This paper introduces tools to predict individual survival probabilities across all times.
problem Lack of tools to provide individual survival probabilities across all time points.
method Develops and evaluates new models including extensions to Cox model, Accelerated Failure Time, Random Survival Forests, and Multi-Task Logistic Regression.
result Introduces D-Calibration for evaluating individual survival distribution models.
Optimal insurance minimizes ruin probability with non-decreasing functions.
problem Minimizing ruin probability with insurance premiums and non-decreasing functions.
method Reformulated problem with inverse survival function as control variable.
result Deductible insurance with maximum limit is optimal.
Paper introduces symmetric divergence link models for probability distributions.
problem Symmetric divergence measures for probability distributions.
method Two general classes of link models: one for survival functions and another for cumulative probability distribution functions.
result Advantages of symmetric divergence measures over asymmetric measures for model averaging and feature assessment.
The paper develops a method to estimate conditional survival probabilities under noisy firm value data.
problem Estimating conditional default probabilities in models with partial information about firm value.
method Recursive quantization method to approximate conditional survival probabilities.
result The recursive quantization method provides a way to approximate conditional survival probabilities under noisy data.
Paper introduces novel survival models for handling censored data.
problem Complex data structures and heavy censoring in survival analysis.
method Combines imprecise probability theory with attention mechanisms.
result Proposed models, especially iSurvJ, outperform traditional methods.
DNNSurv uses pseudo values to simplify deep learning for survival analysis.
problem Modeling survival data using deep learning methods.
method Two-step approach: transform survival times into pseudo values, then use them in a deep neural network.
result Deep neural networks can be applied to survival analysis by simplifying the problem into a regression task.
Proposes isotonic regression for calibrating Deep Cox models' survival probabilities.
problem Poor calibration of Deep Cox models' survival probabilities.
method Isotonic regression for post hoc calibration of Deep Cox models.
result Establishes favorable theoretical guarantees and demonstrates empirical effectiveness.
First passage models, where corporate assets undergo correlated random walks and a company defaults if its assets fall below a threshold provide an attractive framework for modeling the default process. Typical one year default correlations are small, i.e., of order a few percent, but nonetheless including correlations…
Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging …
GBST model improves credit risk quantification using survival analysis.
problem Quantifying credit risk in heterogeneous consumer finance data.
method Gradient boosting survival tree (GBST) model integrating survival analysis and gradient boosting.
result GBST model outperforms existing survival models in credit risk quantification.
Paper proposes robust methods for estimating optimal treatment rules with censored survival data.
problem Estimating optimal treatment rules for censored survival data.
method Developed two robust criteria and a sampling-based difference-of-convex algorithm for learning optimal treatment rules.
result Proposed methods show improved performance compared to existing methods in simulations and real data.
The paper analyzes how contagion affects the survival probability of investment groups in microfinance.
problem The impact of contagion on the survival probability of investment groups in microfinance.
method A probabilistic approach to compute group survival probability with and without contagion effects.
result In homogeneous groups, including more members increases the probability of eventual default to 1.
Paper proposes a deep learning method to estimate fill probabilities of limit orders in LOBs.
problem Estimating the fill probabilities of limit orders in different levels of a limit order book.
method Survival analysis model using a convolutional-Transformer encoder and a monotonic neural network decoder.
result The proposed method significantly outperforms other approaches in survival analysis.
Survival analysis models predict loan write-off risk under IFRS 9.
problem Estimating loan write-off probabilities in credit risk modeling.
method Discrete-time hazard model and conditional inference survival tree compared to cross-sectional logistic regression.
result Discrete-time hazard model outperforms other two-stage LGD-models.
We study the tick dynamical behavior of the bond futures in Korean Futures Exchange(KOFEX) market. Since the survival probability in the continuous-time random walk theory is applied to the bond futures transaction, the form of the decay function in our bond futures model is discussed from two kinds of Korean Treasury …
A new bandit problem where experiments can be interrupted if results are not promising.
problem Interruptible multi-armed bandit problem with a threshold for cumulative reward.
method Formalized survival regret, identified key components (regret and probability of ruin), derived lower bounds and optimal policies.
result No policy can achieve sublinear survival regret, but optimal policies minimize survival regret in a Pareto sense.
We analyze the data of the Italian and U.S. futures on the stock markets and we test the validity of the Continuous Time Random Walk assumption for the survival probability of the returns time series via a renewal aging experiment. We also study the survival probability of returns sign and apply a coarse graining proce…
New methods improve off-policy evaluation for survival outcomes with censoring.
problem Systematic underestimation of policy performance due to censoring bias in survival outcomes.
method Proposes IPCW-IPS and IPCW-DR to handle censoring bias in survival outcomes.
result The proposed methods are unbiased and achieve double robustness.
Survival analysis is a type of semi-supervised ranking task where the target output (the survival time) is often right-censored. Utilizing this information is a challenge because it is not obvious how to correctly incorporate these censored examples into a model. We study how three categories of loss functions, namely …
The paper develops a model to predict IPO events in private equity investments.
problem Lack of publicly available quantitative information for predicting IPO events.
method Combines neural network and survival analysis for predicting IPO probability.
result The neuro-survival model accurately predicts IPO events across various sectors.
RSM provides insights into deep survival models' decision-making.
problem Ensuring trust in deep survival models' predictions for healthcare applications.
method Reverse survival model (RSM) framework that explains deep survival models' decisions.
result RSM extracts relevant features for deep survival models' predictions.
New method balances covariates for stable causal survival effect estimation.
problem Estimating causal survival effects in data with conditionally-independent censoring.
method Covariate-balancing approach to empirically stable and asymptotically efficient estimation.
result Validated theoretical results in synthetic and semi-synthetic data.
