Function approximation for incompletely specified regression models

Viktor Brailovsky*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This report extends earlier work by Brailovsky on regression theory and methodology, giving particular emphasis to function approximation for incompletely specified models. The interest here is with situations where the form of the regression relation is not known in advance. We discuss several difficulties that arise in using local approximation and linear regression methods, and propose ways to overcome these problems. To aid the data analyst in developing a suitable model, an illustrative table is derived for determining the number of initial explanatory functions justifiable for a given prespecified confidence level. The general approach formulated here is illustrated with an application to medical data. Relevance to classification and possible extensions are discussed.

Original languageEnglish
Pages (from-to)89-99
Number of pages11
JournalJournal of Classification
Issue number1
StatePublished - Mar 1988
Externally publishedYes


  • Function approximation
  • Nonlinear models
  • Subset regression


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