Model Determination using Predictive Distributions with Implementation via Sampling-Based Methods

作者：Alan E. Gelfand, Dipak K. Dey, Haibin Chang · 年份：1992 · DOI：10.1093/oso/9780198522669.003.0009 · 被引用次数：670 · 研究领域：Probabilistic and Robust Engineering Design、Optimal Experimental Design Methods、Soil Geostatistics and Mapping

Abstract Model determination is divided into the issues of model adequacy and model selection. Predictive distributions are used to address both issues. This seems natural since, typically, prediction is a primary purpose for the chosen model. A cross-validation viewpoint is argued for. In particular, for a given model, it is proposed to validate conditional predictive distributions arising from single point deletion against observed responses. Sampling based methods are used to carry out required calculations. An example investigates the adequacy of and rather subtle choice between two sigmoidal growth models of the same dimension.