Nonasymptotic error bounds and strong consistency rates for survival analysis methods.
problem Establishing reliable error bounds and consistency rates for survival analysis methods.
method Nonasymptotic error bounds for Kaplan-Meier-based nearest neighbor and kernel survival probability estimators in metric spaces.
result Rates of strong consistency match existing lower bounds for conditional CDF estimation.
The paper introduces beta survival models to handle heterogeneous populations.
problem Estimating time until an event with heterogeneous survival rates.
method Leveraging beta distribution to characterize unobserved heterogeneity in survival rates.
result The beta survival models provide more accurate predictions and interesting ranking implications.
Paper develops methods for estimating patient-specific survival curve prediction intervals.
problem Lack of uncertainty quantification in ISD models.
method Adapted existing method for ISD survival curve analysis.
result New methods provide accurate simultaneous prediction intervals.
Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the exi…
The paper develops deep learning models for personalized treatment rules in survival analysis.
problem Deriving optimal treatment rules for bivariate survival outcomes in randomized trials.
method Adaptive prediction-powered learning using deep neural networks and stochastic policies.
result Maximizes joint survival probability beyond fixed time points (t1,t2). Improves survival prediction model calibration for better individual decision-making.
problem Survival prediction's marginal and conditional calibration issues.
method Conformal prediction using individual survival probabilities.
result Effective marginal and conditional calibration without compromising discrimination.
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.
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.
Bayesian model estimates ACT impact on pediatric AML survival.
problem Estimating ACT impact on survival in AML patients with treatment timing issues.
method Generative Bayesian semi-parametric model with Gamma Process priors.
result Posterior inference for ACT efficacy under dynamic rules.
SurvTRACE uses transformers to analyze survival times with competing events.
problem Analyzing survival times with multiple competing events and confounders.
method Transformer-based model for survival analysis, handling competing events and confounders.
result SurvTRACE outperforms existing methods in multi-event scenarios.
This paper presents an original approach for jointly fitting survival times and classifying samples into subgroups. The Coxlogit model is a generalized linear model with a common set of selected features for both tasks. Survival times and class labels are here assumed to be conditioned by a common risk score which depe…
Metaparametric neural networks improve survival analysis without prior knowledge.
problem Current neural networks restrict survival analysis to pre-determined times and fixed function shapes.
method Metaparametric neural network framework that extends existing methods to estimate generic functions.
result Metaparametric neural networks outperform state-of-the-art methods in capturing nonlinearities and identifying temporal patterns.
A new method for survival analysis models that ensures fairness without using sensitive demographic data.
problem Ensuring fairness in survival analysis models without relying on sensitive demographic information.
method A worst-case error minimization approach using a training loss function that does not know sensitive demographic information.
result The proposed method often scores better on fairness metrics without a significant drop in prediction accuracy compared to baselines.
Improved survival analysis using square root Cox's models and neural networks.
problem Feature selection in survival analysis.
method Square root Cox's survival analysis by the fittest linear and neural networks model, directly tuning penalty parameter λ.
result Substantially improved over traditional methods, achieving phase transition in feature selection.
Proposes a new model to better handle correlation risk in credit risk calculations.
problem Empirical evidence shows correlation risk is significant in credit risk models.
method Introduces a stochastic correlation extension of the Vasicek model using circular diffusion.
result Demonstrates how correlation volatility and persistence affect joint default and survival probabilities.
Kernel Induced Random Survival Forests (KIRSF) is a statistical learning algorithm which aims to improve prediction accuracy for survival data. As in Random Survival Forests (RSF), Cumulative Hazard Function is predicted for each individual in the test set. Prediction error is estimated using Harrell's concordance inde…
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…
Probabilistic survival predictions from models trained with Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high variance. The field of meteorology, where the paradigm of maximizing sharpness subject to calibration is popular, has addressed this problem by using scoring rules beyond MLE, s…
This note explores the mathematical theory to solve modern gamblers ruin problems. We establish a ruin framework and solve for the probability of bankruptcy. We also show how this relates to the expected time to bankruptcy and review the risk neutral probabilities associated an adjustment to asymmetrical views.
Optimal reinsurance strategies for multi-line insurance companies.
problem Choosing the best dynamic reinsurance policies for multi-line insurance companies.
method Characterized the optimal survival function as the unique nondecreasing viscosity solution of the HJB equation, solved numerically using the finite difference method.
result Provided proof of convergence of numerical solution to the survival probability function.
We develop a model to study the role of rationality in economics and biology. The model's agents differ continuously in their ability to make rational choices. The agents' objective is to ensure their individual survival over time or, equivalently, to maximize profits. In equilibrium, however, rational agents who maxim…
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.
There is currently great interest in applying neural networks to prediction tasks in medicine. It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator. This avoids information loss when training the model and enables generation o…
In this paper we use a hybrid Monte Carlo-Optimal quantization method to approximate the conditional survival probabilities of a firm, given a structural model for its credit defaul, under partial information. We consider the case when the firm's value is a non-observable stochastic process (Vt)t≥0 and inver…
Model predicts sponsorship ROI using renewal probability.
problem Difficulty in measuring return on investment from sponsorships.
method Survival analysis using sponsorship renewal as proxy.
result Predicted values for sponsor's renewal probability and duration.
New metric reduces estimation error in survival model evaluation.
problem Dependent censoring complicates survival model evaluation.
method Dependent Brier score based on Archimedean copula and Copula-Graphic estimator.
result Reduces estimation error by 12-16% on average.