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One Size Fits All or Custom Tailored: Which HB Fits Better? (2001)

The authors (Keith Sentis and Lihua Li) investigated whether the assumption in HB of a single multivariate normal distribution to reflect the population negatively affected the estimated utilities if segments existed with quite different utilities. The authors studied seven actual CBC data sets, systematically excluding some of the tasks to serve as holdouts for internal validation. Keith and Lihua found that whether one ran HB on the entire sample, or whether one segmented first (K-means, demographic, or Latent Class) prior to estimating utilities, the upper-level model assumption in HB of normality did not decrease the fit of the estimated utilities to the holdouts. It seemed unnecessary to segment first before running HB.

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