TY - JOUR
T1 - Accounting for model uncertainty in individualized designs for discrete choice experiments
AU - Consonni, Guido
AU - Deldossi, Laura
AU - Saggini, Eleonora
PY - 2020
Y1 - 2020
N2 - We develop a method for finding optimal designs in Discrete Choice Experiments (DCEs). More specifically, we deal with individualized designs, which are sequentially generated for each person undertaking a survey, using - at each stage - responses from previous choice sets in order to select the next best set. 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 attributes or factors deemed to be relevant in each choice. Using a unique model represents a major limitation which we overcome by allowing for a collection of different models
characterized by distinct linear predictors.
Our approach is fully Bayesian and incorporates a prior distribution on the model space, as well as a prior on the parameter space specific to each respondent.
To perform our optimal design strategy for DCEs, we specify two distinct utility functions, one being targeted to model discrimination, while the other aims at the dual purpose of model discrimination and parameter estimation. We implement our methodology using a sequential Monte Carlo algorithm suitably tailored to account for
model uncertainty and individual-specific parameters.
To evaluate our methodology, several simulation settings are considered in detail. Comparisons with alternative methods are also investigated.
AB - We develop a method for finding optimal designs in Discrete Choice Experiments (DCEs). More specifically, we deal with individualized designs, which are sequentially generated for each person undertaking a survey, using - at each stage - responses from previous choice sets in order to select the next best set. 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 attributes or factors deemed to be relevant in each choice. Using a unique model represents a major limitation which we overcome by allowing for a collection of different models
characterized by distinct linear predictors.
Our approach is fully Bayesian and incorporates a prior distribution on the model space, as well as a prior on the parameter space specific to each respondent.
To perform our optimal design strategy for DCEs, we specify two distinct utility functions, one being targeted to model discrimination, while the other aims at the dual purpose of model discrimination and parameter estimation. We implement our methodology using a sequential Monte Carlo algorithm suitably tailored to account for
model uncertainty and individual-specific parameters.
To evaluate our methodology, several simulation settings are considered in detail. Comparisons with alternative methods are also investigated.
KW - Bayesian sequential design
KW - Model discrimination, Mutual information, Optimal design, Sequential Monte Carlo, Total Entropy
KW - Bayesian sequential design
KW - Model discrimination, Mutual information, Optimal design, Sequential Monte Carlo, Total Entropy
UR - http://hdl.handle.net/10807/130930
U2 - 10.1080/00224065.2019.1569962
DO - 10.1080/00224065.2019.1569962
M3 - Article
SN - 0022-4065
VL - 2020
SP - 81
EP - 96
JO - Journal of Quality Technology
JF - Journal of Quality Technology
ER -