Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
  1. Home
  2. Probability Theory and Related Fields
  3. Article
Model testing for multivariate censored data
Download PDF
Download PDF
  • Published: 01 October 2002

Model testing for multivariate censored data

Part I: Simple null hypotheses

  • Arkady A. Tempelman1 &
  • Michael G. Akritas1 

Probability Theory and Related Fields volume 106, pages 351–369 (1996)Cite this article

  • 70 Accesses

  • 3 Citations

  • Metrics details

Summary.

In the fields like Astronomy and Ecology, the need for proper statistical analysis of data that are censored is being increasingly recognized. Such data occur when, due to noise or other factors, instruments fail to detect low luminosities of celestial objects, or low concentrations of certain pollutants. For multivariate censored data sets there are very few distribution free methods available and researchers in the various fields often impose an assumption on the joint distribution, such as multivariate normality, and carry out parametric inferences. Under censoring, however, such parametric inferences are asymptotically wrong if the imposed assumption is incorrect. In this paper we propose a class of goodness-of-fit procedures for testing assumptions about the multivariate distribution under random censoring. The test procedures generalize Pearson's goodness-of-fit test in the sense that they are based on the concept of observed-minus-expected frequencies. The theory of the test statistic, however, differs from that for the classical Pearson test due to the accommodation of censored data.

Download to read the full article text

Working on a manuscript?

Avoid the common mistakes

Author information

Authors and Affiliations

  1. Department of Statistics, Pennsylvania State University, 326 COB University Park, PA 16082, USA (e-mail: arkady@stat.psu.edu), 16082, Park, PA, US

    Arkady A. Tempelman & Michael G. Akritas

Authors
  1. Arkady A. Tempelman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael G. Akritas
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Received: 24 May 1994 / In revised form: 3 March 1996

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Tempelman, A., Akritas, M. Model testing for multivariate censored data. Probab Theory Relat Fields 106, 351–369 (1996). https://doi.org/10.1007/s004400050068

Download citation

  • Published: 01 October 2002

  • Issue Date: November 1996

  • DOI: https://doi.org/10.1007/s004400050068

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Mathematics Subject Classification (1991):62F03, 62H15
Download PDF

Working on a manuscript?

Avoid the common mistakes

Advertisement

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature