Browsing by Author "Guglielmi, Alessandra"
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- ItemA semiparametric Bayesian joint model for multiple mixed-type outcomes: an application to acute myocardial infarction(2018) Guglielmi, Alessandra; Ieva, Francesca; Paganoni, Anna Maria; Quintana Quintana, Fernando
- ItemA Simple Class of Bayesian Nonparametric Autoregression Models(2013) Di Lucca, Maria Anna; Guglielmi, Alessandra; Müller, Peter; Quintana Quintana, Fernando
- ItemBayesian first order auto-regressive latent variable models for multiple binary sequences(SAGE PUBLICATIONS LTD, 2011) Giardina, Federica; Guglielmi, Alessandra; Quintana, Fernando A.; Ruggeri, FabrizioLongitudinal clinical trials often collect long sequences of binary data monitoring a disease process over time. Our application is a medical study conducted in the US by the Veterans Administration Cooperative Urological Research Group to assess the effectiveness of a chemotherapy treatment (thiotepa) in preventing recurrence on subjects affected by bladder cancer. We propose a generalized linear model with latent auto-regressive structure for longitudinal binary data following a Bayesian approach. We discuss inference as well as sensitivity to prior choices for the bladder cancer data. We find that there is a significant treatment effect in the sense that treated patients have much smaller predicted recurrence probabilities than placebo patients.
- ItemThe Semi-Hierarchical Dirichlet Process and Its Application to Clustering Homogeneous Distributions(INT SOC BAYESIAN ANALYSIS, 2021) Beraha, Mario; Guglielmi, Alessandra; Quintana, Fernando A.Assessing homogeneity of distributions is an old problem that has received considerable attention, especially in the nonparametric Bayesian literature. To this effect, we propose the semi-hierarchical Dirichlet process, a novel hierarchical prior that extends the hierarchical Dirichlet process of Teh et al. (2006) and that avoids the degeneracy issues of nested processes recently described by Camerlenghi et al. (2019a). We go beyond the simple yes/no answer to the homogeneity question and embed the proposed prior in a random partition model; this procedure allows us to give a more comprehensive response to the above question and in fact find groups of populations that are internally homogeneous when I >= 2 such populations are considered. We study theoretical properties of the semi hierarchical Dirichlet process and of the Bayes factor for the homogeneity test when I = 2. Extensive simulation studies and applications to educational data are also discussed.