The recurrence interval of extreme returns can be predicted with high accuracy.
problem Predicting the occurrence of extreme financial returns.
method Recurrence interval analysis of extreme returns, using q-exponential distribution. result The recurrence interval of extreme returns follows a q-exponential distribution, leading to more accurate forecasts. The paper proposes a new method for clustering survival data using smoothed log-hazard trajectories.
problem Clustering survival data based on instantaneous risk dynamics.
method Functional Principal Component Analysis applied to B-spline smoothed log-hazard trajectories.
result The proposed method provides an interpretable representation of relative temporal risk dynamics.
The paper develops a filtering framework for estimating hazard rates with jumps in financial and insurance applications.
problem Estimating hazard rates with unobservable change-points in financial and insurance contexts.
method Continuous-time filtering framework using progressive enlargement of filtration, stochastic differential equations, and sensitivity analysis.
result Explicit formula for survival probability conditional on partial information.
Study optimal reinsurance contracts to prevent moral hazard under non-concave premium principles.
problem Preventing moral hazard in reinsurance contracts under non-concave premium principles.
method Develops optimal reinsurance contracts under a diffusion risk model with incentive compatibility constraints and extended distortion premium principles.
result An optimal reinsurance contract exists and is characterized by solving a double obstacle problem.
New method estimates treatment effects in high-dimensional survival data.
problem Estimating causal effects for survival outcomes with many covariates.
method Orthogonal score method and Hazards Difference (HDi) estimator for high-dimensional additive hazards model.
result Valid inference for conditional treatment effect under high-dimensional setting.
Being able to forcast extreme volatility is a central issue in financial risk management. We present a large volatility predicting method based on the distribution of recurrence intervals between volatilities exceeding a certain threshold Q for a fixed expected recurrence time τQ. We find that the recurrence inter…
The paper solves an insurance problem using mean-variance and rank-dependent utility theory.
problem Formulating and solving an insurance problem with rank-dependent utility and mean-variance premium principle.
method Formulated as a non-concave maximization problem, then turned into a concave quantile optimization problem, solved using calculus of variations.
result An optimal insurance contract is derived and numerically computed.
A method for predicting survival using neural networks for both continuous and discrete time.
problem Survival prediction for both continuous and discrete time data.
method Proposes a scheme for discretizing continuous-time data and two interpolation schemes for continuous-time survival estimates.
result The hazard rate parametrization of neural networks yields better performance than the parametrization of the probability mass function.
New model predicts financial tail events using RIA-EVT-Copula.
problem Predicting financial tail events for risk management.
method RIA-EVT-Copula framework combining POT, RIA, and copulas.
result Improved accuracy in predicting financial extremes.
SVB method provides scalable Bayesian proportional hazards model for high-dimensional gene expression data.
problem Bayesian methods for high-dimensional sparse survival data often sacrifice uncertainty quantification or computational scalability.
method Mean-field variational approximation for scalable Bayesian proportional hazards model.
result SVB method offers posterior distribution for parameters and variable selection via posterior inclusion probabilities.
Develops a framework to assess infrastructure reliability under natural and malicious events.
problem Assessing reliability and costs of infrastructure under various hazards.
method Coupling mechanical reliability analyses with economical reliability analyses using probabilistic considerations.
result Indicators of probable cost of failure for infrastructure, aiding safety investments.
Develops a method to estimate average hazard under non-proportional hazards without relying on proportional hazards assumption.
problem Estimation of treatment effects when hazards are non-proportional, leading to unstable hazard ratios.
method Semiparametric, doubly robust framework for covariate-adjusted average hazard estimation.
result Valid sqrt{n} inference with small bias and near-nominal confidence-interval coverage across proportional and non-proportional hazards settings.
Study explains mortgage burnout using Cox hazard models.
problem Understanding burnout in mortgage pools.
method Modeling mortgage prepayment using Cox hazard processes.
result Observed pool hazard is a survival-weighted mean of individual hazards with a selection term.
CBNNs model survival with time-varying interactions, outperforming other methods.
problem Complex covariate effects and time-varying interactions in survival analysis.
method Combines case-base sampling with neural networks to model time-varying effects and complex baseline hazards.
result CBNNs outperform regression and neural network-based survival methods in simulations and real data applications.
