Results 11 to 20 of about 51 (51)

SFB 823 Nonparametric inference on Lévy measures and copulas Discussion Paper Nonparametric inference on Lévy measures and copulas

, 2020
In this paper nonparametric methods to assess the multivariate Lévy measure are introduced. Starting from high-frequency observations of a Lévy process X, we construct estimators for its tail integrals and the Pareto Lévy copula and prove weak ...
62m09, Axel Bücher, Mathias Vetter
core  

Estimating the parameters of a dependent model and applying it to environmental data set. [PDF]

J Appl Stat, 2023
Mohtashami-Borzadaran V, Amini M, Ahmadi J.   +2 more
europepmc   +1 more source

Quantile association regression on bivariate survival data. [PDF]

Can J Stat, 2021
Chen LW, Cheng Y, Ding Y, Li R.
europepmc   +1 more source

ASYMPTOTIC DISTRIBUTIONS OF HIGH-DIMENSIONAL DISTANCE CORRELATION INFERENCE. [PDF]

Ann Stat, 2021
Gao L, Fan Y, Lv J, Shao QM.
europepmc   +1 more source

Oscillating neural circuits: Phase, amplitude, and the complex normal distribution. [PDF]

Can J Stat, 2023
Urban KN, Bong H, Orellana J, Kass RE.
europepmc   +1 more source

Asymptotic expansions of test statistics for dimensionality and additional information in canonical correlation analysis when the dimension is large

This paper examines asymptotic expansions of test statistics for dimensionality and additional information in canonical correlation analysis based on a sample of size N=n+1 on two sets of variables, i.e., and .
Sakurai, Tetsuro
core  

Successive direction extraction for estimating the central subspace in a multiple-index regression

In this paper we propose a dimension reduction method for estimating the directions in a multiple-index regression based on information extraction. This extends the recent work of Yin and Cook [X. Yin, R.D.
Yin, Xiangrong, Cook, R. Dennis, Li, Bing   +2 more
core  

Eigenanalysis on a bivariate covariance kernel

Certain constructions of copulas can be interpreted as an eigendecomposition of a kernel. We study some properties of the eigenfunctions and their integrals of a covariance kernel related to a bivariate distribution.
Cuadras, Carles M., Cuadras, Daniel
core  

On Stein's lemma, dependent covariates and functional monotonicity in multi-dimensional modeling

Tracking the correct directions of monotonicity in multi-dimensional modeling plays an important role in interpreting functional associations. In the presence of multiple predictors, we provide empirical evidence that the observed monotone directions via
Li, Jialiang, Zhang, Chunming, Meng, Jingci   +2 more
core  

Bivariate copulas, norms and non-exchangeability

Dependence Modeling, 2015
Papini Pier Luigi
doaj   +1 more source