Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Saddlepoint distribution function approximations in biostatistical inference.

J E Kolassa1

  • 1Department of Statistics, Rutgers University, Piscataway, NJ 08854-8019 USA. kolassa@stat.rutgers.edu

Statistical Methods in Medical Research
|March 6, 2003
PubMed
Summary

Saddlepoint approximations offer efficient methods for calculating distribution functions. These techniques are valuable for statistical inference, including hypothesis testing and confidence intervals in exponential families.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Approximate Monte Carlo conditional inference in exponential families.

Biometrics·2001
Same author

Small-sample confidence regions in exponential families.

Biometrics·2001
Same author

Response of C-reactive protein and serum amyloid A to influenza A infection in older adults.

The Journal of infectious diseases·2001
Same author

Comparison of respiratory syncytial virus humoral immunity and response to infection in young and elderly adults.

Journal of medical virology·1999
Same author

Evaluation of a handwashing intervention to reduce respiratory illness rates in senior day-care centers.

Infection control and hospital epidemiology·1999
Same author

Changes in alveolar septal border lengths with postnatal lung growth.

The American journal of physiology·1998

Area of Science:

  • Statistics
  • Computational Statistics

Background:

  • Distribution functions are fundamental in statistical analysis.
  • Saddlepoint approximations provide accurate approximations for complex distributions.

Purpose of the Study:

  • To review and demonstrate applications of saddlepoint approximations.
  • To apply these methods to marginal and conditional distributions.
  • To utilize saddlepoint approximations for statistical testing and confidence interval construction.

Main Methods:

  • Review of existing literature on saddlepoint approximations.
  • Derivation of saddlepoint approximations for marginal and conditional distributions.
  • Application to canonical exponential families.

Main Results:

Related Experiment Videos

  • Demonstrated accuracy of saddlepoint approximations for distribution functions.
  • Provided calculations for marginal and conditional distributions.
  • Successful application in testing and confidence interval generation.

Conclusions:

  • Saddlepoint approximations are a powerful tool for statistical inference.
  • These methods are particularly effective within canonical exponential families.
  • The study highlights the utility of saddlepoint approximations in practical statistical problems.