Market activity scales near a constant of 0.632 in intrinsic time.
problem Understanding the stability of market scaling laws.
method Modeling market directional changes as a memoryless exponential hazard process and identifying the intrinsic time scaling constant.
result The intrinsic time scaling constant is 1−1/e=0.632. Study compares Cox model and RSF for predicting patient survival, finding RSF superior in certain scenarios.
problem Comparing predictive accuracy of Cox proportional hazards model and Random Survival Forest for patient-specific survival probabilities.
method Conducted a comprehensive comparison study using simulation scenarios and real-world datasets.
result RSF outperforms Cox model in nonproportional hazards settings and with treatment-covariate interactions.
Algorithm distinguishes light-tailed from non-light-tailed distributions.
problem Characterize the tail of a distribution using hazard rate.
method Careful bucketing scheme based on hazard rate.
result Polynomial number of samples required for success.
New method estimates hazard ratios without bias in observational studies.
problem Uninterpretable hazard ratios due to unspecified baseline hazard.
method Kernel-based machine learning to model risk set changes.
result Debiased maximum-likelihood estimators identify true hazard ratios.
MEC-Cox: A Machine-Learning-Assisted Generalized Entropy Calibration Method for Estimating ATT Marginal Hazard-Ratio
problem Estimating ATT marginal hazard-ratio in externally controlled survival trials
method Machine-learning-assisted generalized entropy calibration for IPW Cox regression
result Reduces bias, increases efficiency, and improves coverage
Novel approach to compute hazard ratios from observational studies using SCMs and backdoor adjustment.
problem Identifying causal relationships from observational data using hazard ratios.
method Backdoor adjustment through structural causal models (SCMs) and do-calculus.
result Novel approach for computing hazard ratios from observational studies.
This paper extends FTPL algorithms for bandits beyond bounded hazard rate assumptions.
problem Adversarial multi-armed bandit problem with perturbations.
method Introduces new regret bounds for FTPL algorithms without bounded hazard rate assumption.
result Gaussian distribution leads to near optimal regret, up to logarithmic factors.
Unified model estimates landslide hazard combining susceptibility, intensity, and frequency.
problem Lack of unified statistical models for landslide hazard estimation.
method Deep learning combined with extreme-value theory.
result Model performs excellently and can estimate hazard for multiple return periods.
EdgeLite detects hazardous supermarket floors, improving safety.
problem Detecting hazardous conditions on supermarket floors to prevent injuries.
method Developed a lightweight deep learning model, EdgeLite, for edge devices.
result EdgeLite outperformed state-of-the-art models in detecting hazards on supermarket floors.
SurvITE learns treatment effects from time-to-event data, addressing unique challenges.
problem Inferring heterogeneous treatment effects from time-to-event data.
method Proposes a novel deep learning method for treatment-specific hazard estimation.
result Method outperforms baselines by addressing covariate shifts from various sources.
Study on financial crises duration and volatility in US markets.
problem Duration of negative stock market returns and its impact on volatility.
method Survival models, log-normal distribution, continuous time analysis.
result Conditional probability of ending negative return spells increases up to 2-3 months after onset.
SAFE detects fraudsters in advance by predicting survival probabilities.
problem Detecting fraudsters in time given their activity sequences.
method Survival analysis with RNN to map user activities to hazard values.
result SAFE outperforms existing models in fraud early detection.
Boosts hazard estimation with time-varying covariates using gradient boosting.
problem Estimating nonparametric hazard functions with time-dependent covariates.
method Gradient boosting procedure for nonparametric hazard estimation.
result Step-size restriction prevents overfitting and ensures convergence.
Paper proposes a method to identify wind hazard types and predict extreme wind speeds.
problem Difficulty in identifying wind hazard types from meteorological data records.
method Numerical pattern recognition method with feature extraction and generalization.
result Algorithm performance validated using K-fold cross-validation and real-world data.
We develop a theory for pricing non-diversifiable mortality risk in an incomplete market. We do this by assuming that the company issuing a mortality-contingent claim requires compensation for this risk in the form of a pre-specified instantaneous Sharpe ratio. We prove that our ensuing valuation formula satisfies a nu…
Spatially-aware model improves earthquake hazard assessment accuracy.
problem Misrepresentation of seismic effects across diverse landscapes.
method Causal Bayesian network with Gaussian Processes and normalizing flows.
result Achieves up to 35.2% AUC improvement over existing methods.
QSurv models survival data without discretization, achieving high accuracy.
problem Intractable likelihood estimation for continuous-time survival models.
method QSurv uses numerical quadrature for cumulative hazard approximation and time-conditioned low-rank adaptation.
result QSurv achieves competitive predictive performance and interpretable hazard patterns.
