Analyzing Real Estate Data Problems Using the Gibbs Sampler
针对房地产数据中常见的缺失、删失和测量误差等问题，提出用吉布斯采样器进行基于模型的模拟拟合，并通过简单示例展示保留部分观测数据能显著改善推断效果。
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.