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A theoretical comparison of disco and CADIAG-II-like systems for medical diagnoses

Tatiana Kiseliova (2006)

Kybernetika

In this paper a fuzzy relation-based framework is shown to be suitable to describe not only knowledge-based medical systems, explicitly using fuzzy approaches, but other ways of knowledge representation and processing. A particular example, the practically tested medical expert system Disco, is investigated from this point of view. The system is described in the fuzzy relation-based framework and compared with CADIAG-II-like systems that are a “pattern” for computer-assisted diagnosis systems based...

A weighted version of Gamma distribution

Kanchan Jain, Neetu Singla, Rameshwar D. Gupta (2014)

Discussiones Mathematicae Probability and Statistics

Weighted Gamma (WG), a weighted version of Gamma distribution, is introduced. The hazard function is increasing or upside-down bathtub depending upon the values of the parameters. This distribution can be obtained as a hidden upper truncation model. The expressions for the moment generating function and the moments are given. The non-linear equations for finding maximum likelihood estimators (MLEs) of parameters are provided and MLEs have been computed through simulations and also for a real data...

Adaptive goodness-of-fit testing from indirect observations

Cristina Butucea, Catherine Matias, Christophe Pouet (2009)

Annales de l'I.H.P. Probabilités et statistiques

In a convolution model, we observe random variables whose distribution is the convolution of some unknown density f and some known noise density g. We assume that g is polynomially smooth. We provide goodness-of-fit testing procedures for the test H0: f=f0, where the alternative H1is expressed with respect to 𝕃 2 -norm (i.e. has the form ψ n - 2 ∥ f - f 0 ∥ 2 2 ≥ 𝒞 ). Our procedure is adaptive with respect to the unknown smoothness parameterτ of f. Different testing rates (ψn) are obtained according to whether f0 is polynomially...

Adaptive trimmed likelihood estimation in regression

Tadeusz Bednarski, Brenton R. Clarke, Daniel Schubert (2010)

Discussiones Mathematicae Probability and Statistics

In this paper we derive an asymptotic normality result for an adaptive trimmed likelihood estimator of regression starting from initial high breakdownpoint robust regression estimates. The approach leads to quickly and easily computed robust and efficient estimates for regression. A highlight of the method is that it tends automatically in one algorithm to expose the outliers and give least squares estimates with the outliers removed. The idea is to begin with a rapidly computed consistent robust...

Additive Covariance kernels for high-dimensional Gaussian Process modeling

Nicolas Durrande, David Ginsbourger, Olivier Roustant (2012)

Annales de la faculté des sciences de Toulouse Mathématiques

Gaussian Process models are often used for predicting and approximating expensive experiments. However, the number of observations required for building such models may become unrealistic when the input dimension increases. In oder to avoid the curse of dimensionality, a popular approach in multivariate smoothing is to make simplifying assumptions like additivity. The ambition of the present work is to give an insight into a family of covariance kernels that allows combining the features of Gaussian...

Addressing the problem of lack of representativeness on syndromic surveillance schemes

Isabel Natário, M. Lucília Carvalho (2009)

Discussiones Mathematicae Probability and Statistics

A major concern with some contagious diseases has recently led to an enormous effort to monitor population health status by several different means. This work presents a modeling approach to overcome this poor data characteristic, allowing its use for the estimation of the true population disease picture. We use a state space model, where we run two processes in parallel - a process describing the non observable states of the population concerning the presence/absence of disease,...

Currently displaying 81 – 100 of 1240