Penalized Likelihood for General Semi-Parametric Regression Models.

Abstract

This paper examines maximum penalized likelihood estimation in the context of general regression problems, characterized as probability models with composite; likelihood functions. The emphasis is on the common situation where a parametric model is considered satisfactory but for inhomogeneity with respect to a few extra variables. A finite-dimensional formulation is adopted, using a suitable set of basis functions. Appropriate definitions of deviance, degrees of freedom, and residual are provided, and the method of cross-validation for choice of the tuning constant is discussed. Quadratic approximations are derived for all the required statistics. Additional keywords: algorithms; smoothing; goodness of fit tests; nonlinear repression. (Author)

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1985
Accession Number
ADA158133

Entities

People

  • P. J. Green

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Composite Materials
  • Data Science
  • Distribution Functions
  • Information Science
  • Mathematics
  • Maximum Likelihood Estimation
  • Normal Distribution
  • Probability
  • Random Variables
  • Residuals
  • Roughness
  • Statistical Analysis
  • Statistics
  • United States
  • Validation

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Regression Analysis.