Abstract
We consider the issue of finding optimal designs for Discrete Choice Experiments (DCE). Currently the optimal design for a DCE is predicated on a specific mixed logit model which represents the probability of choosing a specific alternative in a choice set through a linear predictor combining several factors. Using a unique model represents a major limitation which we overcome by allowing for a collection of different models characterized by distinct linear predictors. In this new setting, model choice becomes a major concern.
By maximizing the utility of selecting a choice set - computed as the mutual information between the model indicator and the predicted observation at that set - over alternative choice sets, model discrimination is enhanced.
We implement our methodology using a sequential Monte Carlo algorithm suitably tailored to deal with model uncertainty.
Lingua originale | English |
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Titolo della pubblicazione ospite | Cladag 2017. Book of short papers |
Pagine | N/A |
Stato di pubblicazione | Pubblicato - 2017 |
Evento | CLADAG 2017 - Milano Durata: 13 set 2017 → 15 set 2017 |
Convegno
Convegno | CLADAG 2017 |
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Città | Milano |
Periodo | 13/9/17 → 15/9/17 |
Keywords
- Bayesian sequential design
- Model discrimination
- Optimal design