1,721,015 research outputs found
Stochastic Modelling of the Onset of Bronchiolitis Obliterans Syndrome Following Lung Transplantation: An Analysis of Risk Factors
No full textModelling predation in functional response
No full textFunctional response is important in understanding the dynamics of predator-prey systems-it is essentially the interpretation of a bio-assay system in which individual predators have access to fixed numbers of prey for a given period of time. The classical approach to the problem has entailed the use of mechanistic models to interpret the data, but more recently several papers have argued that the use of simple logistic regression is both more consistent with the nature of the data and allows for the stochastic variation inherent in the system. Nevertheless, both the classical approach and this newer interpretation focus only on the modelling of means, and ignore the variability of the data. Another overlooked difficulty is that many published data sets display over-dispersion which itself may be a function of prey density In this paper we present some models which, as well as modelling the mean response, also account for the over-dispersion. The beta-binomial is a common model for admitting extra-variation, and here we develop some variants that allow a dependency on prey density. We also develop some new models based on stochastic counting processes. These models are compared and contrasted on a strict likelihood basis. It is found that beta-binomial models provide a markedly better fit to the data than do simple binomial models. The best-fitting counting process model is almost as good (in likelihood terms) as the best-fitting beta-binomial model. We argue that the counting process models offer richer insights into the predation process than do the other more 'descriptive' models. (c) 2006 Elsevier B.V. All rights reserved
Markov Chain Modelling for Geriatric Patient Care
No full textSummary
Objectives:
To show that Markov chain modelling can be applied to data on geriatric patients and use these models to assess the effects of covariates.
Methods:
Phase-type distributions were fitted by maximum likelihood to data on times spent by the patients in hospital and in community-based care. Data on the different events that ended the patients’ periods of care were used to estimate the dependence of the probabilities of these events on the phase from which the time in care ended. The age of the patients at admission to care and the year of admission were also included as covariates.
Results:
Differential effects of these covariates were shown on the various parameters of the fitted model, and interpretations of these effects made.
Conclusions:
Models based on phase-type distributions were appropriate for describing times spent in care, as the ordered phases had an interpretable structure corresponding to increasing amounts of care being given.</jats:p
Analysis of the Impact of Prawn Trawling on Benthic Species in the Great Barrier Reef Marine Park
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