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arXiv research

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.

168,742 papers · 148 categories

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48 results for vaccine allocation

Italy's vaccine coverage fell, leading to political debates and online social media discussions.

problem Low vaccine coverage and political debates on immunization in Italy.
method Sentiment analysis of tweets in Italian during 2018 to assess public opinion on vaccines.
result There was disorientation among the public due to political announcements, as evidenced by Twitter data.

UK's rapid vaccine rollout linked to reduced COVID-19 mortality.

problem Assessing the impact of accelerated vaccine rollout on public health outcomes.
method Flexible probabilistic models combining interrupted time series analysis and synthetic control methods with multi-output Gaussian processes.
result Substantial reduction in COVID-19 mortality with little effect on transmission rates.

A new method improves estimation of COVID-19 vaccine effectiveness.

problem Estimating vaccine effectiveness under the test-negative design.
method A doubly robust estimator (TNDDR) using cross-fitting and machine learning.
result The TNDDR estimator is n\sqrt{n}-consistent, asymptotically normal, and doubly robust.

Estimates vaccine effectiveness and immune correlates in TND studies with missing data.

problem Confounding and missing data in TND studies of vaccine effectiveness and immune correlates.
method Targeted maximum likelihood estimation using a semiparametric logistic regression model.
result Valid causal inference of vaccine effectiveness and immune correlates in TND studies with missing exposure data.

Study examines how COVID-19 vaccine companies' popularity affects their stock prices.

problem Impact of COVID-19 vaccine development and rollout on stock prices and company popularity.
method Used Python and various libraries to analyze Google Trends data and stock prices of five vaccine companies.
result Significant correlation between Google Trend data and stock prices, with post-rollout periods showing a slight negative correlation.

The study uses neural networks to classify and predict coronavirus data.

problem Classifying and predicting coronavirus data from input variables.
method Artificial neural networks with logcosh loss function to classify branches of set-valued mappings.
result Successfully classified and predicted coronavirus data for each German district.

Deep neural networks predict B-cell epitopes for SARS-CoV and SARS-CoV-2.

problem Accurate prediction of B-cell epitopes for vaccine design.
method Deep neural network model with regularization techniques and key features analysis.
result Overall accuracy of 82% in predicting COVID-19 cases.

Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.

problem Challenges in patient triage, treatment, and care management during the pandemic.
method Integrated four-step approach combining descriptive, predictive, and prescriptive analytics.
result Optimized resource allocation and informed policy decisions.

Estimates the effect of time-varying treatments using machine learning.

problem Estimating the impact of time-varying treatments over multiple periods.
method Difference-in-Differences framework with double/debiased machine learning.
result Higher vaccination rates reduce COVID-19 mortality after several weeks.

Study relaxes identification assumptions for natural direct effects in non-randomized settings.

problem Identifying causal direct effects under unmeasured confounding.
method Developed relaxed conditions for identifying natural direct effects in non-randomized settings.
result Identified natural direct effect under unmeasured confounding conditions.

Personalized cancer vaccines are envisioned as the next generation rational cancer immunotherapy. The key step in developing personalized therapeutic cancer vaccines is to identify tumor-specific neoantigens that are on the surface of tumor cells. A promising method for this is through de novo peptide sequencing from m…

2019-04-17abs ↗pdf ↗

Study assesses weakly-supervised methods for rare outcomes in medical records.

problem Identifying patients with specific medical conditions using electronic health records.
method Compared three methods (PheNorm, MAP, and sureLDA) in simulations with varying outcomes and silver labels.
result No single method consistently outperformed others, but sureLDA often did well.

New method removes interference bias in causal models.

problem Interference bias impedes causal effect identification in real-world settings.
method Novel definition of causal models with local interference, semi-parametric assumptions.
result True Average Causal Effect can be identified in certain semi-parametric models with local interference.

The paper explores fairness, welfare, and equity in personalized pricing across various applications.

problem Interplay of fairness, welfare, and equity in personalized pricing based on customer features.
method Comprehensive literature review and observational metrics without underlying valuation distribution assumptions.
result Personalized pricing can expand access, improve welfare, and increase revenue or budget utilization.

The paper optimizes DIA purchase policies using lifecycle models and asset allocation.

problem Determining the optimal allocation to Deferred Income Annuities (DIAs).
method Employed a lifecycle model with utility of consumption and bequest, formalized optimization process, analyzed results, and extended model to include asset allocation.
result Optimal DIA allocation varies based on refundability, asset allocation, and perceived longevity.

Study optimizes resource allocation in noisy systems for better control.

problem Limited attention in stochastic systems with multiplicative noise.
method Analytical and numerical methods for optimal attention allocation.
result Effective resource allocation enhances noise estimation and control decisions.

The paper proposes an asset allocation strategy using the Sortino ratio for better performance.

problem Traditional asset allocation methods like the Sharpe ratio do not penalize negative returns adequately.
method The Sortino ratio is used to maximize asset allocation, penalizing only negative return variances.
result The Sortino ratio-based strategy outperforms traditional methods like the Kelly criterion.

This paper tackles post-trade allocation inefficiencies and presents a uniform return allocation method.

problem Return divergence among accounts after trade allocation.
method Systematic treatment of trade allocation risk, presenting a uniform return allocation method.
result Uniform allocation of returns irrespective of the number of accounts and trade sizes.

