Nonparametric Tests for Multiple Regression Under Progressive Censoring.

Abstract

For continuous observations from time-sequential studies, suitable Cramer-von Mises and Kolmogorov-Smirnov type (nonparametric) statistics (based on linear rank statistics) for testing hypotheses on some multiple regression models are proposed and studied. Asymptotic theory of these tests is provided for both the null and (local) alternative hypotheses situations and is based on the weak convergence of suitable rank order processes (on the D(0,1) space) to certain functions of Brownian Motions. Bahadur efficiency results are also presented. Empirical values of the percentile points of the null distributions of the proposed test statistics, obtained through simulation studies, are also provided. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1976
Accession Number
ADA036398

Entities

People

  • Hiranmay Majumdar
  • Pranab Kumar Sen

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Confidence Limits
  • Convergence
  • Data Science
  • Distribution Theory
  • Efficiency
  • Hypotheses
  • Information Science
  • Insensitive Explosives
  • New York
  • North Carolina
  • Observation
  • Probability
  • Random Variables
  • Simulations
  • Statistics
  • Weak Convergence

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • Space