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

Modeling schizophrenic behavior using general mixture components

D B Rubin1, Y N Wu

  • 1Department of Statistics, Harvard University, Science Center, Cambridge, Massachusetts 02138, USA.

Biometrics
|March 1, 1997
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

[Cross-host transmission of bacterial antibiotic resistance: research progress on ecological pattern and mechanism].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]·2026
Same author

[Stapled closure of the internal fistula orifice in anal fistula for high complex anal fistula].

Zhonghua wei chang wai ke za zhi = Chinese journal of gastrointestinal surgery·2025
Same author

Counternull sets in randomized experiments.

The American statistician·2025
Same author

[Association between remnant cholesterol and the risk of atherosclerotic cardiovascular disease in a community population in Shanghai].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2025
Same author

[Mediating effects of cardiovascular health status in association between educational level and cardiovascular disease].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2025
Same author

[Application of the Brillouin optical scanning system to investigate the biomechanical properties of the myopic lens and their changes before and after cycloplegia].

[Zhonghua yan ke za zhi] Chinese journal of ophthalmology·2025
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

This study introduces a new statistical model for analyzing schizophrenic behavior, specifically eye-tracking patterns. The model reveals significant differences between schizophrenic and nonschizophrenic individuals.

Area of Science:

  • Psychological statistics
  • Behavioral science
  • Statistical modeling

Background:

  • Schizophrenia research often faces challenges in statistical modeling.
  • Existing models may not fully capture complex behavioral patterns.

Purpose of the Study:

  • To propose a novel mixture component model for analyzing psychological data.
  • To apply this model to understand schizophrenic eye-tracking behavior.

Main Methods:

  • Developed a hierarchical mixture component model with random effects.
  • Utilized ANOVA-like linear regressions and logistic regressions for model components.
  • Employed ECM/SECM algorithms and Gibbs sampling for model fitting and inference.

Main Results:

Related Experiment Videos

  • Successfully fitted the model to eye-tracking data from schizophrenic and nonschizophrenic individuals.
  • Posterior predictive p-values indicated the necessity of all mixture components.
  • Identified scientifically revealing patterns in eye-tracking behavior.

Conclusions:

  • The proposed mixture model provides a robust framework for analyzing complex psychological data.
  • The model effectively differentiates between schizophrenic and nonschizophrenic eye-tracking behaviors.
  • The methodology offers a valuable tool for psychological research and statistical analysis.