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Modeling disease dynamics and survivor functions by sanogenesis curves. / Bart, A. G.; Bart, V. A.; Steland, A.; Zaslavskiy, M. L.

In: Journal of Statistical Planning and Inference, Vol. 132, No. 1-2 SPEC. ISS., 01.06.2005, p. 33-51.

Research output: Contribution to journal › Article › peer-review

Harvard

Bart, AG, Bart, VA, Steland, A & Zaslavskiy, ML 2005, 'Modeling disease dynamics and survivor functions by sanogenesis curves', Journal of Statistical Planning and Inference, vol. 132, no. 1-2 SPEC. ISS., pp. 33-51. https://doi.org/10.1016/j.jspi.2004.06.014

APA

Bart, A. G., Bart, V. A., Steland, A., & Zaslavskiy, M. L. (2005). Modeling disease dynamics and survivor functions by sanogenesis curves. Journal of Statistical Planning and Inference, 132(1-2 SPEC. ISS.), 33-51. https://doi.org/10.1016/j.jspi.2004.06.014

Vancouver

Bart AG, Bart VA, Steland A, Zaslavskiy ML. Modeling disease dynamics and survivor functions by sanogenesis curves. Journal of Statistical Planning and Inference. 2005 Jun 1;132(1-2 SPEC. ISS.):33-51. https://doi.org/10.1016/j.jspi.2004.06.014

Author

Bart, A. G. ; Bart, V. A. ; Steland, A. ; Zaslavskiy, M. L. / Modeling disease dynamics and survivor functions by sanogenesis curves. In: Journal of Statistical Planning and Inference. 2005 ; Vol. 132, No. 1-2 SPEC. ISS. pp. 33-51.

BibTeX

@article{ab2312099b864226917209a18468fe3b,
title = "Modeling disease dynamics and survivor functions by sanogenesis curves",
abstract = "We propose to analyse the development of a disease by sanogenesis curves which model the result of interacting exitatory and inhibitory factors on a disease. Assuming that high-dimensional data describing the disease course are driven by a latent complex-valued Gaussian process with Markovian structure, we can identify the sanogenesis curve as the real part of the covariance function of the latent process. By applying techniques of stochastic process theory and partially inverse functions theory this finding allows to estimate the model parameters. In addition, the sanogensis curve also suggests a new model for survival times, where failures (deads) are only observed during critical time periods (crises) defined by the sanogenesis curve. We illustrate our approach by analyzing two real data sets from medicine.",
keywords = "Confidence regions, Correlation function, Critical and latent time points, Factor analysis, Partially inverse function, Sanogenesis curve, Survivor function",
author = "Bart, {A. G.} and Bart, {V. A.} and A. Steland and Zaslavskiy, {M. L.}",
year = "2005",
month = jun,
day = "1",
doi = "10.1016/j.jspi.2004.06.014",
language = "English",
volume = "132",
pages = "33--51",
journal = "Journal of Statistical Planning and Inference",
issn = "0378-3758",
publisher = "Elsevier",
number = "1-2 SPEC. ISS.",

}

RIS

TY - JOUR

T1 - Modeling disease dynamics and survivor functions by sanogenesis curves

AU - Bart, A. G.

AU - Bart, V. A.

AU - Steland, A.

AU - Zaslavskiy, M. L.

PY - 2005/6/1

Y1 - 2005/6/1

N2 - We propose to analyse the development of a disease by sanogenesis curves which model the result of interacting exitatory and inhibitory factors on a disease. Assuming that high-dimensional data describing the disease course are driven by a latent complex-valued Gaussian process with Markovian structure, we can identify the sanogenesis curve as the real part of the covariance function of the latent process. By applying techniques of stochastic process theory and partially inverse functions theory this finding allows to estimate the model parameters. In addition, the sanogensis curve also suggests a new model for survival times, where failures (deads) are only observed during critical time periods (crises) defined by the sanogenesis curve. We illustrate our approach by analyzing two real data sets from medicine.

AB - We propose to analyse the development of a disease by sanogenesis curves which model the result of interacting exitatory and inhibitory factors on a disease. Assuming that high-dimensional data describing the disease course are driven by a latent complex-valued Gaussian process with Markovian structure, we can identify the sanogenesis curve as the real part of the covariance function of the latent process. By applying techniques of stochastic process theory and partially inverse functions theory this finding allows to estimate the model parameters. In addition, the sanogensis curve also suggests a new model for survival times, where failures (deads) are only observed during critical time periods (crises) defined by the sanogenesis curve. We illustrate our approach by analyzing two real data sets from medicine.

KW - Confidence regions

KW - Correlation function

KW - Critical and latent time points

KW - Factor analysis

KW - Partially inverse function

KW - Sanogenesis curve

KW - Survivor function

UR - http://www.scopus.com/inward/record.url?scp=15844366564&partnerID=8YFLogxK

U2 - 10.1016/j.jspi.2004.06.014

DO - 10.1016/j.jspi.2004.06.014

M3 - Article

AN - SCOPUS:15844366564

VL - 132

SP - 33

EP - 51

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

SN - 0378-3758

IS - 1-2 SPEC. ISS.

ER -

ID: 61819181