A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Machine learning predicts mask mandates reduce COVID-19 deaths.
AI generates a sequence of death causes from hospital records.
Study uses machine learning to analyze state drug policies and reduce overdose deaths.
Study uses artificial counterfactuals to show lockdowns reduced US case and death counts.
Study birth-death dynamics for sampling Gibbs measures with nonconvex potentials.
New RL algorithm reduces regret in birth-death queueing problems.
condLSTM-Q predicts COVID-19 deaths at county level with quantile forecasts.
Deep learning improves death cause coding accuracy.
Model predicts US COVID-19 deaths with quantile estimates.
Recently we developed a new framework in Hirz et al (2015) to model stochastic mortality using extended CreditRisk methodology which is very different from traditional time series methods used for mortality modelling previously. In this framework, deaths are driven by common latent stochastic risk factors which may…
Study shows death ratio of COVID-19 deaths increases financial volatility.
This paper addresses the risk-minimization problem, with and without mortality securitization, à la Föllmer-Sondermann for a large class of equity-linked mortality contracts when no model for the death time is specified. This framework includes the situation where the correlation between the market model and the time o…
Study on trainability of ReLU networks and proposes data-dependent initialization method.
Study identifies clusters of EU countries with similar young mortality patterns.
We propose a general method to obtain approximation of the first passage time distribution for the birth-death processes. We rely on the general properties of birth-death processes, Keilson's theorem and the concept of Riemann sum to obtain closed-form expressions. We apply the method to the three selected birth-death …
Jointly models cause-of-death mortality rates across multiple countries and genders.
New method evaluates personalized treatment in critical care, robust to death.
We consider models of the population or opinion dynamics which result in the non-linear stochastic differential equations (SDEs) exhibiting the spurious long-range memory. In this context, the correspondence between the description of the birth-death processes as the continuous-time Markov chains and the continuous SDE…
Study shows oil prices but not COVID-19 cases affect US economic policy uncertainty.
A new sampling algorithm speeds up Langevin sampling for multimodal distributions.
We find the optimal investment strategy for an individual who seeks to minimize one of four objectives: (1) the probability that his wealth reaches a specified ruin level {\it before} death, (2) the probability that his wealth reaches that level {\it at} death, (3) the expectation of how low his wealth drops below a sp…
New method uses birth-death process and exploration component to accelerate sampling from multimodal distributions.
New method tests Granger non-causality in panel data with cross-sectional dependencies.
A Longitudinal Attribute-Conditioned Neural Network (LANTERN) framework for modeling health-state transition probabilities in irregular longitudinal data.
Multiple cause-of-death data provides a valuable source of information that can be used to enhance health standards by predicting health related trajectories in societies with large populations. These data are often available in large quantities across U.S. states and require Big Data techniques to uncover complex hidd…
Patients who suffer an acute coronary syndrome are at elevated risk for adverse cardiovascular events such as myocardial infarction and cardiovascular death. Accurate assessment of this risk is crucial to their course of care. We focus on estimating a patient's risk of cardiovascular death after an acute coronary syndr…
In this paper we present a numerical valuation of variable annuities with combined Guaranteed Minimum Withdrawal Benefit (GMWB) and Guaranteed Minimum Death Benefit (GMDB) under optimal policyholder behaviour solved as an optimal stochastic control problem. This product simultaneously deals with financial risk, mortali…
Bayesian networks (BNs) are graphical models that are useful for representing high-dimensional probability distributions. There has been a great deal of interest in recent years in the NP-hard problem of learning the structure of a BN from observed data. Typically, one assigns a score to various structures and the sear…
Looking for associations among multiple variables is a topical issue in statistics due to the increasing amount of data encountered in biology, medicine and many other domains involving statistical applications. Graphical models have recently gained popularity for this purpose in the statistical literature. Following t…
Study quantifies how COVID-19 spread affects US stock markets.
In this paper we study the financial repercussions of the destruction of two fully armed and operational moon-sized battle stations ("Death Stars") in a 4-year period and the dissolution of the galactic government in Star Wars. The emphasis of this work is to calibrate and simulate a model of the banking and financial …
Study uses DNA methylation data to predict suicidal and non-suicidal deaths.
Probabilistic bounds on neuron death in deep networks, showing depth can be increased indefinitely.
New method improves phylogenetic model inference by 30x.
FS&P uses birth-death process to ensure global convergence of stochastic conic particle gradient descent.
Paper develops a method for valid inference using language model predictions from verbal autopsy narratives.
The paper audits trading filters, finding a high save-to-miss ratio.
Recently, a marked Poisson process (MPP) model for life catastrophe risk was proposed in [6]. We provide a justification and further support for the model by considering more general Poisson point processes in the context of extreme value theory (EVT), and basing the choice of model on statistical tests and model compa…
Near a birth-death critical point in a one-parameter family of gradient flows, there are precisely two Morse critical points of index difference one on the birth side. This paper gives a self-contained proof of the folklore theorem that these two critical points are joined by a unique gradient trajectory up to time-shi…
We propose a new Bayesian tracking and parameter learning algorithm for non-linear non-Gaussian multiple target tracking (MTT) models. We design a Markov chain Monte Carlo (MCMC) algorithm to sample from the posterior distribution of the target states, birth and death times, and association of observations to targets, …
The paper analyzes multivariate payments in multi-state life insurance using Markovian state processes.
In this article we investigate a state-space representation of the Lee-Carter model which is a benchmark stochastic mortality model for forecasting age-specific death rates. Existing relevant literature focuses mainly on mortality forecasting or pricing of longevity derivatives, while the full implications and methods …
New algorithm predicts lung cancer progression and mortality.
Model predicts drug overdose hotspots using EMS and toxicology data.
Flow Matching for count data improves sample quality and efficiency.
Health risks from cigarette smoking -- the leading cause of preventable death in the United States -- can be substantially reduced by quitting. Although most smokers are motivated to quit, the majority of quit attempts fail. A number of studies have explored the role of self-reported symptoms, physiologic measurements,…
SurvLatent ODE predicts VTE risk for cancer patients, outperforming current methods.
Decision trees are flexible models that are well suited for many statistical regression problems. In a Bayesian framework for regression trees, Markov Chain Monte Carlo (MCMC) search algorithms are required to generate samples of tree models according to their posterior probabilities. The critical component of such an …