The paper examines stochastic inequalities involving minimum and maximum claim amounts.
problem Investigating stochastic inequalities for claim amounts with random number of claims.
method Analyzing stochastic order and reversed hazard rate order for minimum and maximum claim amounts.
result Strengthening and generalizing existing results in the literature.
SJDs unify masked, continuous, and hybrid diffusion models.
problem Unified modeling of diffusion processes.
method Continuous-time Markov processes with token embeddings and hazard rates.
result Unified model recovers masked, continuous, and hybrid diffusion as limits.
The standard deviation and Gini mean difference order based on tail behavior.
problem Ordering between standard deviation and Gini mean difference for real-valued risks.
method Analysis of the mean excess function of the pairwise difference ∣X−X′∣. result Dominance regimes of SD and GMD are determined by tail behavior of the distribution.
RER improves sample complexity by updating in reverse order.
problem Theoretical analysis limits RER's convergence rate.
method Tighter analysis for larger learning rates and longer sequences.
result RER converges faster with larger learning rates and longer sequences.
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…
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.
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.
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.
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.
Paper proposes a method to estimate confidence bands for survival random forests.
problem No statistically valid and computationally feasible approach for estimating confidence bands for survival random forests.
method Extending recent developments in infinite-order incomplete U-statistics, the paper proposes an unbiased confidence band estimation.
result The proposed method accurately estimates the confidence band and achieves desired coverage rate.
This study analyses the duration dependence of events that trigger volatility persistence in stock markets. Such events, in our context, are monthly spells of contiguous price decline or negative returns for the S&P500 stock market index over the last 145 years. Factors known to affect the duration of these spells are …
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 …
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.
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…
We present a plausible micro-founded model for the previously postulated power law finite time singular form of the crash hazard rate in the Johansen-Ledoit-Sornette model of rational expectation bubbles. The model is based on a percolation picture of the network of traders and the concept that clusters of connected tr…
The paper characterizes equilibrium strategies under random risk aversion, showing unique solutions based on risk aversion distribution.
problem Characterizing equilibrium strategies in a continuous-time portfolio selection problem under random risk aversion.
method Provided a complete characterization of all deterministic equilibrium strategies in closed form, analyzing the structure of the solution based on the distribution of random risk aversion.
result The equilibrium is unique (if exists) when the expectation of random risk aversion is finite, but infinite expectation leads to either infinitely many equilibria or a unique trivial one.
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 …
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.
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.
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…
Novel framework identifies pump-specific deterioration rates using Bayesian hierarchical hazard modeling and causal discovery.
problem Challenges in asset management due to heterogeneous deterioration rates in pump equipment.
method Bayesian hierarchical hazard modeling with causal discovery, GPU-accelerated No-U-Turn Sampling (NUTS), and DirectLiNGAM.
result Identified striking heterogeneity in deterioration rates, with negative effects 400 times larger than positive effects.
Predictive rate-distortion analysis suffers from the curse of dimensionality: clustering arbitrarily long pasts to retain information about arbitrarily long futures requires resources that typically grow exponentially with length. The challenge is compounded for infinite-order Markov processes, since conditioning on fi…
We introduce a new stochastic smoothing perspective to study adversarial contextual bandit problems. We propose a general algorithm template that represents random perturbation based algorithms and identify several perturbation distributions that lead to strong regret bounds. Using the idea of smoothness, we provide an…
Consider the problem of pricing options on forwards in energy markets, when spot prices follow a geometric multi-factor model in which several rates of mean reversion appear. In this paper we investigate the role played by slow mean reversion when pricing and hedging options. In particular, we determine both upper and …
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.
In recent years, a market for mortality derivatives began developing as a way to handle systematic mortality risk, which is inherent in life insurance and annuity contracts. Systematic mortality risk is due to the uncertain development of future mortality intensities, or {\it hazard rates}. In this paper, we develop a …
Adaptive algorithm improves convergence rate of Langevin dynamics.
problem Improving convergence rate of Langevin dynamics.
method Adaptive non-reversible stochastic gradient Langevin dynamics algorithm.
result Improved convergence rate of the algorithm.
