On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,694 papers · 148 categories

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8 results for “non-ignorable”

Label Shift Estimators for Non-Ignorable Missing Datastat.ME

Estimates disease prevalence using non-ignorable missing data in health surveys.

problem Estimating disease prevalence in non-representative samples with non-ignorable missing data.

method Connects auxiliary proxy variable framework to label shift setting, uses high-dimensional covariates without generative models.

result Fails to account for non-ignorable missingness can lead to significant misestimations.

missing data disease prevalence non-ignorable label shift health survey

2023-10-27abs ↗pdf ↗

Learning partially ranked data based on graph regularizationstat.ME

Ranked data appear in many different applications, including voting and consumer surveys. There often exhibits a situation in which data are partially ranked. Partially ranked data is thought of as missing data. This paper addresses parameter estimation for partially ranked data under a (possibly) non-ignorable missing…

2019-02-28abs ↗pdf ↗

Joint Models for Handling Non-Ignorable Missing Data using Bayesian Additive Regression Trees: Application to Leaf Photosynthetic Traits Datastat.ME

Two methods use BART to model missing data in leaf photosynthetic trait data.

problem Handling missing data in multivariate outcomes with non-ignorable mechanisms.

method Bayesian Additive Regression Trees (BART) for joint modeling of data and missingness indicators.

result Both methods effectively recover various missingness mechanisms and outperform existing approaches.

Bayesian Additive Regression Trees Joint Modeling Missing Data Photosynthetic Traits Non-Ignorable Missingness

2024-12-19abs ↗pdf ↗

Deeply-Learned Generalized Linear Models with Missing Datastat.ML

New DL model handles missing data in biomedical datasets.

problem Handling missing data in modern biomedical datasets.

method Proposed a new DL architecture, dlglm, for generalized linear models.

result Outperforms existing methods in MNAR missingness scenarios.

Deep Learning Generalized Linear Models Missing Data Supervised Learning Biomedical Sciences

2022-07-18abs ↗pdf ↗

Robust prediction under missingness shiftsstat.ML

Bayes predictor remains robust to ignorable missingness shifts.

problem Challenges in prediction with missing covariates and shifts in missingness reasons.

method Bayesian approach and different prediction methods.

result Bayes predictor remains unchanged by ignorable shifts, but robust prediction requires disregarding missingness for non-ignorable shifts.

Bayesian prediction missing data prediction robustness missingness shifts informative missingness

2024-06-24abs ↗pdf ↗

Estimation of Classification Rules from Partially Classified Datastat.ML

Estimates classification rules from partially classified data.

problem Estimating Bayes' rule for unclassified observations in partially classified data.

method Fitting a g-component mixture model by maximum likelihood (ML) via the EM algorithm.

result Asymptotic relative efficiency (ARE) of Bayes' rule estimated from partially classified samples.

Bayes' rule mixture model EM algorithm classification ML asymptotic relative efficiency

2020-04-13abs ↗pdf ↗

A Directional Rockafellar-Uryasev Regressionstat.ML

Proposes a neural network model to improve predictions in biased datasets.

problem Reduces bias in predictions from datasets with selection bias.

method Integrates meta information about bias direction and quantity into a neural network model.

result Improves prediction accuracy in electoral polls with biased training data.

bias mitigation meta information neural networks selection bias electoral polls

2024-11-04abs ↗pdf ↗

SSLfmm: An R Package for Semi-Supervised Learning with a Mixed-Missingness Mechanism in Finite Mixture Modelsstat.CO

SSLfmm package improves semi-supervised learning by incorporating informative missingness in finite mixture models.

problem Improving semi-supervised learning with informative missingness in datasets.

method Estimates Bayes' classifier under a finite mixture model with MCAR and MAR missingness mechanisms.

result The classifier trained on partially labelled data can achieve lower misclassification rates than supervised methods.

Semi-supervised learning Finite mixture models Informative missingness Expectation--Conditional Maximisation Bayes' classifier

2025-12-03abs ↗pdf ↗