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.
NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia
Stock networks, constructed from stock price time series, are a well-established tool for the characterization of complex behavior in stock markets. Following Mantegna's seminal paper, the linear Pearson's correlation coefficient between pairs of stocks has been the usual way to determine network edges. Recently, possi…
A framework detects nonlinear and interaction effects in epidemiological data with uncertainty quantification.
Develops DML for nonlinear panel data models with fixed effects.
Machine learning techniques have recently received significant attention as promising approaches to deal with the optical channel impairments, and in particular, the nonlinear effects. In this work, a machine learning-based classification technique, known as the Parzen window (PW) classifier, is applied to mitigate the…
We address the problem of distinguishing cause from effect in bivariate setting. Based on recent developments in nonlinear independent component analysis (ICA), we train nonparametrically general nonlinear causal models that allow non-additive noise. Further, we build an ensemble framework, namely Causal Mosaic, which …
Proposes CoDEAL for estimating heterogeneous treatment effects in panel data models.
New memory effect discovered in gravitational wave behavior.
Many financial variables are found to exhibit multifractal nature, which is usually attributed to the influence of temporal correlations and fat-tailedness in the probability distribution (PDF). Based on the partition function approach of multifractal analysis, we show that there is a marked finite-size effect in the d…
The leverage effect-- the correlation between an asset's return and its volatility-- has played a key role in forecasting and understanding volatility and risk. While it is a long standing consensus that leverage effects exist and improve forecasts, empirical evidence paradoxically do not show that most individual stoc…
Exact asymptotic solutions found for nonlinear Hawkes processes.
In this paper, we introduce a new machine learning (ML) model for nonlinear regression called the Boosted Smooth Transition Regression Trees (BooST), which is a combination of boosting algorithms with smooth transition regression trees. The main advantage of the BooST model is the estimation of the derivatives (partial…
Solves nonlinear problems on metric structures through eigenvalue counting.
Families of explicit solutions are found to a nonlinear Black-Scholes equation which incorporates the feedback-effect of a large trader in case of market illiquidity. The typical solution of these families will have a payoff which approximates a strangle. These solutions were used to test numerical schemes for solving …
Incorporating spatial information into hyperspectral unmixing procedures has been shown to have positive effects, due to the inherent spatial-spectral duality in hyperspectral scenes. Current research works that consider spatial information are mainly focused on the linear mixing model. In this paper, we investigate a …
Paper introduces ps-BART for estimating nonlinear ATE and CATE in continuous treatments.
Proposes a new AFT model for nonlinear survival data.
A new method quickly identifies key variables and interactions.
A new geometric shaping method is proposed, leveraging unsupervised machine learning to optimize the constellation design. The learned constellation mitigates nonlinear effects with gains up to 0.13 bit/4D when trained with a simplified fiber channel model.
Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direct…
Estimates effects of multiple interventions with hidden confounders using single-variable interventions.
Online social networks offer a new way to investigate financial markets' dynamics by enabling the large-scale analysis of investors' collective behavior. We provide empirical evidence that suggests social media and stock markets have a nonlinear causal relationship. We take advantage of an extensive data set composed o…
Generalization in nonlinear least squares can be studied via algorithmic stability and effective dimension.
The paper introduces a fast algorithm for learning and forecasting nonlinear dynamics from noisy time series data.
ResGCN detects anomalies in attributed networks by capturing sparsity and nonlinearity.
New method classifies nonlinear time series using deep CNNs and bispectra.
Market illiquidity, feedback effects, presence of transaction costs, risk from unprotected portfolio and other nonlinear effects in PDE based option pricing models can be described by solutions to the generalized Black-Scholes parabolic equation with a diffusion term nonlinearly depending on the option price itself. Di…
Estimates joint causal effects using single-variable interventions on nonlinear models.
Paper uses Koopman operator and Nyström method for efficient nonlinear control.
Function approximation from input and output data pairs constitutes a fundamental problem in supervised learning. Deep neural networks are currently the most popular method for learning to mimic the input-output relationship of a general nonlinear system, as they have proven to be very effective in approximating comple…
This work examines the stability of GD and SGD near minima, revealing nonlinear dynamics that differ from linear analysis.
In this paper, we prove that nonnegative polyharmonic functions on the upper half space satisfying a conformally invariant nonlinear boundary condition have to be the "\emph{polynomials} plus \emph{bubbles}" form. The nonlinear problem is motivated by the recent studies of boundary GJMS operators and the -curvature …
Bistable structures associated with non-linear deformation behavior, exemplified by the Venus flytrap and slap bracelet, can switch between different functional shapes upon actuation. Despite numerous efforts in modeling such large deformation behavior of shells, the roles of mechanical and nonlinear geometric effects …
Proposes a method to reveal nonlinearities in tensor data.
This paper extends performative prediction to nonlinear cases.
MMbeddings reduces categorical embeddings by treating them as latent effects, significantly decreasing parameters and mitigating overfitting.
New algorithms accelerate solving nonlinear matrix decomposition with ReLU.
Over the last decade, both the neural network and kernel adaptive filter have successfully been used for nonlinear signal processing. However, they suffer from high computational cost caused by their complex/growing network structures. In this paper, we propose two random Euler filters for complex-valued nonlinear filt…
Extends deep learning for nonlinear Cox regression variable selection.
Bayesian filtering approach identifies nonlinear restoring forces in dynamic systems.
By taking into account the nonlinear effect of the cause, the inner noise effect, and the measurement distortion effect in the observed variables, the post-nonlinear (PNL) causal model has demonstrated its excellent performance in distinguishing the cause from effect. However, its identifiability has not been properly …
The paper reviews identifiability in linear and nonlinear models, from Gaussian to non-Gaussian.
New method reveals true causal functions in nonlinear time series, not just scores.
New method learns nonlinear projections for reduced-order modeling of complex dynamical systems.
Spectral deconfounding improves machine learning models by reducing hidden confounding effects.
Develops a method for causal inference with noisy confounders.
This paper describes a new neuroimaging analysis toolbox that allows for the modeling of nonlinear effects at the voxel level, overcoming limitations of methods based on linear models like the GLM. We illustrate its features using a relevant example in which distinct nonlinear trajectories of Alzheimer's disease relate…
Novel time series forecasting method using sliding window signatures.