Local Likelihood Estimation

Abstract

This paper extends the idea of local averaging to likelihood based models. One such application is to the class of generalized linear models. The author enlarges this class by replacing the covariate from chi beta with an unspecified smooth function. This function is estimated from the data by a technique called Local Likelihood Estimation - a type of local averaging. Multiple covariates are incorporated through a forward stepwise algorithm. The main application discussed however, is to the proportional hazards model of Cox (1972), for censored data, In a number of real data examples, the local likelihood technique proves to be effective in uncovering non-linear dependencies. Finally, the author gives some asymptotic results for local likelihood estimates and provides some methods for inference.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1984
Accession Number
ADA147317

Entities

People

  • Robert Tibshirani

Organizations

  • Stanford University

Tags

DTIC Thesaurus Topics

  • Algorithms

Fields of Study

  • Mathematics

Readers

  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms