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Savchuk, Vladimir; Tsokos, Chris P.
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For a long time there were a lot of unsuccessful efforts directed toward the solution of many nonparametric problems with the help of the Bayes approach. This can be explained mainly by difficulties a researcher encounter, when he attempts to find a suitable prior distribution, determined on a sample space. Such a distribution in nonparametric problems is chosen in the form of a set of probability distributions on the given sample space. The first work in this field where some progress has been achieved belongs to Ferguson. Ferguson formulated the requirements which must be imposed on a prior distribution
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Brockwell, Peter J.; Davis, Richard A.
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1 Citations
Many time series arising in practice are best considered as components of some vector valued (multivariate) time series {X_{t}} having not only serial dependence within each component series {X_{ti}} but also interdependence between the different component series {X_{ti}} and {X_{tj}}, i ≠ j. Much of the theory of univariate time series extends in a natural way to the multivariate case; however, new problems arise.
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Mignani, Stefania; Cagnone, Silvia; Casadei, Giorgio; Carbonaro, Antonella
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The aim of this paper is to evaluate the student learning about Computer Science subjects. A questionnaire based on ordinal scored items has been submitted to the students through a computer automated system. The data collected have been analyzed by using a latent variable model for ordinal data within the Item Response Theory framework. The scores obtained from the model allow to classify the students according to the reached competence.
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Earle, Keith A.; Broderick, Troy; Kazakov, Oleks
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Magnetic Resonance spectroscopy is a powerful tool for elucidating the details of molecular dynamics. In many important applications, a model of hindered diffusion is useful for summarizing the complex dynamics of ordered media, such as a liquid crystalline environment, as well as the dynamics of proteins in solution or confined to a membrane. In previous work, we have shown how the sensitivity of a magnetic resonance spectrum to the details of molecular dynamics depends on the symmetries of the magnetic tensors for the relevant interactions, e.g., Zeeman, hyperfine, or quadrupolar interactions. If the hindered diffusion is modeled as arising from an orienting potential, then the parameter sensitivity of the magnetic resonance spectrum may be studied by generalizations of methods we have introduced in previous work. In particular, we will show how lineshape calculations using eigenfunction expansions of solutions of the diffusion equation, can be used as inputs to an informationgeometric approach to parameter sensitivity estimation. We illustrate our methods using model systems drawn from Nuclear Magnetic Resonance, Electron Spin Resonance, and Nuclear Quadrupole Resonance.
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Giglio, Beatrice; Wynn, Henry P.; Riccomagno, Eva
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The theory of mixture designs has a considerable history. We address here the important issue of the analysis of an experiment having in mind the algebraic interpretation of the structural restriction Σx_{i} = 1. We present an approach for rewriting models for mixture experiments, based on constructing homogeneous orthogonal polynomials using Gröbner bases. Examples are given utilising the approach.
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Dufner, Julius; Jensen, Uwe; Schumacher, Erich
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Zusammenfassung
SAS (Statistical Analysis System) — in den Jahren von 1975 bis 1980 noch eine reine Statistiksoftware — ist inzwischen zu einem umfassenden Softwaresystem zur Verwaltung, Analyse und Darstellung von Daten ausgebaut worden.
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Maleki, Mohsen; Wraith, Darren; ArellanoValle, Reinaldo B.
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1 Citations
In this paper, we introduce an unrestricted skewnormal generalized hyperbolic (SUNGH) distribution for use in finite mixture modeling or clustering problems. The SUNGH is a broad class of flexible distributions that includes various other wellknown asymmetric and symmetric families such as the scale mixtures of skewnormal, the skewnormal generalized hyperbolic and its corresponding symmetric versions. The class of distributions provides a much needed unified framework where the choice of the best fitting distribution can proceed quite naturally through either parameter estimation or by placing constraints on specific parameters and assessing through model choice criteria. The class has several desirable properties, including an analytically tractable density and ease of computation for simulation and estimation of parameters. We illustrate the flexibility of the proposed class of distributions in a mixture modeling context using a Bayesian framework and assess the performance using simulated and real data.
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Besbeas, Panagiotis; Borysiewicz, Rachel S.; Morgan, Bryon J.T.
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10 Citations
A challenge for integrated population methods is to examine the extent to which different surveys that measure different demographic features for a given species are compatible. Do the different pieces of the jigsaw fit together? One convenient way of proceeding is to generate a likelihood for census data using the Kalman filter, which is then suitably combined with other likelihoods that might arise from independent studies of mortality, fecundity, and so forth. The combined likelihood may then be used for inference. Typically the underlying model for the census data is a statespace model, and capture–recapture methods of various kinds are used to construct the additional likelihoods. In this paper we provide a brief review of the approach; we present a new way to start the Kalman filter, designed specifically for ecological processes; we investigate the effect of breakdown of the independence assumption; we show how the Kalman filter may be used to incorporate densitydependence, and we consider the effect of introducing heterogeneity in the statespace model.
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