428 research outputs found
Gamma shape mixtures for heavy-tailed distributions
No full textAn important question in health services research is the estimation of the proportion of medical expenditures that exceed a given threshold. Typically, medical expenditures present highly skewed, heavy tailed distributions, for which (a) simple variable transformations are insufficient to achieve a tractable low-dimensional parametric form and (b) nonparametric methods are not efficient in estimating exceedance probabilities for large thresholds. Motivated by this context, in this paper we propose a general Bayesian approach for the estimation of tail probabilities of heavy-tailed distributions, based on a mixture of gamma distributions in which the mixing occurs over the shape parameter. This family provides a flexible and novel approach for modeling heavy-tailed distributions, it is computationally efficient, and it only requires to specify a prior distribution for a single parameter. By carrying out simulation studies, we compare our approach with commonly used methods, such as the log-normal model and nonparametric alternatives. We found that the mixture-gamma model significantly improves predictive performance in estimating tail probabilities, compared to these alternatives. We also applied our method to the Medical Current Beneficiary Survey (MCBS), for which we estimate the probability of exceeding a given hospitalization cost for smoking attributable diseases.We have implemented the method in the open source GSM package, available from the Comprehensive R Archive Network
Generalized Quantile Treatment Effect: A Flexible Bayesian Approach Using Quantile Ratio Smoothing
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Planning tools as an aid for multimedia didactics for university students with visual impairments: the communicative aspects.
No full textThis text offers an analysis of the communicative aspects used in the processes of transmitting visual information to blind and partially sighted people through the use of media. The intention is to reflect on what and how to describe the significant information, conveyed through images, in order to improve accessibility to university lectures that make use of multimedia supports (videos, PowerPoint etc.) for minority groups. The work is part of a research project at the University of Ferrara, carried out in partnership between the Department of rights to study and disability services for students (S.M.S. Service) and the Department of Humanities (CARIDlab, Workshop in Science and Technology of cognitive processes and learning), which envisages the implementation of two video lectures, one in the humanistic field, and the other in the medical field1. The final aim of this text is therefore to provide describers with a series of guidelines, identified through the study of reference literature, functional to the application of (visual) content adaptation processes to university lectures for blind and partially sighted students
Combinatorial Mixtures of Multiparameter Distributions: an Application to Prostate Cancer
No full textThe term combinatorial mixtures refers to a flexible class of parametric models for inference on mixture distributions whose components have multidimensional parameters [1]. The idea behind it is to allow each element of the component-specific parameter vector to be shared by a subset of other components.
We develop Bayesian inference and computational approaches based on Markov Chain Monte Carlo methods for this class of mixture distributions with an unknown number of components. We define the structure for a general prior distribution - a mixture of prior distributions itself - where a positive probability is put on every possible combination of sharing patterns. We illustrate our approach in an application based on the normal mixture model for bivariate data. We assume a decomposition of the covariance matrix which allows to model standard deviations and correlations separately. We also discuss solutions to the 'label switching' problem.
For our application, we use publicly available data on mRNA expression in prostate carcinoma [2], where a two-component 'ellipsoidal, varying volume, shape, and orientation' model has been suggested by a different approach [3]
Multi-study factor analysis
No full textWe introduce a novel class of factor analysis methodologies for the joint analysis of multiple studies. The goal is to separately identify and estimate (1) common factors shared across multiple studies, and (2) study-specific factors. We develop an Expec- tation Conditional-Maximization algorithm for parameter estimates and we provide a procedure for choosing the numbers of common and specific factors. We present sim- ulations for evaluating the performance of the method and we illustrate it by applying it to gene expression data in ovarian cancer. In both, we clarify the benefits of a joint analysis compared to the standard factor analysis. We have provided a tool to acceler- ate the pace at which we can combine unsupervised analysis across multiple studies, and understand the cross-study reproducibility of signal in multivariate data. An R package (MSFA), is implemented and is available on GitHub
Combinatorial Mixtures of Multiparameter Distributions : An Application to Bivariate Data
No full textAbstract: We introduce combinatorial mixtures - a flexible class of models for inference on mixture distributions whose components have multidimensional parameters. The key idea is to allow each element of the component-specific parameter vectors to be shared by a subset of other components. This approach allows for mixtures that range from very flexible to very parsimonious and unifies inference on component-specific parameters with inference on the number of components. We develop Bayesian inference and computational approaches for this class of distributions, and illustrate them in an application. This work was originally motivated by the analysis of cancer subtypes: in terms of biological measures of interest, subtypes may be characterized by differences in location, scale, correlations or any of the combinations. We illustrate our approach using publicly available data on molecular subtypes of lung and prostate cancers
Progettazione di un motore Ringbom-Stirling per la produzione di energia elettrica nei paesi in via di sviluppo
No full textVengono presentati i criteri di progetto ed i risultati della simulazione numerica per un motore Ringbom-Stirling di piccola potenza (1 kW). Nella fase di studio si è anche previsto l'impiego di soffietti metallici
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