Name
Abstract | ||
|---|---|---|
We consider the case in which the available knowledge does not allow to specify a precise probabilistic model for the prior and/or likelihood in statistical estimation. We assume that this imprecision can be represented by belief functions models. Thus, we exploit the mathematical structure of belief functions and their equivalent representation in terms of closed convex sets of probabilities to derive robust posterior inferences using Walley@?s theory of imprecise probabilities. Then, we apply these robust models to practical inference problems and we show the connections of the proposed inference method with interval estimation and statistical inference with missing data. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.ijar.2013.04.014 | Int. J. Approx. Reasoning |
Keywords | Field | DocType |
belief function,statistical estimation,proposed inference method,practical inference problem,robust model,belief functions model,multivalued mapping robustness,statistical inference,available knowledge,interval estimation,robust posterior inference | Interval estimation,Frequentist inference,Inference,Fiducial inference,Robustness (computer science),Statistical inference,Statistical model,Artificial intelligence,Missing data,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
55 | 1 | 0888-613X |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessio Benavoli | 1 | 229 | 30.52 |