ArticleBiometrics2014
A penalized EM algorithm incorporating missing data mechanism for Gaussian parameter estimation.
Article in Biometrics, 2014. 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.
- Identification and applications of disease-associated differential human and bacterial proteins with metaproteomic evidence.Health information science and systems · 2025Article
- Identification of microbial species and proteins associated with colorectal cancer by reanalyzing CPTAC proteomic datasets.Scientific reports · 2025Article
- Compare the marginal effects for environmental exposure and biomonitoring data with repeated measurements and values below the limit of detection.Journal of exposure science & environmental epidemiology · 2024Article
- Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets.BMC medical research methodology · 2024Article
- MetaProD: A Highly-Configurable Mass Spectrometry Analyzer for Multiplexed Proteomic and Metaproteomic Data.Journal of proteome research · 2023Article
- Robust estimation using multivariateJournal of applied statistics · 2021Article
- A Review of Imputation Strategies for Isobaric Labeling-Based Shotgun Proteomics.Journal of proteome research · 2021Review
- Multiple imputation and direct estimation for qPCR data with non-detects.BMC bioinformatics · 2020Article
- Integrative Proteo-genomic Analysis to Construct CNA-protein Regulatory Map in Breast and Ovarian Tumors.Molecular & cellular proteomics : MCP · 2019Article
- A statistical method for detecting differentially expressed SNVs based on next-generation RNA-seq data.Biometrics · 2017Article
- A MIXED-EFFECTS MODEL FOR INCOMPLETE DATA FROM LABELING-BASED QUANTITATIVE PROTEOMICS EXPERIMENTS.The annals of applied statistics · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Missing data rates could depend on the targeted values in many settings, including mass spectrometry-based proteomic profiling studies. Here, we consider mean and covariance estimation under a multivariate Gaussian distribution with non-ignorable missingness, including scenarios in which the dimension (p) of the response vector is equal to or greater than the number (n) of independent observations. A parameter estimation procedure is developed by maximizing a class of penalized likelihood functions that entails explicit modeling of missing data probabilities. The performance of the resulting "penalized EM algorithm incorporating missing data mechanism (PEMM)" estimation procedure is evaluated in simulation studies and in a proteomic data illustration.
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.