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

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.

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Document Details

Document Type
Technical Report
Publication Date
Dec 04, 1992
Accession Number
ADA258777

Entities

People

  • Alan E. Gelfand
  • Dipak K. Dey
  • Hong Chang

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayesian Inference
  • Bayesian Networks
  • Computational Science
  • Computations
  • Data Analysis
  • Data Mining
  • Data Science
  • Estimators
  • Information Science
  • Monte Carlo Method
  • New York
  • Normal Distribution
  • Probability
  • Sampling
  • Statistical Algorithms
  • Statistical Analysis
  • Surveys

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Statistical inference.
  • Theoretical Analysis.