Nonparametric Inference in Additive Risk Models for Counting Processes.

Abstract

Nonparametric estimators for the hazard functions in an additive risk model for counting process are studied. This document establishes a functional central limit theorem for the integrated estimators and show how this can be used to find the asymptotic null distribution of a maximal deviation statistic for Kolmogorov-Smirnov type testing. In addition, the author provides confidence bands f or approximations to the integrated hazard functions and show that certain smoothed versions of the hazard function estimators are uniformly consistent. Keywords: Martingale methods; Regression models. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1986
Accession Number
ADA173498

Entities

People

  • Ian W. Mckeague

Organizations

  • Florida State University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Additives (Chemicals)
  • Algorithms
  • Asymptotic Normality
  • Data Science
  • Estimators
  • Inequalities
  • Information Science
  • Integrals
  • Mathematics
  • New York
  • Probability
  • Random Variables
  • Statistical Algorithms
  • Statistical Inference
  • Statistics
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

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