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

A test for randomness

P C O'Brien

    Biometrics
    |June 1, 1976
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new statistical procedure to test if random variables are independent and identically distributed, particularly sensitive to clustering. It also examines the sample coefficient of variation for continuous data randomness testing.

    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

    Risk of dementia in MCI: combined effect of cerebrovascular disease, volumetric MRI, and 1H MRS.

    Neurology·2009
    Same author

    Atrophy rates accelerate in amnestic mild cognitive impairment.

    Neurology·2007
    Same author

    Chromosome painting and molecular dating indicate a low rate of chromosomal evolution in golden moles (Mammalia, Chrysochloridae).

    Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology·2006
    Same author

    Polyneuropathy dysfunction scores.

    Journal of neurology, neurosurgery, and psychiatry·2006
    Same author

    Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI.

    Neurology·2005
    Same author

    A randomized controlled crossover trial of aspirin for fatigue in multiple sclerosis.

    Neurology·2005
    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

    Area of Science:

    • Statistics
    • Biostatistics
    • Probability Theory

    Background:

    • Hypothesis testing for random variable distribution is crucial in statistical analysis.
    • Identifying departures from independent and identically distributed (i.i.d.) assumptions is vital for model validity.
    • Clustering patterns can indicate non-randomness in data sequences.

    Purpose of the Study:

    • To propose a novel statistical procedure for testing the hypothesis of independent and identically distributed (i.i.d.) dichotomous random variables.
    • To assess the sensitivity of the proposed procedure to departures from randomness, specifically multiple clustering.
    • To explore the distribution of the sample coefficient of variation from an exponential distribution for continuous data randomness testing.

    Main Methods:

    Related Experiment Videos

  • Development of a new statistical procedure for hypothesis testing on dichotomous random variables.
  • Evaluation of the procedure's sensitivity to deviations from i.i.d. assumptions, focusing on clustering.
  • Analysis of the sample coefficient of variation's distribution under an exponential model.

    • Main Results:

    • The proposed procedure demonstrates notable sensitivity to departures from randomness characterized by multiple clustering.
    • The study provides insights into the behavior of the sample coefficient of variation for continuous data.
    • An application of the procedure in testing for Schwann cell disease is presented.

    Conclusions:

    • The new statistical procedure offers a sensitive method for detecting non-i.i.d. patterns in dichotomous data, especially clustering.
    • The research contributes to statistical methods for randomness testing in both discrete and continuous data.
    • The findings have potential implications for disease testing and data analysis in biostatistics.