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Discussion to: Bayesian graphical models for modern biological applications by Y. Ni, V. Baladandayuthapani, M. Vannucci and F.C. Stingo
No full textWe contribute to the discussion of the paper by Ni et al. (Stat Methods Appl, 2021. https://doi.org/10.1007/s10260-021-00572-8) by focusing on two aspects: (i) ordering of the variables for directed acyclic graphical models, and (ii) heterogeneity of the data in the presence of covariates. With regard to (i) we claim that an ordering should be assumed only when strongly reliable prior information is available; otherwise one should proceed with an unspecified ordering to guard against order misspecification. Alternatively, one can carry out Bayesian inference on the space of Markov equivalence classes or use a blend of observational and interventional data to alleviate the lack of identification. With regard to (ii) we complement the Authors’ analysis by enlarging the scope to mixed graphs as well as nonparametric Bayesian models
Assessing skewness in financial markets
Full text linkIt is common knowledge that investors like large gains and dislike large losses.
This translates into a preference for right-skewed return distributions, with right tails heavier than left tails.
Skewness is thus interesting not only as a way to describe the shape of a distribution, but also for risk measurement.
We review the statistical literature on skewness and provide a comprehensive framework for its assessment.
We present a new measure of skewness, based on a relative comparison between above average and below average returns.
We show that this measure represents a valid complement to the state of the art
Flash-pumped pulsed dye laser for vascular skin lesions: our experience and an overview of the literature
No full textFlash-pumped pulsed dye laser for vascular skin lesions: our experience and an overview of the literature
No full textTests based on intrinsic priors for the equality of two correlated proportions
No full textCorrelated proportions arise in longitudinal (panel) studies. A typical example is the “opinion swing” problem: “Has the proportion of
people favoring a politician changed after his recent speech to the nation on TV?” Because the same group of individuals is interviewed
before and after the speech, the two proportions are correlated. A natural null hypothesis to be tested is whether the corresponding population
proportions are equal. A standard Bayesian approach to this problem has already been considered in the literature, based on a Dirichlet prior
for the cell probabilities of the underlying 2×2 table under the alternative hypothesis, together with an induced prior under the null. With a
lack of specific prior information, a diffuse (e.g., uniform) distribution may be used.We claim that this approach is not satisfactory, because
in a testing problem one should make sure that the prior under the alternative is adequately centered around the region specified by the
null, in order to obtain a fairer comparison between the two hypotheses, especially when the data are in reasonable agreement with the null.
Following an intrinsic prior methodology, we develop two strategies for the construction of a collection of objective priors increasingly
peaked around the null.We provide a simple interpretation of their structure in terms of weighted imaginary sample scenarios.We illustrate
our method by means of three examples, carrying out sensitivity analysis and providing comparison with existing results
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