Dissecting the Random Component of Utility

By Jordan J. Louviere, Deborah J. Street, Richard T. Carson, Andrew Ainslie, J. Deshazo, T. Cameron, David A. Hensher, Robert Kohn, T. Marley. 2002

249 citations recorded by the source corpus.

Subfields: Economics and Econometrics, General Decision Sciences, Marketing

View Paper: https://www.semanticscholar.org/paper/a83516c1a70d8df0efe005b8ccc4da6b7bb455ca

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Citations in the corpus

The Mixed Logit model: The state of practice (2003, background): “What is important for modellers is the recognition that each individual's random component variance is perfectly confounded with their mean or systematic components (Louviere et al 2001).”
Conducting Discrete Choice Experiments to Inform Healthcare Decision Making (2012, background): “dent efficiency (influenced in part by cognitive bur- single data sources.[93] Thus, we need to understand den), which remains under-researched despite some the effects of these factors, including the effects on work.”
Using discrete choice experiments to value health and health care (2008, background): “Indeed, there is evidence to suggest that the complexity of the task in a DCE affects, one of the multiple components of the random part of the utility function (Louviere et al., 2002), increasing response variability hence error variances (see, e.”
Designing Policy for Reducing the Off-farm Effects of Soil Erosion Using Choice Experiments (2005, background): “2 Although see the discussion in Louviere et al., (2002) .”
Modeling preference heterogeneity in stated choice data: an analysis for public goods generated by agriculture (2009, background): “Louviere et al. (2002) argued that each person has a scale factor that is perfectly correlated with their parameter vector; hence the distribution estimated in RPL choice models is indeterminate.”
Small-sample properties of tests for heteroscedasticity in the conditional logit model (2006, background): “This assumption has been called into question in several recent papers (Hensher et al. 1999; Louviere, 2001; DeShazo and Fermo, 2002; Louviere et al., 2002).”
Bayesian optimal designs for discrete choice experiments with partial profiles (2011, background): “However, estimation of the MNL model (3) does not allow to separately identify both β and μ (Ben-Akiva and Lerman 1985; Swait and Louviere 1993; Louviere et al. 2002).”
Modeling the choices of individual decision-makers by combining efficient choice experiment designs with extra preference information (2008, methodology): “If assumptions about preference distributions are incorrect, the inferences from top-down models will be biased and incorrect (e.g., Louviere et al. 2000, 2002; Louviere 2004a,b; Louviere and Eagle 2006).1 Thus, the purpose of this paper is to propose, describe, illustrate and discuss a relatively…”