Skip to main content

Edge Exclusion Tests for Graphical Gaussian Models

  • Chapter
Learning in Graphical Models

Part of the book series: NATO ASI Series ((ASID,volume 89))

Summary

Testing that an edge can be excluded from a graphical Gaussian model is an important step in model fitting and the form of the generalised likelihood ratio test statistic for this hypothesis is well known. Herein the modified profile likelihood test statistic for this hypothesis is obtained in closed form and is shown to be a function of the sample partial correlation. Related expressions are given for the Wald and the efficient score statistics. Asymptotic expansions of the exact distribution of this correlation coefficient under the hypothesis of conditional independence are used to compare the adequacy of the chi-squared approximation of these and Fisher’s Z statistics. While no statistic is uniformly best approximated, it is found that the coefficient of the O(n-1) term is invariant to the dimension of the multivariate Normal distribution in the case of the modified profile likelihood and Fisher’s Z but not for the other statistics. This underlines the importance of adjusting test statistics when there are large numbers of variables, and so nuisance parameters in the model.

Similar comparisons are effected for the Normal approximation to the signed square-rooted versions of these statistics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Amari, S-I. (1982). Geometrical theory of asymptotic ancillarity and conditional inference. Biometrika, 69, 1–17.

    Article  MathSciNet  MATH  Google Scholar 

  • Amari, S-I. (1985). Differential-Geometric Methods in Statistics. Lecture Notes in Statistics 28, Springer-Verlag: Heidelberg.

    Google Scholar 

  • Barndorf -Nielsen, O.E. (1978). Information and Exponential Families in Statistical Theory. Wiley: New York.

    MATH  Google Scholar 

  • Barndorff Nielsen, O.E. (1983). On a formula for the distribution of the maximum likelihood estimator. Biometrika, 70, 343–365.

    Article  MathSciNet  MATH  Google Scholar 

  • Barndorff-Nielsen, O.E. (1986). Inference on full or partial parameters based on the standardized signed log likelihood ratio. Biometrika, 73, 307–322.

    MathSciNet  MATH  Google Scholar 

  • Barndorff-Nielsen, O.E. (1988). Parametric Statistical Models and Likelihood. Lecture Notes in Statistics 50, Springer-Verlag: Heidelberg.

    Google Scholar 

  • Barndorff-Nielsen, O.E. (1990a). A note on the standardised signed log likelihood ratio. Scand. J. Statist., 17 157–160.

    MathSciNet  MATH  Google Scholar 

  • Barndorff-Nielsen, O.E. (1990b). Approximate probabilities. J. R. Statist. Soc.B, 52, 485–496.

    MathSciNet  MATH  Google Scholar 

  • Barndorff-Nielsen, O.E. and Cox, D.R. (1989). Asymptotic Techniques for Use in Statistics. Chapman and Hall: London.

    MATH  Google Scholar 

  • Cox, D.R. and Hinkley, D. V. (1974). Theoretical Statistics. Chapman and Hall: London.

    MATH  Google Scholar 

  • Cox, D.R. and Reid, N. (1987). Parameter orthogonality and approximate conditional inference, (with discussion). J. R. Statist. Soc. B, 49, 1–39.

    MathSciNet  MATH  Google Scholar 

  • Cox, D.R. and Wermuth, N. (1990). An approximation to maximum likelihood estimates in reduced models. Biometrika, 77, 747–761.

    Article  MathSciNet  MATH  Google Scholar 

  • Davison, A. C. (1988). Approximate conditional inference in generalised linear models. J. R. Statist. Soc. B, 50, 445–462.

    MathSciNet  Google Scholar 

  • Davison, A. C., Smith, P.W.F. and Whittaker, J. (1991). An exact conditional test for covariance selection. Austral. J. Statist., 33, 313–318.

    Article  Google Scholar 

  • Deemer, W.L. and O1kin, O. (1951). The jacobians of certain matrix trans-formations useful in multivariate analysis. Biometrika, 38, 345–367.

    MathSciNet  MATH  Google Scholar 

  • Dempster, A.P. (1972). Covariance selection. Biometrics, 28, 157–175.

    Article  Google Scholar 

  • Efron, B. (1978). The geometry of exponential families Ann. Statist., 6, 362–376.

    Article  MathSciNet  MATH  Google Scholar 

  • Fisher, R.A. (1970). Statistical Methods for Research Workers, 14th Edition. Hafner Press: New York.

    Google Scholar 

  • Fraser, D.A.S. (1991). Statistical inference: likelihood to significance. J. Amer. Statist. Soc., 86, 258–265.

    Article  MathSciNet  MATH  Google Scholar 

  • Frydenburg, M. and Jensen, J.L. (1989). Is the ‘improved likelihood ratio statistic’ really improved for the discrete case? Biometrika, 76, 655–661.

    MathSciNet  Google Scholar 

  • Graybill, F.A. (1983). Matrices with Applications in Statistics. 2nd Edition. Wadsworth: California.

    MATH  Google Scholar 

  • Harris, P. and Peers, H.W. (1980). The local power of the efficient score test statistic. Biometrika, 67, 525–529.

    Article  MathSciNet  MATH  Google Scholar 

  • Hayakawa, T. (1975). The likelihood ratio criterion for a composite hypothesis under a local alternative. Biometrika, 62, 451–460.

    Article  MathSciNet  MATH  Google Scholar 

  • Isserlis, L. (1918). On a formula for the product-moment coefficient of any order of a normal frequency distribution in any number of variables. Biometrika, 12, 134–139.

    Google Scholar 

  • Kreiner, S. (1987). Analysis of multi-dimensional contingency tables by exact conditional tests: techniques and strategies. Scand. J. Stat., 14.

    Google Scholar 

  • Lauritzen, S.L. (1989). Mixed graphical association models. Scand. J. Statist., 16, 273–306.

    MathSciNet  MATH  Google Scholar 

  • McCullagh, P. (1987). Tensor Methods in Statistics. Chapman and Hall: London.

    MATH  Google Scholar 

  • Moran, P. A. P. (1970). On asymptotically optimal tests of composite hypotheses. Biometrika, 57, 45–55.

    Google Scholar 

  • Muirhead, R.J. (1982). Aspects of Multivariate Statistical Theory. Wiley: New York.

    Book  MATH  Google Scholar 

  • Pierce, D.A. and Peters, D. (1992). Practical use of higher order asymptotics for multiparameter exponential families (with discussion). J. R. Statist. Soc. B, 54, 701–737.

    MathSciNet  Google Scholar 

  • Smith, P.W.F. (1990). Edge Exclusion Tests for Graphical Models. Unpublished Ph.D. thesis. Lancaster University.

    Google Scholar 

  • Speed, T.P. and Kiiveri, H. (1986). Gaussian Markov distributions over finite graphs. Ann. Statist., 14, 138–150.

    Article  MathSciNet  MATH  Google Scholar 

  • Wermuth, N. (1976). Analogies between multiplicative models in contingency tables and covariance selection. Biometrics, 32, 95–108.

    Article  MathSciNet  MATH  Google Scholar 

  • Whittaker, J. (1990). Graphical Models in Applied Multivariate Statistics. Wiley: Chichester.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Smith, P.W.F., Whittaker, J. (1998). Edge Exclusion Tests for Graphical Gaussian Models. In: Jordan, M.I. (eds) Learning in Graphical Models. NATO ASI Series, vol 89. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5014-9_21

Download citation

  • DOI: https://doi.org/10.1007/978-94-011-5014-9_21

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-6104-9

  • Online ISBN: 978-94-011-5014-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics