This paper develops new statistical methodology for joint modeling of longitudinal biomarkers and time-to-event data improves dynamic predictions by 18% in auc: a comparison across 12 diseases. We propose a Bayesian hierarchical framework that jointly models multiple sources of uncertainty while accounting for complex dependence structures including spatial, temporal, and measurement error components.
This paper develops new statistical methodology for species distribution models with preferential sampling correction increase predicted range sizes by 23%: a global assessment for 500 bird species. We propose a Bayesian hierarchical framework that jointly models multiple sources of uncertainty while accounting for complex dependence structures including spatial, temporal, and measurement error components.
This paper develops new statistical methodology for exposure-response modeling via targeted minimum loss estimation reveals non-monotone dose-toxicity curves in 3 oncology drugs. We propose a Bayesian hierarchical framework that jointly models multiple sources of uncertainty while accounting for complex dependence structures including spatial, temporal, and measurement error components.
This paper develops new statistical methodology for functional data analysis of continuous glucose monitor traces predicts hba1c with r² = 0.89: outperforming traditional summary statistics.
We investigate a fundamental computational challenge in modern Bayesian statistics: stein variational gradient descent collapses in high dimensions: mode coverage drops below 50% for d > 20. Through rigorous theoretical analysis and extensive numerical experiments, we characterize the conditions under which existing algorithms fail and propose a novel correction that restores reliable performance.
We provide causal evidence that public pension generosity reduces private savings by only 30 cents per dollar: revised estimates using administrative data from 8 oecd countries. Our identification strategy combines quasi-experimental variation with state-of-the-art econometric techniques including difference-in-differences with staggered treatment adoption, instrumental variables estimation, and regression discontinuity designs.
This paper investigates the econometric foundations underlying bartik instruments require 50+ sectors for valid inference: a finite-sample simulation study. Using a combination of Monte Carlo simulations, analytical derivations, and empirical applications, we demonstrate that conventional approaches suffer from previously unrecognized biases.
We provide causal evidence that microfinance group lending reduces default rates by 11% compared to individual lending only for loans below $500: a multi-country rct. Our identification strategy combines quasi-experimental variation with state-of-the-art econometric techniques including difference-in-differences with staggered treatment adoption, instrumental variables estimation, and regression discontinuity designs.
We provide causal evidence that e-government portals reduce bribery incidence by 41% in mid-income countries: quasi-experimental evidence from 23 nations. Our identification strategy combines quasi-experimental variation with state-of-the-art econometric techniques including difference-in-differences with staggered treatment adoption, instrumental variables estimation, and regression discontinuity designs.
We provide causal evidence that the dutch disease operates primarily through real estate appreciation rather than manufacturing decline: evidence from 19 oil exporters. Our identification strategy combines quasi-experimental variation with state-of-the-art econometric techniques including difference-in-differences with staggered treatment adoption, instrumental variables estimation, and regression discontinuity designs.
This paper investigates the econometric foundations underlying causal forests with honest splitting have asymptotically normal treatment effects even under 20% attrition: a trimming bounds extension. Using a combination of Monte Carlo simulations, analytical derivations, and empirical applications, we demonstrate that conventional approaches suffer from previously unrecognized biases.
This paper investigates the econometric foundations underlying regression kink designs have lower power than regression discontinuity by a factor of n^{1/5}: optimal bandwidth implications. Using a combination of Monte Carlo simulations, analytical derivations, and empirical applications, we demonstrate that conventional approaches suffer from previously unrecognized biases.
We provide causal evidence that teacher performance pay increases student test scores only when measured relative to peers: a 450-school rct in india. Our identification strategy combines quasi-experimental variation with state-of-the-art econometric techniques including difference-in-differences with staggered treatment adoption, instrumental variables estimation, and regression discontinuity designs.
Cell Cycle Duration Variance Is 60% Heritable Across Sister Cells. Lineage Tracking of 14,000 Division Events in Mammary Epithelium We present a comprehensive quantitative analysis that challenges conventional understanding.
We present new results on shannon capacity with applications to lovasz theta. Our main theorem establishes sharp bounds that improve upon the best previously known results, settling a conjecture in the affirmative for the cases considered.
Post-Translational Modifications Create a Histone Code Degeneracy. 340 Distinct Modification Patterns Map to Only 12 Functional Chromatin States We present a comprehensive quantitative analysis that challenges conventional understanding.
We present new results on additive combinatorics with applications to zero sum theory. Our main theorem establishes sharp bounds that improve upon the best previously known results, settling a conjecture in the affirmative for the cases considered.