Bayesian imputation of non-chosen attribute values in revealed preference surveys

Journal of Advanced Transportation

Volumn 48, Issue 1, 2014, Pages 48-65

  Washington, Simon ^a   Ravulaparthy, Srinath ^b   Rose, John M ^c   Hensher, David ^c   Pendyala, Ram ^d  

^a QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF SYDNEY   (Australia)

^d Arizona State University   (United States)

Author keywords

Bayesian methods; choice models; imputation; missing data analysis; multinomial logit; synthesized data; unobserved choice attributes

Indexed keywords

BAYESIAN NETWORKS; LOGISTIC REGRESSION; TRAFFIC CONTROL; TRAVEL TIME;

BAYESIAN METHODS; CHOICE MODEL; IMPUTATION; MISSING DATA ANALYSIS; MULTINOMIAL LOGIT; SYNTHESIZED DATA; UNOBSERVED CHOICE ATTRIBUTES;

SURVEYS;

EID: 84891866318     PISSN: 01976729     EISSN: 20423195     Source Type: Journal    
DOI: 10.1002/atr.201     Document Type: Article

References (22)

1. Hensher DA, Rose JM, Greene WH,. Applied Choice Analysis: A Primer. Cambridge University Press: Cambridge, 2005.
2. Jou YJ, Cho HJ, Lin PW, Wang CY, American Society of Civil Engineers. Incomplete information analysis for the Origin-Destination survey table. Journal of Urban Planning and Development 2006; 132 (4): 193-200.
3. Brownstone D,. Multiple imputation methodology for missing data, non-random response, and panel attrition. Theoretical Foundations of Travel Choice Modeling. University of California Transportation Center: Berkeley, 1998.
4. Rubin D,. Inference and missing data. Biometrika 1976; Biometrika Trust. 63 (3): 581-592.
5. Schafer JL,. Analysis of Incomplete Multivariate Data. CRC Press/Chapman Hall: Boca Raton, FL, 1997.
6. Laird NM,. Missing data in longitudinal studies. Statistics in Medicine 1988 Jan-Feb; 7 (1-2): 305-15.
7. th Annual Meeting. National Academies, Washington DC, 2008.
8. Wang J, Zou N, Chang G,. Empirical analysis of missing data issues for ATIS applications: travel time prediction. Proceedings of the Transportation Research Board 87th Annual Meeting. National Academies, Washington DC, 2008.
9. Zhao F, Chung S,. A study of alternative land use forecasting models. Florida Department of Transportation Report BD015-10: FDOT 99700-3596-119, 2006.
10. Steimetz SSC, Browstone D,. Estimating commuters "value of time" with noisy data: a multiple imputation approach. Transportation Research Part B: Methodological 2005; 39 (10): 865-889.
11. Brownstone D, Golob TF,. The effectiveness of ridesharing incentives: discrete-choice models of commuting in Southern California. Regional Science and Urban Economics 1992; 22: 5-24.
12. Congdon P,. Bayesian Statistical Modelling. Wiley: New York, NY, 2001.
13. Congdon P,. Bayesian predictive model comparison via parallel sampling. Computational Statistics and Data Analysis. Wiley: New York, NY, 2004.
14. Washington S, Karlaftis M, Mannering F,. Statistical and Econometric Methods for Transportation Data Analysis. Chapman Hall/CRC Press: Boca Raton, FL, 2003.
15. Marsaglia G,. Ratios of normal variables. Journal of Statistical Software 2006; (16): 4.
16. Daly A, Hess S, Train K,. 2009. Assuring finite moments for willingness to pay in random coefficient models. Presented at the European Transport Conference, Leeuwenhorst, October 2009.
17. Congdon P,. Applied Bayesian Statistical Models. Wiley: New York, NY, 2003.
18. Gelman A, Carlin J, Stern H, Rubin DB,. Bayesian Data Analysis. Chapman and Hall: New York, NY, 1995.
19. Gilks W, Richardson S, Spiegelhalter D, (eds.) Markov Chain Monte Carlo in Practice. Chapman and Hall: New York, NY, 1996.
20. Brooks S, Roberts GO,. Assessing convergence on Markov chain Monte Carlo algorithms. Statistics and Computing 1998; 8: 319-335.
21. Robert C, Casella G,. Monte Carlo Statistical Methods. Springer. New York, NY, 1997.
22. NuStats. Maricopa regional household travel survey, Final Report. Submitted to the Maricopa Association of Governments, Phoenix Arizona, 2002.