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.
Identifying causal direction in location-scale noise models with hidden variables
Differentially private log-location-scale regression models improve privacy in statistical analysis.
SkewD robustly discovers causal relationships in skewed noise models.
Study identifies and estimates causal LSNM models, proving feature maps are consistent.
Alternative to likelihood-based LSNM model selection, residual independence testing is more robust to noise misspecification.
Study calculates tail risk for various mixture distributions.
Study shows one-dimensional location-scale-shape models are flat in Wasserstein geometry.
Paper proposes MWDE for estimating finite location-scale mixtures.
This paper argues that a class of Riemannian metrics, called warped metrics, plays a fundamental role in statistical problems involving location-scale models. The paper reports three new results : i) the Rao-Fisher metric of any location-scale model is a warped metric, provided that this model satisfies a natural invar…
Exact 1-Wasserstein distance between location-scale distributions derived, with privacy effects studied.
Paper proposes a k-NN classifier for detecting spike-and-wave seizures in EEG.
Paper analyzes VI for location-scale families, proving robustness guarantees for mean and correlation recovery.
NAMLSS models provide interpretable neural regression for location, scale, and shape.
Paper establishes universal lower bounds and optimal rates for clustering sub-exponential mixture models.
Study on conditions for achieving optimal robustness in statistical estimators.
Study uniform rates for estimating Gaussian mixtures without separation assumption.
The goal of regression analysis is to predict the value of a numeric outcome variable y given a vector of joint values of other (predictor) variables x. Usually a particular x-vector does not specify a repeatable value for y, but rather a probability distribution of possible y--values, p(y|x). This distribution has a l…
ELU algorithm improves on EM for over-specified Gaussian mixtures.
Guarantees convergence for black-box variational inference without modifications.
New method distinguishes cause from effect using causal velocity.
We introduce a wavelet-domain functional analysis of variance (fANOVA) method based on a Bayesian hierarchical model. The factor effects are modeled through a spike-and-slab mixture at each location-scale combination along with a normal-inverse-Gamma (NIG) conjugate setup for the coefficients and errors. A graphical mo…
BBVI converges nearly dimensionally independent for log-concave targets.
Recent variational inference methods use stochastic gradient estimators whose variance is not well understood. Theoretical guarantees for these estimators are important to understand when these methods will or will not work. This paper gives bounds for the common "reparameterization" estimators when the target is smoot…
Over the last decades, the challenges in applied regression and in predictive modeling have been changing considerably: (1) More flexible model specifications are needed as big(ger) data become available, facilitated by more powerful computing infrastructure. (2) Full probabilistic modeling rather than predicting just …
This paper studies identifiability and convergence behaviors for parameters of multiple types in finite mixtures, and the effects of model fitting with extra mixing components. First, we present a general theory for strong identifiability, which extends from the previous work of Nguyen [2013] and Chen [1995] to address…
Proposes a federated learning approach for industrial asset failure prediction.
Concrete distribution properties examined on simplex.
Black-box variational inference tries to approximate a complex target distribution though a gradient-based optimization of the parameters of a simpler distribution. Provable convergence guarantees require structural properties of the objective. This paper shows that for location-scale family approximations, if the targ…
We propose a new framework of CatBoost that predicts the entire conditional distribution of a univariate response variable. In particular, CatBoostLSS models all moments of a parametric distribution (i.e., mean, location, scale and shape [LSS]) instead of the conditional mean only. Choosing from a wide range of continu…
We propose a new framework of XGBoost that predicts the entire conditional distribution of a univariate response variable. In particular, XGBoostLSS models all moments of a parametric distribution (i.e., mean, location, scale and shape [LSS]) instead of the conditional mean only. Choosing from a wide range of continuou…
Wealth tax equivalent to government stake, affecting returns and portfolio choice.
The cost of belief changes with precision and is a hyperbolic geometry.
We empirically analyze the price and liquidity responses to trade signs, traded volumes and signed traded volumes. Utilizing the singular value decomposition, we explore the interconnections of price responses and of liquidity responses across the whole market. The statistical characteristics of their singular vectors …
Paper models and forecasts intra-day electricity price spreads.
We introduce a new Bayesian multi-class support vector machine by formulating a pseudo-likelihood for a multi-class hinge loss in the form of a location-scale mixture of Gaussians. We derive a variational-inference-based training objective for gradient-based learning. Additionally, we employ an inducing point approxima…
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…
Investigates methods to regularize quantile regression for accurate predictions.
This research improves asset life prediction by integrating deep learning with mixture distributions.
We consider universal adversarial patches for faces -- small visual elements whose addition to a face image reliably destroys the performance of face detectors. Unlike previous work that mostly focused on the algorithmic design of adversarial examples in terms of improving the success rate as an attacker, in this work …
Study compares geometric approaches for shape and deformation statistics.
Develops a new model to track financial market interconnectedness over time.
We present a new algorithm for boosting generalized additive models for location, scale and shape (GAMLSS) that allows to incorporate stability selection, an increasingly popular way to obtain stable sets of covariates while controlling the per-family error rate (PFER). The model is fitted repeatedly to subsampled data…
Recent financial disasters emphasised the need to investigate the consequence associated with the tail co-movements among institutions; episodes of contagion are frequently observed and increase the probability of large losses affecting market participants' risk capital. Commonly used risk management tools fail to acco…
We study a class of weakly identifiable location-scale mixture models for which the maximum likelihood estimates based on i.i.d. samples are known to have lower accuracy than the classical error. We investigate whether the Expectation-Maximization (EM) algorithm also converges slowly for these m…
The paper improves guarantees for VI in symmetric cases.
A federated model predicts failures using multi-stream incomplete data.
Paper bridges VAEs and KDEs for more flexible posterior estimation.
We propose a Bayesian non-parametric approach for modeling the distribution of multiple returns. In particular, we use an asymmetric dynamic conditional correlation (ADCC) model to estimate the time-varying correlations of financial returns where the individual volatilities are driven by GJR-GARCH models. The ADCC-GJR-…