A Note on Estimating The Number of Super Imposed Exponential Signals by the Cross-Validation Approach

Abstract

In this paper, a procedure based on the delete-1 cross-validation is given for estimating the number of super imposed exponential signals, its limiting behavior is explored and it is shown that the probability of overestimating the true number of signals is larger than a positive constant for sample size large enough. Also a general procedure based on the cross-validation is presented when the deletion precedes according to a collection of subsets of indices. The result is similar to the delete-1 cross-validation if the number of deletion is fixed. The simulation results are provided for the performance of the procedure when the collections of subsets of indices are chosen as those suggested by 7 in a linear model selection problem.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Aug 25, 1999
Accession Number
ADA369860

Entities

People

  • Fangxing Li
  • K. W. Tam
  • M. M. Zen
  • Yipeng Wu

Organizations

  • Pennsylvania State University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Abstracts
  • Bayesian Networks
  • Computational Science
  • Data Science
  • Eigenvalues
  • Eigenvectors
  • Governments
  • Information Science
  • Information Theory
  • Models
  • Monte Carlo Method
  • Multivariate Analysis
  • Probability
  • Signal Processing
  • Simulations
  • United States Government
  • Validation

Readers

  • Regression Analysis.