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Analyzing Real Estate Data Problems Using the Gibbs Sampler
Author(s) -
Knight John R.,
Sirmans C.F.,
Gelfand Alan E.,
Ghosh Sujit K.
Publication year - 1998
Publication title -
real estate economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.064
H-Index - 61
eISSN - 1540-6229
pISSN - 1080-8620
DOI - 10.1111/1540-6229.00753
Subject(s) - censoring (clinical trials) , gibbs sampling , inference , missing data , econometrics , computer science , truncation (statistics) , real estate , observational error , data mining , statistics , economics , mathematics , machine learning , artificial intelligence , bayesian probability , finance
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.