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 analysis of truncated data.

J Qin1, M C Wang

  • 1Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York 10021, USA.

Lifetime Data Analysis
|October 27, 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

The developmental pattern of calcium mobilisation during embryogenesis in Muscovy ducks.

British poultry science·2026
Same author

Challenging Spontaneous Quantum Collapse with the XENONnT Dark Matter Detector.

Physical review letters·2026
Same author

Loss-Tolerant Detection of Squeezed States in the 2  μm Region.

Physical review letters·2026
Same author

Black Hole Spectroscopy and Tests of General Relativity with GW250114.

Physical review letters·2026
Same author

[Laparoscopic subtotal colectomy with antiperistaltic cecorectal anastomosis versus laparoscopic total colectomy with ileorectal anastomosis for slow transit constipation: a multicenter retrospective cohort study].

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

WIMP Dark Matter Search Using a 3.1 Tonne-Year Exposure of the XENONnT Experiment.

Physical review letters·2025
Same journal

Shared frailty sieve estimation for dependent left truncated and interval censored data.

Lifetime data analysis·2026
Same journal

Functional win-fractions regression models for composite outcomes.

Lifetime data analysis·2026
Same journal

Variable selection in causal semiparametric transformation models with all-or-nothing treatment compliance.

Lifetime data analysis·2026
Same journal

Correction to: A uniformisation-driven algorithm for inference-related estimation of a phase-type ageing model.

Lifetime data analysis·2026
Same journal

Unobserved heterogeneity in threshold regression based on the hitting times of a reflected Brownian motion for recurrent hypoglycemia.

Lifetime data analysis·2026
Same journal

Variable selection with broken adaptive ridge regression for interval-censored competing risks data.

Lifetime data analysis·2026
See all related articles

This study introduces an efficient semiparametric method for estimating parameters in randomly truncated data from K populations with common distributions but different truncation mechanisms. The approach offers robust statistical inference for both parametric and nonparametric components.

Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Randomly truncated data are common in studies due to sampling designs.
  • Existing literature offers nonparametric and semiparametric methods for one-sample models.
  • A need exists for efficient estimation methods in complex truncation scenarios.

Purpose of the Study:

  • To develop an efficient semiparametric method for parameter estimation with randomly truncated data.
  • To address models where K populations share a common distribution but have distinct truncation mechanisms.
  • To derive inferences for both parametric and nonparametric model components.

Main Methods:

  • Semiparametric likelihood estimation is employed.
  • The method is developed for a one-sample model with K populations.

Related Experiment Videos

  • Inferences are derived for parametric and nonparametric components.

    • Main Results:

    • An efficient estimation method for unknown parameters in the semiparametric model is developed.
    • The method provides robust inferences for model components.
    • The approach is applicable to two-sample problems for comparing lifetime distributions.

    Conclusions:

    • The proposed semiparametric method effectively handles randomly truncated data from multiple populations.
    • The technique facilitates robust statistical inference in complex sampling designs.
    • The method demonstrates utility in both one-sample estimation and two-sample comparison of lifetime distributions.