1,721,023 research outputs found
Hierarchical space-time modelling of epidemic dynamics: an application to measles outbreaks
No full textHow infectious diseases spread in space and time is an important question
that has received considerable theoretical attention. There are, however, few
empirical studies to support theoretical approaches, because data is scarce. In this
paper we propose to model the epidemic spread of measles in the London boroughs
between 1960 and 1970 by an extension of the Kriged Kalman filter (Mardia et
al., 1998) to count data. Results show the flexibility of our approach in describing
complex spatio-temporal dynamic
Effectiveness of combinations of Gaussian graphical models for model building
No full textCombining statistical models is an useful approach in all the research area where a global picture of the problem needs to be constructed by binding together evidence from different sources [M.S. Massa and S.L. Lauritzen Combining Statistical Models, M. Viana and H. Wynn, eds., American Mathematical Society, Providence, RI, 2010, pp. 239-259]. In this paper, we investigate the effectiveness of combining a fixed number of Gaussian graphical models respecting some consistency assumptions in problems of model building. In particular, we use the meta-Markov combination of Gaussian graphical models as detailed in Massa and Lauritzen and compare model selection results obtained by combining selections over smaller sets of variables with selection results over all variables of interest. In order to do so, we carry out some simulation studies in which different criteria are considered for the selection procedures. We conclude that the combination performs, generally, better than global estimation, is computationally simpler by virtue of having fewer and simpler models to work on, and has an intuitive appeal to a wide variety of contexts. © 2013 Copyright Taylor and Francis Group, LLC
A note on the asymptotic distribution of the maximum likelihood estimator for the scalar Skew-normal distribution
No full textWe consider likelihood based inference for the parameter of a skewnormal
distribution. One of the problems shown by this model is the singularity
of the Fisher information matrix when skewness is absent. We derive the rate of
convergence to the asymptotic distribution of the maximum likelihood estimator
and study an alternative parameterization which overcomes problems related to the
singularity of the information matrix
Combinations of covariance selections for graphical modelling.
No full textWe explore the possibility of composing the results of a fixed number of Gaussian graphical model selections on some partially overlapping variables. This appears to be an useful approach in all the research areas where a large amount of data from different sources and types of experiments is available. Therefore the focus is in binding together information coming from heterogeneous studies to improve the understanding of a particular phenomenon of interest. The proposed approach relies on numerical results on artificial and real data
Exploratory analysis of epidemiological time series by means of transfer function models
No full textPartially parametric interval estimation of Pr(Y>X)
No full textLet X andY be two independent continuous random variables. Three techniques to obtain confidence intervals for \rho=PrY >X are
discussed in a partially parametric framework. One method relies on the asymptotic normality of an estimator for \rho; the remaining
methods involve empirical likelihood and combine it with maximum likelihood estimation and with full parametric likelihood,
respectively. Finite-sample accuracy of the confidence intervals is assessed through a simulation study.An illustration is given using
a data set on the detection of carriers of Duchenne Muscular Dystrophy
Additive regression modelling for zero inflated Poisson distribution
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Short term ozone effects on morbidity for the city of Milano, Italy, 1996-2003.
Full text linkIn this paper, we explore a range of concerns that arise in measuring short term ozone effects on health. In particular, we tackle the problem of measuring exposure using alternative daily measures of ozone derived from hourly concentrations. We adopt the exposure paradigm of Chiogna and Bellini (2002), and we compare its performances with respect to traditional exposure measures by exploiting model selection. For investigating model selection stability issues, we then apply the idea of bootstrapping the modelling process
Hierarchical space-time modelling of epidemics dynamics: an application to measles outbreaks
No full textSemiparametric zero-inflated Poisson models with application to animal abundance studies
No full textThis paper describes a framework for flexibly modeling zero-inflated data. Semiparametric regression based on
penalized regression splines for zero-inflated Poisson models is introduced. Moreover, an EM-type algorithm is
developed to perform maximum likelihood estimation. As an illustration, a study of animal abundance is tackled.
In fact, abundance often shows excess of zeroes and is a complicated function of the explanatory variables. In
particular, the relationships between avian abundance and environmental variables indicating land use are tackled
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