Construction of experimental designs for mixed logit models allowing for correlation across choice observations

By Michiel C. J. Bliemer, John M. Rose. 2010

400 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f3f3b157d4131162e33c8fd1c67dc489fe1567b8

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In Economics and Econometrics

Citations in the corpus

Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. (2013, methodology): “By using the same general methodological framework used by Sándor and Wedel [42,43,55], Street and Burgess [16], Kanninen [22], Bliemer and Rose [29,44], and Bliemer et al.”
Providing quantified evidence to policy makers for promoting bike-sharing in heavily air-polluted cities: A mode choice model and policy simulation for Taiyuan-China (2018, neutral): “…is not as advanced as several later proposed designs, such as D-optimal design and D-efficient design (Bliemer et al., 2009; Rose and Bliemer, 2009; Bliemer and Rose, 2010), this project adopted the traditional orthogonal design due to time, cost and availability of advanced data collection tools…”
Sample size requirements for stated choice experiments (2013, result): “Nevertheless, recent research has shown (e.g., McFadden 1974; Sándor and Wedel 2001; Yu et al. 2009; Bliemer and Rose 2010) that D-optimal designs can provide (much) better parameter estimates than orthogonal designs, assuming that prior knowledge is available, at much smaller sample sizes.”
Efficient stated choice experiments for estimating nested logit models (2009, background): “To our best knowledge, the only paper dealing with this topic of dependent choice situations is Bliemer and Rose (2008).”
Contemporary Guidance for Stated Preference Studies (2017, background): “Experimental design in these contexts, and particularly for CEs, is complex and evolving (Sándor and Wedel 2002; Johnson et al. 2006, 2013; Ferrini and Scarpa 2007; Rose et al. 2008; Scarpa and Rose 2008; Rose and Bliemer 2009, 2014; Bliemer and Rose 2010; Vermeulen et al. 2011).”
On the Treatment of Repeated Observations in Panel Data: Efficiency of Mixed Logit Parameter Estimates (2011, background): “It is still inversely related to the square value of the attributes associated with each parameter (as in the case of the fixed parameters model), to the number of repeated observations, and is also a function of the probabilities ( Bliemer and Rose, 2010 ).”
Are consumers willing to pay to let cars drive for them? Analyzing response to autonomous vehicles (2017, methodology): “Attribute levels were combined into specific choice situations according to a Bayesian D-efficient design (Bliemer and Rose, 2010), with priors taken from a pretest of the survey (with sample size N=100).”
Approximation of bayesian efficiency in experimental choice designs (2008, neutral): “More recently however, some researchers have begun to question the relevance of orthogonal designs when applied to SC experiments (e.g., Huber and Zwerina 1996; Kanninen 2002; Kessels et al. 2006; Sándor and Wedel 2001, 2002, 2005; Bliemer and Rose 2008; Rose et al. 2008; Bliemer et al. in press).”
Sample Size Requirements for Discrete-Choice Experiments in Healthcare: a Practical Guide (2015, methodology): “Details of this variancecovariance matrix for various models have been provided elsewhere in the literature, in particular in the literature on generating efficient designs, see e.g. Kessels et al [2], Sandor & Wedel [3], and Bliemer & Rose [4].”
Location of logistics companies : a stated preference study to disentangle the impact of accessibility (2015, methodology): “To decide on the exact choice tasks, we utilized the optimal experimental design approach advocated by Bliemer & Rose (2010) and Kessels et al. (2009, 2011b-c).”
Bayesian optimal designs for discrete choice experiments with partial profiles (2011, methodology): “Several studies exploring the complexity and cognitive burden associated with DCEs provide evidence that a large number of attributes has a detrimental effect on the ability to choose, contributing to an increased error variance.”