The prediction of future outcomes of a random phenomenon is typically based on a certain number of “analogous” observations from the past. When observations are generated by multiple samples, a natural notion of analogy is partial exchangeability and the problem of prediction can be effectively addressed in a Bayesian nonparametric setting. Instead of confining ourselves to the prediction of a single future experimental outcome, as in most treatments of the subject, we aim at predicting features of an unobserved additional sample of any size. We first provide a structural property of prediction rules induced by partially exchangeable arrays, without assuming any specific nonparametric prior. Then we focus on a general class of hierarchical random probability measures and devise a simulation algorithm to forecast the outcome of m future observations, for any m≥1. The theoretical result and the algorithm are illustrated by means of a real dataset, which also highlights the “borrowing strength” behavior across samples induced by the hierarchical specification.
Camerlenghi, F., Lijoi, A., & Pruenster, I. (2017). Bayesian prediction with multiple-samples information. JOURNAL OF MULTIVARIATE ANALYSIS, 156, 18-28 [10.1016/j.jmva.2017.01.010].
Citazione: | Camerlenghi, F., Lijoi, A., & Pruenster, I. (2017). Bayesian prediction with multiple-samples information. JOURNAL OF MULTIVARIATE ANALYSIS, 156, 18-28 [10.1016/j.jmva.2017.01.010]. | |
Tipo: | Articolo in rivista - Articolo scientifico | |
Carattere della pubblicazione: | Scientifica | |
Presenza di un coautore afferente ad Istituzioni straniere: | No | |
Titolo: | Bayesian prediction with multiple-samples information | |
Autori: | Camerlenghi, F; Lijoi, A; Pruenster, I | |
Autori: | ||
Data di pubblicazione: | 2017 | |
Lingua: | English | |
Rivista: | JOURNAL OF MULTIVARIATE ANALYSIS | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1016/j.jmva.2017.01.010 | |
Appare nelle tipologie: | 01 - Articolo su rivista |