A comparison of different Bayesian design criteria for setting up stated preference studies

Transportation Research Part B: Methodological

Volumn 46, Issue 7, 2012, Pages 789-807

  Yu, Jie ^a   Goos, Peter ^b,c   Vandebroek, Martina ^a,d  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b UNIVERSITY OF ANTWERP   (Belgium)

^c ERASMUS UNIVERSITY ROTTERDAM   (Netherlands)

^d Katholieke Universiteit Leuven   (Belgium)

Author keywords

Bayesian design criterion; Discrete choice experiment; Expected posterior covariance matrix; Fisher information; Generalized Fisher information; Shannon information

Indexed keywords

BAYESIAN NETWORKS; COVARIANCE MATRIX; DESIGN OF EXPERIMENTS; INVERSE PROBLEMS; MATRIX ALGEBRA; MAXIMUM LIKELIHOOD; MAXIMUM LIKELIHOOD ESTIMATION; OPTIMAL SYSTEMS; QUALITY CONTROL; SAMPLING; STATISTICS;

BAYESIAN DESIGN CRITERIONS; DISCRETE CHOICE EXPERIMENTS; EXPECTED POSTERIOR COVARIANCE MATRIXES; FISHER INFORMATION; GENERALIZED FISHER INFORMATIONS; SHANNON INFORMATION;

FISHER INFORMATION MATRIX;

BAYESIAN ANALYSIS; COMPARATIVE STUDY; COVARIANCE ANALYSIS; DISCRETE CHOICE ANALYSIS; LOGIT ANALYSIS; MAXIMUM LIKELIHOOD ANALYSIS; PARADIGM SHIFT;

EID: 84861345193     PISSN: 01912615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trb.2012.01.007     Document Type: Article

References (75)