AI analyzes corporate ESG filings to identify key dimensions and investor reactions.
problem Lack of reliable ESG ratings systems in corporate filings.
method AI techniques to separate and measure ESG dimensions and investor responses.
result AI can improve ESG ratings systems by identifying key dimensions and investor reactions.
A new method recovers latent potentials from graph flows, preserving ordering and stability.
problem Recovering latent potentials from graph flows is ill-posed and standard methods collapse the ordering.
method Gauge-invariant, parameter-insensitive regularization using Dirichlet energy.
result The method preserves ordering and stability across different regularization strengths.
Survival analysis of 832,941 Solana token launches shows a significant decline in graduation rate.
problem Analyzing the survival rate of Solana token launches and identifying factors affecting graduation.
method Survival analysis using Kaplan-Meier and Cox proportional-hazards models.
result The survival rate of Solana token launches has declined significantly, with a 3.18x decrease from previous rates.
New method detects inconsistencies in AHP matrices using triadic preference reversals.
problem Challenges in assessing consistency in AHP pairwise comparison matrices.
method Triadic preference reversals to detect inconsistencies between pairs of elements.
result 97% accuracy in detecting inconsistencies, significantly surpassing traditional methods.
In this paper we investigate the local risk-minimization approach for a combined financial-insurance model where there are restrictions on the information available to the insurance company. In particular we assume that, at any time, the insurance company may observe the number of deaths from a specific portfolio of in…
Paper evaluates deadline-ILS on insider trading contracts, finding it distinguishes signals from noise.
problem Deadlines in insider trading contracts and information leakage detection.
method Empirical evaluation using FFIC dataset, hazard-rate estimation, cross-market wallet analysis.
result Deadline-ILS distinguishes signal from proxy artefact, with a significant shift in magnitude.
Investigates geometric mean reversion process using Lie symmetry method.
problem Describes dynamics of short-term interest rates.
method Lie symmetry method and optimal system of invariant solutions.
result Constructs an optimal system of invariant solutions.
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.
We introduce Dirac processes, using Dirac delta functions, for short-rate-type pricing of financial derivatives. Dirac processes add spikes to the existing building blocks of diffusions and jumps. Dirac processes are Generalized Processes, which have not been used directly before because the dollar value of non-Real nu…
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.
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.
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.
We propose a new model for pricing Quanto CDS and risky bonds. The model operates with four stochastic factors, namely: hazard rate, foreign exchange rate, domestic interest rate, and foreign interest rate, and also allows for jumps-at-default in the FX and foreign interest rates. Corresponding systems of PDEs are deri…
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.
Empirical study on trends reversion in financial markets.
problem Understanding when trends in financial markets revert.
method Polynomial regression and bootstrapping on 30 years of daily futures prices.
result Trends revert when they reach a critical level of statistical significance.
BoXHED2.0 boosts survival analysis for complex data.
problem Survival analysis with time-dependent covariates.
method Tree-boosted hazard estimator, fully nonparametric, scalable.
result Scalable to parametric boosted survival models in speed.
The paper classifies reversible and strongly reversible elements in Hermitian isometry groups.
problem Classifying reversible and strongly reversible elements in Hermitian isometry groups.
method Classification through group theory and algebraic manipulation.
result New classification of strongly reversible elements in Sp(n).
A new method prices time-to-event cash flows using survival analysis.
problem Pricing insurance investment portfolios with time-to-event cash flows.
method Discrete-time survival analysis framework, hazard rate estimators, asymptotic multivariate normality.
result Pricing model yields estimates closer to actual cash flows than non-random models.
Let G be a group. An element g in G is called reversible if it is conjugate to g−1 within G, and called strongly reversible if it is conjugate to its inverse by an order two element of G. Let HHn be the n-dimensional quaternionic hyperbolic space. Let PSp(n,1) be the i…
New optimization methods improve Cox Proportional Hazards model training for high-dimensional data.
problem Vanishing second order derivatives in Newton method prevent convergence for high-dimensional CPH model training.
method Construct and minimize surrogate functions exploiting hidden mathematical structures of CPH model.
result Global convergence and monotonic loss decrease, leading to sparse and high-quality models.