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

Testing specific hypotheses by fitting underlying distributions to categorical data

W D Johnson1, R C Elston, A R Wickremasinghe

  • 1Department of Biometry and Genetics, Louisiana State University Medical Center, New Orleans 70112-1393.

Journal of Biopharmaceutical Statistics
|March 1, 1994
PubMed
Summary
This summary is machine-generated.

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

Prevalence of Chronic Obstructive Pulmonary Disease (COPD) in Sri Lankan adults: results of a cross sectional study.

BMC pulmonary medicine·2026
Same author

Longitudinal study of knee pain amongst workers in the Cultural and Psychosocial Influences on Disability (CUPID) study.

BMC musculoskeletal disorders·2025
Same author

Ecology of healthcare in an urban and rural area of Gampaha district of Sri Lanka: a community-based prospective study on symptom prevalence and healthcare utilization.

BMC health services research·2025
Same author

Psychometric properties of the Warwick Edinburgh Mental Well-being Scale: a systematic review.

Systematic reviews·2025
Same author

Cardiovascular mortality of 40-69-year-olds in Sri Lanka from 1980 to 2010: a birth cohort analysis by age and sex.

BMJ open·2025
Same author

Chronic obstructive Pulmonary Disease (COPD) in non-smoking Sri Lankan adults; a cross-sectional study.

BMC research notes·2025
Same journal

Correction.

Journal of biopharmaceutical statistics·2026
Same journal

Leveraging external controls in clinical trials: estimands, estimation, assumptions.

Journal of biopharmaceutical statistics·2026
Same journal

Special issue of nonclinical statistics in regulatory applications guest editors' notes.

Journal of biopharmaceutical statistics·2026
Same journal

Comparison of flexible parametric modeling and nonparametric methods to estimate restricted mean survival time: A simulation study.

Journal of biopharmaceutical statistics·2026
Same journal

Simulated treatment comparisons with jackknife pseudo values for estimating population-adjusted marginal treatment effects.

Journal of biopharmaceutical statistics·2026
Same journal

Sample sizes for randomized controlled trials utilizing Bayesian response adaptive randomization for continuous outcomes.

Journal of biopharmaceutical statistics·2026
See all related articles

This study introduces maximum-likelihood estimation and likelihood ratio tests for ordered categorical data. It focuses on fitting mixture distributions, particularly normal mixtures, and includes goodness-of-fit tests for statistical models.

Area of Science:

  • Statistical analysis
  • Probability theory
  • Data modeling

Background:

  • Estimating parameters and testing hypotheses for categorical data is a significant challenge in statistical analysis.
  • Existing methods often rely on linear models and weighted least squares for multinomial data.
  • There is a need for robust methods to handle ordered categorical responses with complex underlying distributions.

Purpose of the Study:

  • To present maximum-likelihood estimation and likelihood ratio tests for ordered categorical response variates.
  • To emphasize the fitting and inference of mixture distribution parameters, especially normal mixtures.
  • To provide goodness-of-fit tests for assessing distributional model adequacy.

Main Methods:

  • Maximum-likelihood estimation (MLE) for ordered categorical data.

Related Experiment Videos

  • Likelihood ratio tests (LRTs) for hypothesis testing.
  • Fitting and inference for mixture distributions, including normal mixtures.
  • Development of goodness-of-fit tests.

    • Main Results:

    • The proposed methods enable effective parameter estimation and hypothesis testing for ordered categorical data.
    • The approach allows for flexible modeling using discrete or continuous underlying distributions.
    • Mixture distributions, particularly normal mixtures, can be effectively fitted and analyzed.
    • Goodness-of-fit tests validate the chosen distributional models.

    Conclusions:

    • Maximum-likelihood estimation and likelihood ratio tests offer a powerful framework for analyzing ordered categorical data.
    • The methods are particularly useful for modeling complex data structures using mixture distributions.
    • The inclusion of goodness-of-fit tests ensures the reliability of statistical inferences.