1. Albert J.H., Chib S. Bayesian analysis of binary and polychotomous response data. Journal of the American Statistical Association 1993, 88(422):669-679.
2. Allenby G.M., Rossi P.E. Marketing models of consumer heterogeneity. Journal of Econometrics 1999, 89(1-2):57-78.
3. Arora N., Allenby G., Ginter J. A hierarchical Bayes model of primary and secondary demand. Marketing Science 1998, 17(1):29-44.
4. Arora N., Huber J. Improving parameter estimates and model prediction by aggregate customization in choice experiments. Journal of Consumer Research 2001, 28(2):273-283.
5. Atkinson A.C., Donev A.N., Tobias R. Optimum Experimental Designs with SAS 2007, Clarendon Press, Oxford, UK.
6. Axhausen K.W., Hess S., König A., Abay G., Bates J.J., Bierlaire M. Income and distance elasticities of values of travel time savings: new Swiss results. Transport Policy 2008, 15(3):173-185.
7. Berger J.O. Statistical Decision Theory and Bayesian Analysis. Springer Series in Statistics 1985, Springer-Verlag.
8. Bhat C.R. Quasi-random maximum simulated likelihood estimation of the mixed multinomial logit model. Transportation Research Part B 2001, 35(7):677-693.
9. Bhat C. Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences. Transportation Research Part B 2003, 37(9):837-855.
10. Bliemer, M.C.J., Rose, J.M., 2006. Designing stated choice experiments: state-of-the- art. Paper presented at the 11th International Conference on Travel Behaviour Research, Kyoto, Japan.
11. Bliemer M.C.J., Rose J.M. Construction of experimental designs for mixed logit models allowing for correlation across choice observations. Transportation Research Part B 2010, 44(6):720-734.
12. Bliemer M.C.J., Rose J.M., Hess S. Approximation of Bayesian efficiency in experimental choice designs. Journal of Choice Modelling 2008, 1(1):98-127.
13. Bliemer M.C.J., Rose J.M., Hensher D.A. Constructing efficient stated choice experiments allowing for differences in error variances across subsets of alternatives. Transportation Research Part B 2009, 43(1):19-35.
14. Bradley M., Daly A.J. New analysis issues in stated preference research. Perspective 2000, 4. PTRC Education and Research Services Ltd. J.D. Ortuzar (Ed.).
15. Bradley P.C., Thomas A.L. Bayes and Empirical Bayes Methods for Data Analysis 1996, Chapman and Hall.
16. Bunch D.S., Batsell R.R. A Monté Carlo comparison of estimators for the multinomial logit model. Journal of Marketing Research XXVI 1989, 56-68.
17. Chaloner K., Verdinelli I. Bayesian experimental design: a review. Statistical Science 1995, 10(3):273-304.
18. Crabbe M., Vandebroek M. Improving the efficiency of individualized designs for the mixed logit choice model by including covariates. Computational Statistics and Data Analysis 2012, 56(6):2059-2072.
19. Danthurebandara V., Yu J., Vandebroek M. Sequential choice designs to estimate the heterogeneity distribution of willingness-to-pay. Quantitative Marketing and Economics 2011, 9(4):429-448.
20. Ferrini S., Scarpa R. Designs with a priori information for nonmarket valuation with choice experiments: a Monte Carlo study. Journal of Environmental Economics and Management 2007, 53(3):342363.
21. Fowkes A., Shinghal N. The Leeds adaptive stated preference methodology. Freight Transportation Demand and Stated Preference Experiments 2002, FrancoAngeli, Milan, Italy. R. Daniels (Ed.).
22. Garrod G.D., Scarpa R., Willis K.G. Estimating the benefits of traffic calming on through routes: a choice experiment approach. Journal of Transport Economics and Policy 2002, 36(2):211-232.
23. Godoy G., Ortúzar J.de D. On the estimation of mixed logit models. Transportation Research Trends 2008, 289-310. Nova Science Publishers, Hauppauge. P.O. Inweldi (Ed.).
24. Gotwalt C.M., Jones B.A., Steinberg D.M. Fast computation of designs robust to parameter uncertainty for nonlinear settings. Technometrics 2009, 51(1):88-95.
25. Han C., Chaloner K. Bayesian experimental design for nonlinear mixed-effects models with application to HIV dynamics. Biometrics 2004, 60(1):25-33.
26. Hauser J.R., Toubia O. The impact of utility balance and endogeneity in conjoint analysis. Marketing Science 2005, 24(3):498-507.
27. Hensher D.A. The valuation of commuter travel time savings for car drivers: evaluating alternative model specifications. Transportation 2001, 28(2):101-118.
28. Hensher D.A. Measurement of the valuation of travel time savings. Journal of Transport Economics and Policy 2001, 35(1):71-98.
29. Hensher D.A., King J. Parking demand and responsiveness to supply, pricing and location in the Sydney central business district. Transport Research Part A 2001, 35(3):177-196.
30. Hess, S., Bain, S., Rose, J.M., 2009. Random scale heterogeneity in discrete choice models. In: European Transport Conference, Leeuwenhorst, October 5-7.
31. Hess S., Bierlaire M., Polak J.W. Estimation of value of travel-time savings using mixed logit models. Transportation Research Part A 2005, 39(2-3):221-236.
32. Hess, S., Rose, J.M., 2009. Some lessons in stated choice survey design. In: European Transport Conference, Leeuwenhorst, October 5-7.
33. Hickernell F.J., Hong H.S., L'Ecuyer P., Lemieux C. Extensible lattice sequences for quasi-Monte Carlo quadrature. SIAM Journal on Scientific Computing 2000, 22(3):1117-1138.
34. Hojman P., Ortúzar J.de D., Rizzi L.I. On the joint valuation of averting fatal victims and severe injuries in highway accidents. Journal of Safety Research 2005, 36(4):377-386.
35. Jennings D.E. Judging inference adequacy in logistic regression. Journal of the American Statistical Association 1986, 81(394):471-476.
36. Kessels R., Goos P., Vandebroek M. A comparison of criteria to design efficient choice experiments. Journal of Marketing Research 2006, 43(3):409-419.
37. Kessels R., Jones B., Goos P., Vandebroek M. An efficient algorithm for constructing Bayesian optimal choice designs. Journal of Business and Economic Statistics 2009, 27(2):279-291.
