Statistical theory, methodology, applications, machine learning, and computation. ← all categories
Operational risk capital: internal loss data contributes only 12% information when external data available. BMA across 15 models, 42 banks.
Risk parity portfolios fail during liquidity crises. 20 years (2005--2025), 8 asset classes.
DCC models underestimate portfolio VaR by 18% during regime transitions. 60 equity/bond portfolios (2000--2025), 3 regimes.
Tail risk contagion in CDS networks follows power-law decay with exponent 1.4, not exponential.
Wrong-way risk in margin lending amplifies losses 3.1x during VIX > 40 events.
Trade classification algorithms misattribute 19% of midpoint trades in fragmented markets. We evaluate on 847M TAQ trades (2020--2024).
Cryptocurrency portfolio risk cannot be captured by Gaussian models. Tempered stable distributions reduce VaR estimation error by 45.
Systemic risk indicators based on Shapley values from cooperative game theory predict bank failures 6 months earlier than CoVaR and SRISK. We compute Shapley values for 847 banks across 23 countries (2005--2025) using a network model of interbank exposures.
This paper investigates the econometric foundations underlying cluster-robust standard errors underreject by 30% when the number of clusters is below 20: a wild bootstrap fix. Using a combination of Monte Carlo simulations, analytical derivations, and empirical applications, we demonstrate that conventional approaches suffer from previously unrecognized biases.
Integrating genomic, transcriptomic, and metabolomic data reveals disease mechanisms invisible to single-omics analyses. We apply sparse canonical correlation analysis (sCCA) to 2,847 T2D patients and 3,124 controls from 3 cohorts.
We provide causal evidence that conditional cash transfers increase vaccination rates by 19 percentage points when disbursed via mobile phones: evidence from pakistan. 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.
Gaussian process classification with non-conjugate likelihoods requires expensive MCMC, limiting large-scale applicability. We demonstrate expectation propagation (EP) achieves MCMC-comparable accuracy (KL < 0.
Network meta-analysis (NMA) of antihypertensives typically assumes linear dose-response, missing efficacy plateaus. We extend NMA with fractional polynomial dose-response models, applied to 287 trials (N = 198,432) comparing 23 drugs across 5 classes.
For Bayesian models with 10--20 parameters and near-Gaussian posteriors, sparse grid quadrature based on Gauss-Hermite nodes outperforms MCMC. Across 80 benchmarks with $d \in \{10, 12, 15, 18, 20\}$, sparse grid achieves 3.
We provide causal evidence that currency devaluations in sub-saharan africa increase food insecurity by 18% within 6 months: a synthetic control study. 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.
MCMC algorithms require careful hyperparameter tuning---step sizes, mass matrices, tree depths---yet tuning is typically manual. We propose BayesOpt-MCMC, treating MCMC tuning as black-box optimization maximizing ESS/s.
This paper investigates the econometric foundations underlying identification in triangular systems with discrete endogenous variables requires rank conditions that fail 73% of the time in practice. Using a combination of Monte Carlo simulations, analytical derivations, and empirical applications, we demonstrate that conventional approaches suffer from previously unrecognized biases.
Cancer patients face competing risks of cancer death and CVD. Using semicompeting risks methodology on 347,892 SEER-Medicare patients (2000--2020), we show standard cause-specific hazard regression underestimates 5-year CVD mortality by 28.
Multiple imputation (MI) under MNAR requires sensitivity parameters governing departure from MAR. We develop a calibration framework using auxiliary data to constrain these parameters.
Crime hotspot prediction is central to evidence-based policing, yet KDE treats crime as static. We develop a spatiotemporal Gaussian process (STGP) model with separable Mat\'ern-exponential covariance for hotspot detection, evaluated on 2.