1,721,108 research outputs found
Stima delle probabilità di incidenti su una rete informatica con il modello IDM (Imprecise Dirichlet Model)
No full textA note on multiple testing for composite null hypotheses
No full textMultiple hypothesis testing literature has recently experienced a growing development with particular attention to the control of the false discovery rate (FDR) based on p-values. While these are not the only methods to deal with multiplicity, inference with small samples and large sets of hypotheses depends on the specific choice of the p-value used to control the FDR in the presence of nuisance parameters. In this paper we propose to use the partial poste- rior predictive p-value [Bayarri, M.J., Berger, J.O., 2000. p-values for composite null models. J. Amer. Statist. Assoc. 95, 1127–1142] that overcomes this difficulty. This choice is motivated by theoretical considerations and examples. Finally, an application to a controlled microarray experiment is presented
Tree-ring based climate reconstruction using a hierarchical Bayesian model
No full textA hierarchical Bayesian model for paleoclimate reconstruction is illustrated along with
an application to an Italian site. Climate is represented through temperature and moisture variables, while the
reconstruction is based on tree-ring widths only
Extreme value analysis within a parametric outlier detection framework
No full textThreshold selection is a key aspect in extreme values analysis, especially when the sample size is small. The main idea underpinning this work is that extreme observations are assumed to be outliers of a specified parametric model. We propose a threshold selection method based on outlier detection using a suitable measure of surprise
A Markov chain representation of the multiple testing problem
Full text linkThe problem of multiple hypothesis testing can be represented as a Markov process where a new alternative hypothesis is accepted in accordance with its relative evidence to the currently accepted one. This virtual and not formally observed process provides the most probable set of non null hypotheses given the data; it plays the same role as Markov Chain Monte Carlo in approximating a posterior distribution. To apply this representation and obtain the posterior probabilities over all alternative hypotheses, it is enough to have, for each test, barely defined Bayes Factors, e.g. Bayes Factors obtained up to an unknown constant. Such Bayes Factors may either arise from using default and improper priors or from calibrating p-values with respect to their corresponding Bayes Factor lower bound. Both sources of evidence are used to form a Markov transition kernel on the space of hypotheses. The approach leads to easy interpretable results and involves very simple formulas suitable to analyze large datasets as those arising from gene expression data (microarray or RNA-seq experiments)
- …
