Explicit estimators of parametric functions in nonlinear regression

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

When repetitive estimations are to be made under field conditions using data that follow a nonlinear regression law, a simple polynomial function of the observations has considerable appeal as an estimator. The polynomial estimator of finite degree with smallest average mean squared error is found. Conditions are given such that as degree increases it converges in probability to the Bayes estimator and its average mean squared error converges to the lower bound of all square integrable estimators. In an example, a linear estimator performs better than the maximum likelihood estimator and nearly as well as the Bayes estimator.

Original languageEnglish (US)
Pages (from-to)182-193
Number of pages12
JournalJournal of the American Statistical Association
Volume75
Issue number369
DOIs
StatePublished - Jan 1 1980

Fingerprint

Nonlinear Regression
Bayes Estimator
Estimator
Mean Squared Error
Converge
Linear Estimator
Appeal
Polynomial function
Maximum Likelihood Estimator
Lower bound
Polynomial
Nonlinear regression

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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Explicit estimators of parametric functions in nonlinear regression. / Gallant, Andrew Ronald.

In: Journal of the American Statistical Association, Vol. 75, No. 369, 01.01.1980, p. 182-193.

Research output: Contribution to journalArticle

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