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Choice experiments for estimating main effects and interactions
Authors:Jing Chen
Affiliation:a Verisk Health, United States
b Department of Statistics, Temple University, 341, Speakman Hall, 1801 N. 13th Street, Philadelphia, PA 19122, United States
Abstract:Choice-based conjoint experiments are used when choice alternatives can be described in terms of attributes. The objective is to infer the value that respondents attach to attribute levels. This method involves the design of profiles on the basis of attributes specified at certain levels. Respondents are presented sets of profiles and asked to select the one they consider best. However if choice sets have too many profiles, they may be difficult to implement. In this paper we provide strategies for reducing the number of profiles in choice sets. We consider situations where only a subset of interactions is of interest, and we obtain connected main effect plans with smaller choice sets that are capable of estimating subsets of two-factor and three-factor interactions in 2n and 3n plans. We also provide connected main effect plans for mixed level designs.
Keywords:Factorial experiments   Choice experiments   Pareto optimal subset
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[3]、Thomas J. Santner.Designing two factor experiments for selecting interactions[J].Journal of statistical planning and inference,1981,5(1):45-55.
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[5]、Designs for first-order interactions in paired comparison experiments with two-level factors[J].Journal of statistical planning and inference
[6]、FINNEY DJ.Main effects and interactions[J].Journal of the American Statistical Association,1948,43(244):566-571.
[7]、Samuel Rosa,Radoslav Harman.Optimal approximate designs for estimating treatment contrasts resistant to nuisance effects[J].Statistical Papers,2016,57(4):1077-1106.
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[9]、Arthur G. Holms.Chain pooling model selection for two-level fixed effects factorial experiments[J].统计学通讯:模拟与计算,2013,42(1):51-71.
[10]、Chebg-Tao Chang,Joanna Cheng.Three-factor symmetrical balanced designs with full efficiency for main effects[J].统计学通讯:理论与方法,2013,42(12):4757-4772.
[11]、J. Arthur Woodward ,Douglas G. Bonett.Simple main effects in factorial designs[J].Journal of applied statistics,1991,18(2):255-264.
[12]、S. M. Lewis,& A. M. Dean.Detection of interactions in experiments on large numbers of factors[J].Journal of the Royal Statistical Society. Series B, Statistical methodology,2001,63(4):633-672.
[13]、Taka-aki Shiraishi.Studentized robust statistics for,main effects in a two-factor manova[J].统计学通讯:理论与方法,2013,42(3-4):809-823.
[14]、J. E. Walsh,G. J. Kelleher.Nonparametric models and results for two-way ANOVA with fixed effects and interactions[J].Statistical Papers,1974,15(2-3):116-129.
[15]、Charles J. Monlezun.Addendum to fitting polynomials to main effects in multi factor experiments: A note concerning polynomial coefficients when all factors are quantitative[J].统计学通讯:理论与方法,2013,42(12):2915-2919.
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