An Expectation-Maximization Algorithm to Estimate the Integrated Choice and Latent Variable Model

References

• Aitkin M, Aitkin I (1996) A hybrid EM/Gauss–Newton algorithm for maximum likelihood in mixture distributions. Statist. Comput. 6(2):127–130.Crossref, Google Scholar
• Ben-Akiva M, Lerman SR (1987) Discrete Choice Analysis, Theory and Application to Travel Demand (MIT Press, Cambridge, MA).Google Scholar
• Ben-Akiva M, Choudhury C, Toledo T (2006) Modeling latent choices: Application to driving behavior. Kitamura R, Yoshii T, Yamamoto T, eds. 11th Internat. Conf. Travel Behaviour Res. (Emerald Group Publishing, Bingley, UK), 16–20.Google Scholar
• Ben-Akiva M, Walker J, Bernardino AT, Gopinath D, Morikawa T, Polydoropoulou A (2002) Integration of choice and latent variable models. Mahmassani HS, ed. Perpetual Motion: Travel Behaviour Research Opportunities and Application Challenges (Elsevier, Amsterdam), 431–470.Crossref, Google Scholar
• Ben-Elia E, Erev I, Shiftan Y (2008) The combined effect of information and experience on drivers’ route choice behavior. Transportation 35(2):165–177.Crossref, Google Scholar
• Bhat CR (2003) Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences. Transportation Res. Part B 37(9):837–855.Crossref, Google Scholar
• Bhat CR (2011) The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models. Transportation Res. Part B 45(7):923–939.Crossref, Google Scholar
• Bhat CR, Dubey SK (2014) A new estimation approach to integrate latent psychological constructs in choice modeling. Transportation Res. Part B 67:68–85.Crossref, Google Scholar
• Bogers EAI, Viti F, Hoogendoorn SP (2005) Joint modeling of advanced travel information service, habit, and learning impacts on route choice by laboratory simulator experiments. Transportation Res. Record 1926:189–197.Crossref, Google Scholar
• Bolduc D, Daziano RA (2010) On estimation of hybrid choice models. Hess S, Daly A, eds. Choice Modeling: The State-of-Art and the State-of-Practice, Proc. Inaugural Internat. Choice Modeling Conf. (Emerald Group Publishing, Bingley, UK), 259.Crossref, Google Scholar
• Bollen KA (1989) Structural Equations with Latent Variables (Wiley, Chichester, UK).Crossref, Google Scholar
• Cherchi A, Guevara CA (2012) A Monte Carlo experiment to analyze the curse of dimensionality in estimating random coefficients models with a full variance–covariance matrix. Transportation Res. Part B 46(2):321–332.Crossref, Google Scholar
• Chiou L, Walker JL (2007) Masking identification of discrete choice models under simulation methods. J. Econometrics 141(2):683–703.Crossref, Google Scholar
• Davidson R, MacKinnon JG (1993) Estimation and Inference in Econometrics (Oxford University Press, Oxford, UK).Google Scholar
• Daziano RA, Bolduc D (2013a) Incorporating pro-environmental preferences towards a green automobile technologies through a Bayesian hybrid choice model. Transportmetrica A: Transport Sci. 9(1):74–106.Crossref, Google Scholar
• Daziano RA, Bolduc D (2013b) Covariance, identification, and finite-sample performance of the MSL and Bayes estimators of a logit model with latent variables. Transportation 40(3):640–670.Crossref, Google Scholar
• Efron B (1979) Bootstrap methods: Another look at the jack-knife. Ann. Statist. 7:1–26.Crossref, Google Scholar
• Greene W (2000) Econometric Analysis, Internat. ed. (Prentice Hall, Upper Saddle River, NJ).Google Scholar
• Johansson MV, Heldt T, Johansson P (2006) The effects of attitudes and personality trait on mode choice. Transportation Res. Part A 40(6):507–525.Google Scholar
• Kenny DA (1979) Correlation and Causality (Wiley, New York).Google Scholar
• Madanat SM, Yang CY, Yen YM (1995) Analysis of stated route diversion intentions under advanced traveler information systems using latent variable modeling. Transportation Res. Record 1485:10–17.Google Scholar
• Morikawa T, Ben-Akiva M, McFadden D (2002) Discrete choice models incorporating revealed preferences and psychometric data. Franses PH, Montgomery AL, eds. Econometric Models in Marketing, Vol. 16 (JAI, Amsterdam), 29–55.Crossref, Google Scholar
• Polydoropoulou A, Ben-Akiva ME, Kaysi I (1995) Influence of traffic information on drivers’ route choice behavior. Transportation Res. Record 1453:56–65.Google Scholar
• Prashker JA (1979) Scaling perceptions of reliability of urban travel modes using Indscal factor analysis methods. Transportation Res. Part A 13(3):203–212.Crossref, Google Scholar
• Prato CG, Bekhor S, Pronello C (2012) Latent variables and route choice behavior. Transportation 39(2):299–319.Crossref, Google Scholar
• Stapleton DC (1978) Analyzing political participation data with a MIMIC model. Sociol. Methodology 15(1):52–74.Crossref, Google Scholar
• Train K (2007) A recursive estimator for random coefficient models. Working paper, Department of Economics, University of California, Berkeley.Google Scholar
• Train K (2008) Discrete Choice Methods with Simulation (Cambridge University Press, Cambridge, UK).Google Scholar
• Walker JL (2001) Extended discrete choice models: integrated framework, flexible error structures, and latent variables. Unpublished doctoral thesis, Massachusetts Institute of Technology, Cambridge.Google Scholar
• Weeks D, Lange K (1989) Trials, tribulations, and triumphs of the EM algorithm in pedigree analysis. J. Math. Appl. Medicine Biol. 6(4):209–232.Crossref, Google Scholar