothing
    Wiley ; 2005
    In:  Journal of Chemometrics Vol. 19, No. 5-7 ( 2005-05), p. 317-328
    Details
    In: Journal of Chemometrics, Wiley, Vol. 19, No. 5-7 ( 2005-05), p. 317-328
    Abstract: In many applications it is expected from theory that a signal should be non‐negative and unimodal, that is, show only one peak. If the data are noisy, standard smoothing algorithms will not always give the desired result: peaks may be rounded and negative lobes may occur in the tails. Positive unimodal fits can be obtained by modeling the logarithm of a curve and combining a standard roughness penalty with a specialized asymmetric penalty. The theoretical basis and implementation in Matlab are presented, as well as performance on real and simulated data. Copyright © 2006 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 0886-9383 , 1099-128X
    URL: Issue
    URL: Article
    DOI: 10.1002/cem.v19:5/7
    DOI: 10.1002/cem.935
    Language: English
    Publisher: Wiley
    Publication Date: 2005
    detail.hit.zdb_id: 2001613-X
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