Optimal contracts remain linear in output when both moral hazard and adverse selection are present.
problem Optimal compensation problems involving competing principals with uncertainty from both moral hazard and adverse selection.
method Continuous-time setting with risk-averse agent controlling drift of output process driven by Brownian motion. Shows linear contracts hold under type-dependent reservation utilities.
result Optimal contracts remain linear in output when both moral hazard and adverse selection are present.
In data sets with many more features than observations, independent screening based on all univariate regression models leads to a computationally convenient variable selection method. Recent efforts have shown that in the case of generalized linear models, independent screening may suffice to capture all relevant feat…
Study detects anomalies in robot vision data to predict hazards.
problem Detecting unexpected hazards in robot exploration data.
method Anomaly detection using autoencoders at different scales.
result Autoencoders improve anomaly detection performance on diverse robot scenarios.
Study examines survival models for ALS, focusing on proportional hazards assumption.
problem Impact of proportional hazards assumption on survival models for ALS.
method Theoretical and empirical investigation of survival forests and their variants.
result Alternative split procedures can improve model performance in non-proportional hazards situations.
BoXHED boosts hazard estimation for dynamic health risk scores.
problem Analyzing time-varying health vitals for disease onset prediction.
method Gradient boosting for nonparametric hazard function estimation with time-dependent covariates.
result Novel interaction effects among risk factors identified in cardiovascular disease onset data.
Flexible DNN for survival data, avoiding proportional hazards assumption.
problem Survival analysis with complex interactions and non-proportional hazards.
method Partially linear DNN model with a flexible nonparametric component.
result FLEXI-Haz achieves optimal convergence rates and asymptotic efficiency.
In this paper, we provide a solution to two problems which have been open in default time modeling in credit risk. We first show that if τ is an arbitrary random (default) time such that its Azéma's supermartingale $Z_t^τ=¶(τ>t|\F_t)$ is continuous, then τ avoids stopping times. We then disprove a conjecture about …
Enhances survival analysis by separating population behavior from individual dynamics.
problem Improving the training and inference of survival analysis models for sparsely occurring events.
method Decouples survival analysis into an aggregated baseline hazard and independent survival scores.
result Achieves competitive performance and robust results without fine-tuning.
Method maps gaze fixations to hazards for safer construction.
problem Unrecognized hazards pose safety risks to construction workers.
method Instance segmentation using transfer learning.
result Mapped gaze data improves understanding of hazard recognition.
Authors prove the existence of a martingale measure in credit risk models.
problem Existence of an equivalent martingale measure in hazard process models of credit risk.
method By identifying a no-arbitrage condition, the authors construct a measure that turns discounted stock and bond prices into martingales.
result The existence of a martingale measure is demonstrated in credit risk models.
Federated Cox model handles non-proportional hazards in siloed data.
problem Handling non-proportional hazards in federated healthcare data.
method Developed a federated Cox model that relaxes proportional hazards assumption.
result Federated model performs similarly to standard models on clinical datasets.
This paper proposes a decorrelation-based approach to test hypotheses and construct confidence intervals for the low dimensional component of high dimensional proportional hazards models. Motivated by the geometric projection principle, we propose new decorrelated score, Wald and partial likelihood ratio statistics. Wi…
Study constructs balanced datasets for seismic failure prediction.
problem Imbalanced datasets limit machine learning performance in seismic failure prediction.
method Framework with three steps: GMF identification, probability density estimation, and sample transformation.
result Framework improves machine learning performance in seismic failure mode prediction.
ICODEN models survival data with interval-censored times using neural networks and ODEs.
problem Predicting time-to-event outcomes with interval-censored data, especially when models require strong assumptions or cannot handle high-dimensional predictors.
method ICODEN uses ordinary differential equations and deep neural networks to model the hazard function and cumulative hazard without proportional hazards assumption.
result ICODEN achieves satisfactory predictive accuracy across various simulation and real-world applications, handling high-dimensional predictors robustly.
Proposes FarmHazard model for hazard regression with correlated covariates.
problem Model selection challenges in high-dimensional data with correlated covariates.
method Factor-Augmented Regularized Model for Hazard Regression (FarmHazard) that learns latent factors and idiosyncratic components.
result Proves model selection and estimation consistency under mild conditions.
Edge-based sensor network monitors natural hazards efficiently.
problem Fast decision making in natural hazard warning systems.
method Event-triggered micro-seismic sensors, co-detection, machine learning, convolutional neural networks.
result Real-time hazard monitoring with low power consumption.
Paper connects Plackett-Luce and Cox models for preference estimation.
problem Estimating preferences from annotated data.
method Connects Plackett-Luce model to Cox Proportional Hazards model.
result Implications of the connection between the two models.