DeepHazard uses neural networks to predict time-varying survival risks.
problem Traditional survival models assume proportional hazards and do not account for time-varying covariate information.
method DeepHazard is a neural network approach that models time-varying hazards without proportional hazards assumption.
result DeepHazard outperforms existing methods in predicting survival time, as shown by C-index metrics on real datasets.
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.
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.
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.
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.
KAPLAN-HR models survival data without manual interactions, outperforming existing methods.
problem Survival analysis challenges with complex covariates and time-varying effects.
method Kolmogorov-Arnold Networks (KAN) for nonparametric hazard estimation.
result KAPLAN-HR matches or exceeds existing methods in clinical survival data.
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.
New formulations capture aversion to ambiguity about volatility.
problem Capturing aversion to ambiguity about unknown and time-varying volatility.
method Introduces novel preference formulations and compares them with existing models.
result Illustrates the impact of ambiguity aversion in static and dynamic models.
New methods estimate survival functions with time-varying covariates.
problem Estimating survival functions with time-varying covariates.
method Generalized conditional inference and relative risk forests, adapted transformation forest.
result Proposed methods outperform traditional models in estimating survival functions.
A new model predicts spatially varying inland flooding from time-varying inputs.
problem Ignoring time series and spatial correlations in flood models leads to inaccurate predictions.
method Introduced a multioutput Gaussian process model with separable kernels for functional inputs and spatial locations.
result The model provides accurate predictions of spatially varying inland flooding with minimal computational time.
Study improves survival analysis for credit risk by accounting for data drift.
problem Survival analysis in credit risk assumes a stationary data-generating process, but real-world data drift affects model performance.
method Proposes a dynamic joint modelling framework integrating longitudinal behavioural markers and hazard formulations, combined with drift-adaptive techniques.
result Proposed model outperforms classical survival models and drift-adaptive learners in various data drift scenarios.
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.
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.
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.
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.
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.
Recent work on follow the perturbed leader (FTPL) algorithms for the adversarial multi-armed bandit problem has highlighted the role of the hazard rate of the distribution generating the perturbations. Assuming that the hazard rate is bounded, it is possible to provide regret analyses for a variety of FTPL algorithms f…
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.
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.
Estimating causal effects for survival outcomes in the high-dimensional setting is an extremely important topic for many biomedical applications as well as areas of social sciences. We propose a new orthogonal score method for treatment effect estimation and inference that results in asymptotically valid confidence int…
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.
Supermarkets need to ensure clean and safe environments for both shoppers and employees. Slips, trips, and falls can result in injuries that have a physical as well as financial cost. Timely detection of hazardous conditions such as spilled liquids or fallen items on supermarket floors can reduce the chances of serious…
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 …
The inverse first passage time problem asks whether, for a Brownian motion B and a nonnegative random variable ζ, there exists a time-varying barrier b such that P{Bs>b(s),0≤s≤t}=P{ζ>t}. We study a "smoothed" version of this problem and ask whether there is a "barrier" b such th…
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…
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.
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.
An extreme wind speed estimation method that considers wind hazard climate types is critical for design wind load calculation for building structures affected by mixed climates. However, it is very difficult to obtain wind hazard climate types from meteorological data records, because they restrict the application of e…
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.
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.
Research studies have shown that a large proportion of hazards remain unrecognized, which expose construction workers to unanticipated safety risks. Recent studies have also found that a strong correlation exists between viewing patterns of workers, captured using eye-tracking devices, and their hazard recognition perf…
Semi-parametric survival analysis methods like the Cox Proportional Hazards (CPH) regression (Cox, 1972) are a popular approach for survival analysis. These methods involve fitting of the log-proportional hazard as a function of the covariates and are convenient as they do not require estimation of the baseline hazard …
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.
CoxSE combines deep learning with self-explaining neural networks for survival analysis.
problem Improving predictive power of Cox Proportional Hazards model while maintaining explainability.
method Proposes CoxSE, a locally explainable Cox proportional hazards model using SENN, and CoxSENAM, a hybrid model with NAM.
result CoxSE provides more stable and consistent explanations while maintaining predictive power.
Application of discrete-time survival methods for continuous-time survival prediction is considered. For this purpose, a scheme for discretization of continuous-time data is proposed by considering the quantiles of the estimated event-time distribution, and, for smaller data sets, it is found to be preferable over the …
We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from it nonparametrically, as well as dependence on covariates. As opposed to many other methods in survival analysis, our framework does not im…
Proposes HGP to improve survival analysis models.
problem Improving survival analysis models by addressing density function changes.
method Imposes constraints on local data points by regularizing the hazard function gradient.
result HGP enhances survival analysis model performance.
In credit risk literature, the existence of an equivalent martingale measure is stipulated as one of the main assumptions in the hazard process model. Here we show by construction the existence of a measure that turns the discounted stock and defaultable bond prices into martingales by identifying a no-arbitrage condit…
Proposes a flexible neural model for multi-state survival analysis.
problem Limited applicability of Cox models for multi-state and competing events.
method Uses neural ordinary differential equations to solve Kolmogorov forward equations.
result Demonstrates state-of-the-art performance and interpretability.
Study quantifies firm risks from nature decline, showing significant equity losses.
problem Estimating the financial impact of nature deterioration on companies.
method Developed metrics (Country Degradation Index, Nature Risk Score) and assessed five environmental hazards.
result Global equities lose 26.8% in a nature decline scenario, with worst firms losing 75%.
ADHAM provides interpretable survival analysis for healthcare.
problem Limited interpretability in deep learning survival models.
method Additive Deep Hazard Analysis Mixtures (ADHAM) with latent subgroup structure.
result ADHAM offers interpretable insights into exposure-outcome associations.
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.
SurvLIME-KS improves survival model explanations robustly.
problem Improving explanations of unreliable survival models.
method SurvLIME-KS combines Cox proportional hazards model and Kolmogorov-Smirnov bounds for robust optimization.
result SurvLIME-KS minimizes average distance and maximizes distance in approximating cumulative hazard functions.
Federated CTMC model estimates bridge deterioration hazards without sharing raw data.
problem Bridge inspection data privacy and cross-organizational data sharing constraints.
method Federated CTMC hazard model with local optimization and FedAvg aggregation.
result Federated model converges on global benchmark parameters without data transfer.
Extends tracking guarantees for time-varying variational inequalities.
problem Tracking solutions of time-varying variational inequalities.
method Extends existing results to sublinear solution paths and periodic problems.
result Discrete dynamical systems of periodic time-varying VI can exhibit chaotic behavior or converge to the solution.
Given functional data from a survival process with time-dependent covariates, we derive a smooth convex representation for its nonparametric log-likelihood functional and obtain its functional gradient. From this, we devise a generic gradient boosting procedure for estimating the hazard function nonparametrically. An i…
New method tracks time-varying parameters in data.
problem Tracking unknown time-varying parameters in data.
method Stochastic gradient descent-based recursive scheme with log-likelihood as gain function.
result Convergence in mean-square error in a suitable neighborhood of the unknown parameter.