Analyzing real estate data problems using the Gibbs sampler

Published

Journal Article

Real estate data are often characterized by data irregularities: missing data, censoring or truncation, measurement error, etc. Practitioners often discard missing-or censored-data cases and ignore measurement error. We argue here that an attractive remedy for these irregularity problems is simulation-based model fitting using the Gibbs sampler. The style of the paper is primarily pedagogic, employing a simple illustration to convey the essential ideas, unobscured by implementation complications. Focusing on the missing-data problem, we show dramatic improvement in inference by retaining rather than deleting cases of partially observed data. We also detail Gibbs-sampler usage for other data problems.

Full Text

Duke Authors

Cited Authors

  • Knight, JR; Sirmans, CF; Gelfand, AE; Ghosh, SK

Published Date

  • January 1, 1998

Published In

Volume / Issue

  • 26 / 3

Start / End Page

  • 469 - 492

International Standard Serial Number (ISSN)

  • 1080-8620

Digital Object Identifier (DOI)

  • 10.1111/1540-6229.00753

Citation Source

  • Scopus