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Averaging Approach (averaging + approach)

Distribution by Scientific Domains

Mathematics and Statistics	50%

Selected Abstracts

Fitting copulas to bivariate earthquake data: the seismic gap hypothesis revisited

ENVIRONMETRICS, Issue 3 2008
Aristidis K. Nikoloulopoulos
Abstract The seismic gap hypothesis assumes that the intensity of an earthquake and the time elapsed from the previous one are positively related. Previous works on this topic were based on particular assumptions for the joint distribution implying specific type of dependence. We investigate this hypothesis using copulas. Copulas are flexible for modelling the dependence structure far from assuming simple linear correlation structures and, thus, allow for better examination of this controversial aspect of geophysical research. In fact, via copulas, marginal properties and dependence structure can be separated. We propose a model averaging approach in order to allow for model uncertainty and diminish the effect of the choice of a particular copula. This enlarges the range of potential dependence structures that can be investigated. Application to a real data set is provided. Copyright © 2007 John Wiley & Sons, Ltd. [source]

A Bayesian model averaging approach for cost-effectiveness analyses

HEALTH ECONOMICS, Issue 7 2009
Caterina Conigliani
Abstract We consider the problem of assessing new and existing technologies for their cost-effectiveness in the case where data on both costs and effects are available from a clinical trial, and we address it by means of the cost-effectiveness acceptability curve. The main difficulty in these analyses is that cost data usually exhibit highly skew and heavy-tailed distributions so that it can be extremely difficult to produce realistic probabilistic models for the underlying population distribution, and in particular to model accurately the tail of the distribution, which is highly influential in estimating the population mean. Here, in order to integrate the uncertainty about the model into the analysis of cost data and into cost-effectiveness analyses, we consider an approach based on Bayesian model averaging: instead of choosing a single parametric model, we specify a set of plausible models for costs and estimate the mean cost with a weighted mean of its posterior expectations under each model, with weights given by the posterior model probabilities. The results are compared with those obtained with a semi-parametric approach that does not require any assumption about the distribution of costs. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Exact sampled-data analysis of quasi-resonant converters with finite filter inductance and capacitance

INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, Issue 1 2002
Chung-Chieh Fang
Abstract Previous models of quasi-resonant converters generally use averaging and assume infinite filter inductance and capacitance to reduce circuit complexity, but at the expense of accuracy. In this paper, exact sampled-data modelling is used. A general block diagram model applicable to various topologies of quasi-resonant converters is proposed. Large-signal analysis, steady-state analysis and small-signal analysis are all studied. They agree closely with the experimental results in the literature. Compared with the averaging approach, the sampled-data approach is more systematic and accurate. Copyright © 2001 John Wiley & Sons, Ltd. [source]

Bayesian regression with multivariate linear splines

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 1 2001
C. C. Holmes
We present a Bayesian analysis of a piecewise linear model constructed by using basis functions which generalizes the univariate linear spline to higher dimensions. Prior distributions are adopted on both the number and the locations of the splines, which leads to a model averaging approach to prediction with predictive distributions that take into account model uncertainty. Conditioning on the data produces a Bayes local linear model with distributions on both predictions and local linear parameters. The method is spatially adaptive and covariate selection is achieved by using splines of lower dimension than the data. [source]