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.
The paper classifies cervical cancer using various techniques and feature selection.
problem Classifying cervical cancer from a dataset with missing values and imbalance.
method Feature selection, over-sampling, under-sampling, dimensionality reduction, and classification techniques.
result Age, first sexual intercourse, number of pregnancies, smokes, hormonal contraceptives, and STDs: genital herpes are the main predictive features with high accuracy.
Proposes logistic-beta process for modeling dependent probabilities with beta marginals.
problem Limited work on flexible and computationally convenient stochastic process extensions for dependent random probabilities.
method Introduces logistic-beta process with logistic transformation and beta marginals, capable of modeling dependence in discrete and continuous domains.
result Logistic-beta processes enable effective posterior inference and design of computationally tractable dependent Bayesian nonparametric models.
Valid causal inference with unobserved confounding in high-dimensional settings.
problem Estimating causal effects with unobserved confounders in high-dimensional data.
method Proposes methods to estimate causal effects with valid confidence intervals in the presence of unobserved confounders and high-dimensional nuisance models.
result Valid semiparametric inference can be obtained with unobserved confounding, and uncertainty intervals are proposed.
The paper addresses intervention effects in longitudinal studies with dropout and many timepoints.
problem Handling dropout and positivity violations in longitudinal studies with many timepoints.
method Generalizes effects of incremental interventions, accommodating multiple outcomes and dropout, and derives nonparametric efficiency bounds and efficient estimators.
result Incremental intervention effects yield near-exponential gains in statistical precision compared to deterministic effects in a setting with many timepoints.
Paper addresses measurement error in observational data, estimating effects of maternal smoking and opioid use on childhood obesity.
problem Systematic bias in inferences from observational datasets due to measurement error.
method Missing data view of measurement error problem; marginalizes latent true exposure; identifies outcome distribution under specific conditions.
result Method estimates effects of maternal smoking and opioid use on childhood obesity using only subject-reported data, refining estimates and consistency with existing literature.
Machine learning models outperform structural econometric models in cash transfer prediction tasks.
problem Evaluating conditional cash transfer policies using machine learning methods.
method Comparison of machine learning algorithms (CART, C4.5, LASSO, random forest, adaboost) with structural econometric models for cash transfer prediction.
result Random forest and adaboost models have the highest accuracy for individual subgroup outcomes in the out-of-sample forecast.