ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2019
MM Algorithms For Variance Components Models.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- A Cornucopia of Maximum Likelihood Algorithms.The American statistician · 2025Article
- Hierarchical selection of genetic and gene by environment interaction effects in high-dimensional mixed models.Statistical methods in medical research · 2025Review
- Modeling intra-individual inter-trial EEG response variability in autism.Statistics in medicine · 2024Article
- MM optimization: Proximal distance algorithms, path following, and trust regions.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- A Legacy of EM Algorithms.International statistical review = Revue internationale de statistique · 2022Article
- Multilevel hybrid principal components analysis for region-referenced functional electroencephalography data.Statistics in medicine · 2022Article
- Efficient ReML inference in variance component mixed models using a Min-Max algorithm.PLoS computational biology · 2022Article
- VCSEL: PRIORITIZING SNP-SET BY PENALIZED VARIANCE COMPONENT SELECTION.The annals of applied statistics · 2021Article
- IGREX for quantifying the impact of genetically regulated expression on phenotypes.NAR genomics and bioinformatics · 2020Article
- OPENMENDEL: a cooperative programming project for statistical genetics.Human genetics · 2020Review
- Variance Component Selection With Applications to Microbiome Taxonomic Data.Frontiers in microbiology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Variance components estimation and mixed model analysis are central themes in statistics with applications in numerous scientific disciplines. Despite the best efforts of generations of statisticians and numerical analysts, maximum likelihood estimation and restricted maximum likelihood estimation of variance component models remain numerically challenging. Building on the minorization-maximization (MM) principle, this paper presents a novel iterative algorithm for variance components estimation. Our MM algorithm is trivial to implement and competitive on large data problems. The algorithm readily extends to more complicated problems such as linear mixed models, multivariate response models possibly with missing data, maximum a posteriori estimation, and penalized estimation. We establish the global convergence of the MM algorithm to a Karush-Kuhn-Tucker (KKT) point and demonstrate, both numerically and theoretically, that it converges faster than the classical EM algorithm when the number of variance components is greater than two and all covariance matrices are positive definite.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.