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

Semiparametric maximum likelihood for measurement error model regression.

D W Schafer1

  • 1Department of Statistics, Oregon State University, Corvallis 97331-4606, USA. schafer@stat.orst.edu

Biometrics
|March 17, 2001
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

Direct experimental observation of the gas density depression effect using a two-bunch X-ray FEL beam.

Journal of synchrotron radiation·2017
Same author

Application of fecal near-infrared spectroscopy and nutritional balance software to monitor diet quality and body condition in beef cows grazing Arizona rangeland.

Journal of animal science·2013
Same author

Effects of a long-acting, trace mineral, reticulorumen bolus on range cow productivity and trace mineral profiles.

Journal of animal science·2006
Same author

Thyroid cancer following scalp irradiation: a reanalysis accounting for uncertainty in dosimetry.

Biometrics·2001
Same author

Thyroid cancer after scalp irradiation: a reanalysis accounting for uncertainty in dosimetry.

Radiation research·2001
Same author

Hypnosis and the treatment of ulcerative colitis and Crohn's disease.

The American journal of clinical hypnosis·1998
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 an EM algorithm for regression models with measurement errors. The semiparametric approach efficiently estimates parameters, outperforming incorrect distributional assumptions.

Area of Science:

  • Statistics
  • Econometrics
  • Biostatistics

Background:

  • Regression models are fundamental in statistical analysis.
  • Measurement errors in explanatory variables can bias results.
  • Existing methods often require strong distributional assumptions.

Purpose of the Study:

  • To develop a robust statistical method for regression analysis with measurement errors.
  • To address limitations of traditional methods by employing semiparametric approaches.
  • To provide an efficient estimation algorithm for complex regression models.

Main Methods:

  • An Expectation-Maximization (EM) algorithm is proposed.
  • Semiparametric likelihood analysis is employed for linear, generalized linear, and nonlinear models.

Related Experiment Videos

  • Nonparametric maximum likelihood estimation is used for unspecified distributions of true explanatory variables.

    • Main Results:

    • The semiparametric maximum likelihood estimator demonstrates high efficiency.
    • The proposed method outperforms maximum likelihood with incorrect distributional assumptions.
    • Simulations confirm the estimator's performance across various model structures.

    Conclusions:

    • The developed EM algorithm offers a flexible and efficient solution for regression with measurement errors.
    • This semiparametric approach provides a valuable alternative when distributional forms are unknown.
    • The method is applicable to diverse regression models and data types.