This paper examines allocation mechanisms in markets with transfer costs, showing how these costs affect economic efficiency.

problem Transfer costs in decentralized exchange markets reduce economic efficiency.
method An axiomatic study of allocation mechanisms in the presence of transfer costs, providing robust and conditional mean allocation mechanisms.
result Robust and conditional mean allocation mechanisms are identified, relating to risk sharing in agent pools.

The aims of this study are twofold. First, we consider an optimal risk allocation problem with non-convex preferences. By establishing an infimal representation for distortion risk measures, we give some necessary and sufficient conditions for the existence of optimal and asymptotic optimal allocations. We will show th…

2015-03-15abs ↗pdf ↗

New method allocates capital based on tail central moments for financial risk assessment.

problem Inability of CTE-based capital allocation to reflect tail behavior of losses.
method Developed TCM-based capital allocation for normal mean-variance mixture distributions.
result TCM-based method captures tail risk contributions not detected by CTE.

The paper explores capital allocation using Euler formula with VaR and ES, revealing non-monotonicity and providing estimation methods.

problem Non-monotonicity in VaR-based capital allocation and the need for consistent risk measures.
method Use of Euler formula, Value-at-Risk (VaR), Expected shortfall (ES), simulation, and Markov chain Monte Carlo.
result Capital allocation with VaR is not monotonous, and consistent risk measures are crucial.

Capital allocation principles are used in various contexts in which a risk capital or a cost of an aggregate position has to be allocated among its constituent parts. We study capital allocation principles in a performance measurement framework. We introduce the notation of suitability of allocations for performance me…

2013-01-23abs ↗pdf ↗

The financial crisis showed the importance of measuring, allocating and regulating systemic risk. Recently, the systemic risk measures that can be decomposed into an aggregation function and a scalar measure of risk, received a lot of attention. In this framework, capital allocations are added after aggregation and can…

2016-07-12abs ↗pdf ↗

The paper analyzes insurance pricing and capital allocation in imperfect markets.

problem Analyzing insurance pricing and capital allocation in imperfect markets.
method Non-additive distortion pricing functional and principle of equal priority of payments in default.
result Derives the natural allocation of premium and margin with properties that merit the name.

We study the problem of allocating stocks to dark pools. We propose and analyze an optimal approach for allocations, if continuous-valued allocations are allowed. We also propose a modification for the case when only integer-valued allocations are possible. We extend the previous work on this problem to adversarial sce…

2010-03-11abs ↗pdf ↗

The European insurance sector will soon be faced with the application of Solvency 2 regulation norms. It will create a real change in risk management practices. The ORSA approach of the second pillar makes the capital allocation an important exercise for all insurers and specially for groups. Considering multi-branches…

2015-06-12abs ↗pdf ↗

In this paper we develop a novel methodology for estimation of risk capital allocation. The methodology is rooted in the theory of risk measures. We work within a general, but tractable class of law-invariant coherent risk measures, with a particular focus on expected shortfall. We introduce the concept of fair capital…

2019-02-26abs ↗pdf ↗

This work reviews and tests risk allocation strategies in finance, highlighting Shapley allocation's advantages.

problem Risk allocation in financial institutions with non-additive risk measures and layered structures.
method Systematic review of risk allocation strategies, testing in simplified and realistic settings, including Basel 2.5 and FRTB.
result Shapley allocation offers the best compromise between simplicity, mathematical properties, and computational cost.

Enhances topic models to better handle polysemous words.

problem Lack of polysemy handling in Gaussian latent Dirichlet allocation.
method Introduces a hierarchical structure to capture polysemy in Gaussian latent Dirichlet allocation.
result Significantly improves polysemy detection and provides more parsimonious topic representations.

Study resource allocation strategies in sequential decisions with unknown rewards.

problem Sequential resource allocation with unknown rewards.
method Design combinatorial multi-armed bandit algorithms for discrete or continuous budgets.
result Prove algorithms achieve logarithmic cumulative regret under semi-bandit feedback.

Facing the FRTB, banks need to allocate their capital to each business units or risk positions to evaluate the capital efficiency of their strategies. This paper proposes two computationally efficient allocation methods which are weighted according to liquidity horizon. Both methods provide more stable and less negativ…

2018-01-23abs ↗pdf ↗

A Nash game theory approach allocates capital requirements among financial institutions.

problem Allocating systemic risk measures among financial institutions.
method Proposes a Nash allocation rule inspired by game theory.
result Provides sufficient conditions for the existence and uniqueness of Nash allocation rules.

The paper evaluates index-based allocation policies using data from randomized control trials.

problem Evaluating index-based allocation policies in resource-scarce scenarios.
method Using data from randomized control trials, the paper introduces an efficient estimator and methods for computing asymptotically correct confidence intervals.
result Valid statistical conclusions can be drawn for index-based allocation policies.

Develops a framework to analyze financial structures.

problem Difficulty in systematic analysis, comparison, and verification of financial structures.
method Formalizes financial structures as structured allocation systems with explicit allocation operators.
result Specifies inputs, structural requirements, and feasibility restrictions for financial structures.

Estimates Mozambique's population using remote sensing and microcensus data.

problem Lack of frequent population estimation due to censuses lacking spatio-temporal resolution.
method Combines remote sensing, microcensus data, and transfer learning with publicly available datasets.
result Population predictions improve with footprint area estimation using transfer learning.