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

Asymptotic optimality of likelihood-based cross-validation.

Mark J van der Laan1, Sandrine Dudoit, Sunduz Keles

  • 1Division of Biostatistics, School of Public Health, University of California, Berkeley, USA. laan@stat.berkeley.edu

Statistical Applications in Genetics and Molecular Biology
|May 2, 2006
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

An approach to nonparametric inference on the causal dose-response function.

Journal of causal inference·2026
Same author

MINTsC learns multi-way chromatin interactions from single cell high throughput chromatin conformation data.

Nature communications·2026
Same author

UNLOCKING MULTI-SAMPLE DIFFERENTIAL EXPRESSION FOR SPATIAL TRANSCRIPTOMICS DATA WITH TESSERA.

bioRxiv : the preprint server for biology·2026
Same author

Sequential invitations to FOBT screening and colorectal cancer incidence.

Scientific reports·2026
Same author

Powering RCTs for Marginal Effects With GLMs Using Prognostic Score Adjustment.

Statistics in medicine·2026
Same author

MINTsC learns multi-way chromatin interactions from single cell high throughput chromatin conformation data.

bioRxiv : the preprint server for biology·2026
Same journal

Balanced mediated pathway detection in genomic data.

Statistical applications in genetics and molecular biology·2026
Same journal

Annealed variational mixtures for disease subtyping and biomarker discovery.

Statistical applications in genetics and molecular biology·2026
Same journal

Performance of the permutation test approach with base calling errors for detecting changes in variant allele frequencies in ctDNA for a single patient.

Statistical applications in genetics and molecular biology·2026
Same journal

BLOG: Bayesian longitudinal omics with group constraints.

Statistical applications in genetics and molecular biology·2026
Same journal

AI-driven risk prediction and categorization in cystic fibrosis leveraging AttentiveLSTM and Fox Wolf Optimizer.

Statistical applications in genetics and molecular biology·2026
Same journal

Perfect collinearity not created equal: measuring and visualizing the severity of multi-collinearity of modern omics data.

Statistical applications in genetics and molecular biology·2026
See all related articles

Likelihood-based cross-validation offers a robust method for selecting density estimates. This statistical tool ensures performance comparable to an optimal benchmark, particularly in bandwidth selection and DNA motif detection.

Area of Science:

  • Statistical modeling
  • Machine learning
  • Bioinformatics

Background:

  • Density estimation is crucial for statistical inference.
  • Likelihood-based cross-validation (LBCV) is a method for selecting density estimators.
  • Existing methods lack finite sample guarantees for general LBCV procedures.

Purpose of the Study:

  • Establish a finite sample result for a general class of LBCV procedures.
  • Analyze the asymptotic performance of LBCV compared to an optimal benchmark.
  • Illustrate LBCV's practical utility in bandwidth selection and motif detection.

Main Methods:

  • Developed a general theorem for LBCV performance.
  • Utilized Kullback-Leibler distance for performance evaluation.
  • Employed simulation studies and real-world applications (DNA motif detection).

Related Experiment Videos

Main Results:

  • LBCV selectors perform asymptotically as well as an optimal benchmark.
  • Performance guarantee holds for V-fold cross-validation with large validation samples.
  • Candidate density estimates must be bounded away from zero and infinity.

Conclusions:

  • LBCV provides a theoretically sound and practically effective approach for density estimation.
  • The findings support LBCV's application in complex problems like bandwidth selection and bioinformatics.
  • Future work may explore LBCV under less restrictive conditions.