38. Koop G., Poirier D.J. Bayesian analysis of logit models using natural conjugate priors. Journal of Econometrics 1993, 56(3):323-340.
39. Lindley D.V. On the measure of information provided by an experiment. The Annals of Mathematical Statistics 1956, 27(4):986-1005.
40. Maggi R., Rudel R. The value of quality attributes in freight transport: evidence from an SP-experiment in Switzerland. Recent Developments in Transport Modelling 2008, 179-190. Emerald Group Publishing, New York.
41. McFadden D. Conditional logit analysis of qualitative choice behavior. Frontiers in Econometrics 1974, 105-142. Academic Press, New York. P. Zarembka (Ed.).
42. McCulloch R., Rossi P.E. An exact likelihood analysis of the multinomial probit model. Journal of Econometrics 1994, 64:207-240.
43. Merlé Y., Mentré F. Bayesian design criteria: computation, comparison, and application to a pharmacokinetic and a pharmacodynamic model. Journal of Pharmacokinetics and Biopharmaceutics 1995, 23(1):101-125.
44. Meyer R.K., Nachtsheim C.J. The coordinate-exchange algorithm for constructing exact optimal experimental designs. Technometrics 1995, 37(1):60-69.
45. Monahan J., Genz A. Spherical-radial integration rules for Bayesian computation. Journal of the American Statistical Association 1997, 92(438):664-674.
46. Ortúzar J.de D., Iacobelli A., Valeze C. Estimating demand for a cycle-way network. Transportation Research Part A 2000, 34(5):353-373.
47. Rizzi L.I., Ortúzar J.de D. Stated preference in the valuation of interurban road safety. Accident Analysis and Prevention 2003, 35(1):9-22.
48. Rose, J.M., Bliemer, M.C.J., 2005. Constructing efficient choice experiments. Working paper ITLS-WP-05-07, Institute of Transport and Logistics Studies Sydney.
49. Rose, J.M., Bliemer, M.C.J., 2006. Designing efficient data for stated choice experiments. Paper presented at the 11th International Conference on Travel Behaviour Research, Kyoto, Japan.
50. Rose J.M., Bliemer M.C.J. Stated preference experimental design strategies. Handbooks in Transport 2007, vol. 1. Elsevier Science, Oxford, (Chapter 8). second ed. D.A. Hensher, K. Button (Eds.).
51. Rose J.M., Bliemer M.C.J., Hensher D.A., Collins A.C. Designing efficient stated choice experiments involving respondent based reference alternatives. Transportation Research Part B 2008, 42(4):395-406.
52. Rossi P.E., Allenby G.M. Bayesian statistics and marketing. Marketing Science 2003, 22(3):304-328.
53. Rossi P.E., Allenby G.M., McCulloch R. Bayesian Statistics and Marketing 2005, Wiley, New York.
54. Sándor Z., András P.P. Alternative sampling methods for estimating multivariate normal probabilities. Journal of Econometrics 2004, 120(2):207-234.
55. Sándor Z., Wedel M. Designing conjoint choice experiments using managers' prior beliefs. Journal of Marketing Research 2001, 38(4):430-444.
56. Sándor Z., Wedel M. Heterogeneous conjoint choice designs. Journal of Marketing Research 2005, 42(2):210-218.
57. Scarpa R., Rose J.M. Design efficiency for non-market valuation with choice modelling: how to measure it, what to report and why. The Australian Journal of Agricultural and Resource Economics 2008, 52(3):253282.
58. Scarpa R., Campbell D., Hutchinson W.G. Benefit estimates for landscape improvements: sequential Bayesian design and respondents rationality in a choice experiment. Land Economics 2007, 83(4):617-634.
59. Sebastiani P., Wynn H.P. Bayesian experimental design and Shannon information. Proceedings of the Section on Bayesian Statistical Science. 1997, 176-181.
60. Shannon C.E. A mathematical theory of communication. Bell System Technical Journal 1948, 27(3):379-423.
61. Shannon C.E. A mathematical theory of communication. Bell System Technical Journal 1948, 27(4):623-656.
62. Shiflan Y., Bard-Eden R. Modeling response to parking policy. Transport Research Record 2001, 1765:27-34.
63. Solgaard H.S., Hansen T. A hierarchical Bayes model of choice between supermarket formats. Journal of Retailing and Consumer Services 2003, 10(3):169180.
64. Sun D., Tsutakawa R.K. Bayesian design for dose-response curves with penalized risk. Biometrics 1997, 53(4):1262-1273.
65. Toubia O., Hauser J.R., Simester D.I. Polyhedral methods for adaptive choice-based conjoint analysis. Journal of Marketing Research 2004, 41(1):116-131.
66. Toubia O., Hauser J., Garcia R. Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: theory and application. Marketing Science 2007, 26(5):596-610.
67. Train K. Discrete Choice Methods with Simulation 2003, Cambridge University Press.
68. Train K., Wilson W.W. Estimation on stated-preference experiments constructed from revealed-preference choices. Transportation Research Part B 2008, 42(3):191-203.
69. Tsutakawa R.K. Selection of dose levels for estimating a percentage point of a logistic quantal response curve. Applied Statistics 1980, 29(1):25-33.
70. van der Waerden P., Timmermans H., Borgers A. PAMELA: parking analysis model for predicting effects in local areas. Transport Research Record 2002, 1781:10-18.
71. Vermeulen B., Goos P., Vandebroek M. Models and optimal designs for conjoint choice experiments including a no-choice option. International Journal of Research in Marketing 2008, 25(2):94-103.
72. Washington S., Congdon P., Karlaftis M.G., Mannering F.L. Bayesian multinomial logit: theory and route choice example. Transportation Research Record 2009, 2136:28-36.
73. Yu J., Goos P., Vandebroek M. Efficient conjoint choice designs in the presence of respondent heterogeneity. Marketing Science 2009, 28(1):122-135.
74. Yu J., Goos P., Vandebroek M. Comparing different sampling schemes for approximating the integrals involved in the efficient design of stated choice experiments. Transportation Research Part B 2010, 44(10):1268-1289.
75. Yu J., Goos P., Vandebroek M. Individually adapted sequential Bayesian designs for conjoint choice experiments. International Journal of Research in Marketing 2011, 28(4):378-388.