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.
The paper develops deep learning models for personalized treatment rules in survival analysis.
Method estimates joint distribution of bivariate outcomes.
Optimal treatment regimes (OTR) are individualised treatment assignment strategies that identify a medical treatment as optimal given all background information available on the individual. We discuss Bayes optimal treatment regimes estimated using a loss function defined on the bivariate distribution of dichotomous po…
In the article the necessary and sufficient conditions for a representation of Lipschitz function of two variables as a difference of two convex functions are formulated. An algorithm of this representation is given. The outcome of this algorithm is a sequence of pairs of convex functions that converge uniformly to a p…
New methods optimize sums of bivariate functions on finite domains.
Causal knowledge is vital for effective reasoning in science, as causal relations, unlike correlations, allow one to reason about the outcomes of interventions. Algorithms that can discover causal relations from observational data are based on the assumption that all variables have been jointly measured in a single dat…
We define parametrized cobordism categories and study their formal properties as bivariant theories. Bivariant transformations to a strongly excisive bivariant theory give rise to characteristic classes of smooth bundles with strong additivity properties. In the case of cobordisms between manifolds with boundary, we pr…
Constructs bivariate quantiles using vine copulas for multivariate analysis.
Proposes bivariate DeepKriging for efficient wind field prediction.
We collect well known and less known facts about the bivariate normal distribution and translate them into copula language. In addition, we prove a very general formula for the bivariate normal copula, we compute Gini's gamma, and we provide improved bounds and approximations on the diagonal.
Study classifies mappings of bivariate normal densities, revealing three types with distinct geometric and statistical properties.
Study uses a bivariate model to price crude oil futures.
Worst-case bounds on the expected shortfall risk given only limited information on the distribution of the random variables has been studied extensively in the literature. In this paper, we develop a new worst-case bound on the expected shortfall when the univariate marginals are known exactly and additional expert inf…
We show that gamma distributions provide models for departures from randomness since every neighbourhood of an exponential distribution contains a neighbourhood of gamma distributions, using an information theoretic metric topology. We derive also the information geometry of the 3-manifold of McKay bivariate gamma dist…
In this paper we consider a family of Dirac-type operators on fibration equivariant with respect to an action of an etale groupoid. Such a family defines an element in the bivariant theory. We compute the action of the bivariant Chern character of this element on the image of Connes' map in the cyclic…
The study evaluates financial risk using copulas and statistical tests.
New method improves speed of estimating bivariate functional data.
Modeling stock returns and volatility using a bivariate gamma generalized Laplace law.
Researchers study the conformal geometry of bivariate Gaussian manifolds.
Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attain low classification errors. The resulting classifier is linear in the log-transformed univariate and b…
TRA detects causal direction from bivariate data using geometric shapes.
Long Short-Term Memory (LSTM) infers the long term dependency through a cell state maintained by the input and the forget gate structures, which models a gate output as a value in [0,1] through a sigmoid function. However, due to the graduality of the sigmoid function, the sigmoid gate is not flexible in representing m…
The paper introduces a new model to correct bias in treatment effect estimates due to sample selection.
New method improves bivariate causal discovery by accurately estimating cause variable complexity.
The Bivariate Dynamic Contagion Processes (BDCP) are a broad class of bivariate point processes characterized by the intensities as a general class of piecewise deterministic Markov processes. The BDCP describes a rich dynamic structure where the system is under the influence of both external and internal factors model…
Paper proves global optimality of a simple optimization scheme for learning DAG models.
By building on a recently introduced genetic-inspired attribute-based conceptual framework for safety risk analysis, we propose a novel methodology to compute construction univariate and bivariate construction safety risk at a situational level. Our fully data-driven approach provides construction practitioners and aca…
Paper introduces statistical learning for point processes.
Gradient-based methods can be biased by distributional asymmetries in bivariate categorical data.
Paper introduces MTCM to measure multivariate tail dependence.
Study assesses drought and late-frost risks in Bavaria using vine copulas.
A new Heckman selection model uses a bivariate contaminated normal distribution for more accurate data analysis.
Our goal in this paper is to propose an alternative risk measure which takes into account the fluctuations of losses and possible correlations between random variables. This new notion of risk measures, that we call Copula Conditional Tail Expectation describes the expected amount of risk that can be experienced given …
Causal inference using observational data is challenging, especially in the bivariate case. Through the minimum description length principle, we link the postulate of independence between the generating mechanisms of the cause and of the effect given the cause to quantile regression. Based on this theory, we develop Bi…
Develops a new bivariate process for energy markets with improved simulation methods.
Develops a framework for consistent pricing of interest rate derivatives.
New method infers causal relationships from nonstationary time series data.
New method distinguishes cause from effect using causal velocity.
Deep Q-learning is investigated as an end-to-end solution to estimate the optimal strategies for acting on time series input. Experiments are conducted on two idealized trading games. 1) Univariate: the only input is a wave-like price time series, and 2) Bivariate: the input includes a random stepwise price time series…
A new class of bivariate distributions is introduced that extends the Generalized Marshall-Olkin distributions of Li and Pellerey (2011). Their dependence structure is studied through the analysis of the copula functions that they induce. These copulas, that include as special cases the Generalized Marshall-Olkin copul…
Extended PELCoV for bivariate Student-t copulas to monitor foreign exchange risk.
New method optimizes processes under constraints using bivariate Gaussian models.
A method is developed to estimate the parameters of a Levy copula of a discretely observed bivariate compound Poisson process without knowledge of common shocks. The method is tested in a small sample simulation study. Also, the method is applied to a real data set and a goodness of fit test is developed. With the meth…
The paper studies a gradient system on a beta statistical manifold, proving integrability and deriving explicit expressions.
Analysis of long-range dependence in financial time series was one of the initial steps of econophysics into the domain of mainstream finance and financial economics in the 1990s. Since then, many different financial series have been analyzed using the methods standardly used outside of finance to deliver some importan…
New neural networks learn distribution functions using quantiles and moments.
There are three equivalent ways of representing two jointly observed real-valued signals: as a bivariate vector signal, as a single complex-valued signal, or as two analytic signals known as the rotary components. Each representation has unique advantages depending on the system of interest and the application goals. I…
Method estimates bivariate causal models using normalising flows and variational Gaussian process regression.