arXiv research

The paper introduces new estimators for multivariate functions using Fourier methods.

New method reduces density estimation variance for multivariate data.

Density destructors simplify complex PDFs to maximize entropy, linking to information theory.

Bayesian DDR models complex multivariate distributions.

Bayesian approach for multivariate density regression of complex data.

Paper proposes copula-based models for analyzing multivariate zero-inflated continuous data.

The paper introduces a new method for multivariate density estimation using deep neural mixture models.

Non-parametric estimation of a multivariate density estimation is tackled via a method which combines traditional local smoothing with a form of global smoothing but without imposing a rigid structure. Simulation work delivers encouraging indications on the effectiveness of the method. An application to density-based c…

A genetic algorithm improves multivariate kernel density estimation.

Combines MCTM and NF for flexible multivariate density regression with interpretable marginals.

The paper proposes a method to learn evolving multivariate distributions from sample paths.

A new algorithm for sampling from complex distributions.

EagleEye detects localized density anomalies in multivariate data.

The paper proposes a novel tensor-based method for non-parametric density estimation.

Deep belief networks can approximate any multivariate density with binary hidden units.

Skeleton clustering detects clusters in high-dimensional data without needing prototypes.

The paper proposes a new method for density estimation using spline quasi-interpolation for clustering.

The paper introduces a new method to find meaningful data subsets in multivariate probability density functions.

Spectral density matrix estimation of multivariate time series is a classical problem in time series and signal processing. In modern neuroscience, spectral density based metrics are commonly used for analyzing functional connectivity among brain regions. In this paper, we develop a non-asymptotic theory for regularize…

We introduce closed-form transition density expansions for multivariate affine jump-diffusion processes. The expansions rely on a general approximation theory which we develop in weighted Hilbert spaces for random variables which possess all polynomial moments. We establish parametric conditions which guarantee existen…

Important information concerning a multivariate data set, such as clusters and modal regions, is contained in the derivatives of the probability density function. Despite this importance, nonparametric estimation of higher order derivatives of the density functions have received only relatively scant attention. Kernel …

We introduce a multivariate diffusion model that is able to price derivative securities featuring multiple underlying assets. Each asset volatility smile is modeled according to a density-mixture dynamical model while the same property holds for the multivariate process of all assets, whose density is a mixture of mult…

This paper proposes a geometric estimator of dependency between a pair of multivariate samples. The proposed estimator of dependency is based on a randomly permuted geometric graph (the minimal spanning tree) over the two multivariate samples. This estimator converges to a quantity that we call the geometric mutual inf…

The article derives a novel Gram-Charlier A (GCA) Series based Extended Rule-of-Thumb (ExROT) for bandwidth selection in Kernel Density Estimation (KDE). There are existing various bandwidth selection rules achieving minimization of the Asymptotic Mean Integrated Square Error (AMISE) between the estimated probability d…

We leverage neural networks as universal approximators of monotonic functions to build a parameterization of conditional cumulative distribution functions (CDFs). By the application of automatic differentiation with respect to response variables and then to parameters of this CDF representation, we are able to build bl…

New matching estimators correct bias in multivariate settings without smoothing parameters.

New scoring rules for multivariate distributions and level sets.

SLS optimizes minimum-volume regions for conditional quantiles, bypassing density estimation.

We introduce a multivariate stochastic volatility model for asset returns that imposes no restrictions to the structure of the volatility matrix and treats all its elements as functions of latent stochastic processes. When the number of assets is prohibitively large, we propose a factor multivariate stochastic volatili…

DDN models flexible free-form conditional distributions.

Bayesian model selection improves multivariate causal discovery without restrictive assumptions.

A clustering method for multivariate populations with similar dependence structures.

Calibrating a Lévy process usually requires characterizing its jump distribution. Traditionally this problem can be solved with nonparametric estimation using the empirical characteristic functions (ECF), assuming certain regularity, and results to date are mostly in 1D. For multivariate Lévy processes and less smooth …

Unified method for calculating financial option prices from characteristic functions.

We introduce a novel conditional density estimation model termed the conditional density operator (CDO). It naturally captures multivariate, multimodal output densities and shows performance that is competitive with recent neural conditional density models and Gaussian processes. The proposed model is based on a novel …

The Morse-Smale complex of a function $f$ decomposes the sample space into cells where $f$ is increasing or decreasing. When applied to nonparametric density estimation and regression, it provides a way to represent, visualize, and compare multivariate functions. In this paper, we present some statistical results on es…

Develops a method for reverse stress testing in multivariate scenarios.

This work tackles multivariate CDFs and copulas using tensor factorization.

This paper studies neural network operators and their convergence properties.

Proposes a new method for kernel density estimation using stagewise minimization and a simple dictionary.

We propose a projection pursuit (PP) algorithm based on Gaussian mixture models (GMMs). The negentropy obtained from a multivariate density estimated by GMMs is adopted as the PP index to be maximised. For a fixed dimension of the projection subspace, the GMM-based density estimation is projected onto that subspace, wh…

This paper describes a recursive estimation procedure for multivariate binary densities (probability distributions of vectors of Bernoulli random variables) using orthogonal expansions. For $d$ covariates, there are $2^d$ basis coefficients to estimate, which renders conventional approaches computationally prohibitive …

We introduce a multivariate Hawkes process with constraints on its conditional density. It is a multivariate point process with conditional intensity similar to that of a multivariate Hawkes process but certain events are forbidden with respect to boundary conditions on a multidimensional constraint variable, whose evo…

We consider a multivariate default system where random environmental information is available. We study the dynamics of the system in a general setting and adopt the point of view of change of probability measures. We also make a link with the density approach in the credit risk modelling. In the particular case where …

Robust clustering methods for multivariate time series data.

New method efficiently interpolates nonparametric density estimators.

Study analyzes stock market correlations using multivariate distributions.

Paper develops deep learning for metocean variable extremes.