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Mixing convergence of LSE for supercritical AR(2) processes with Gaussian innovations using random scaling
We prove mixing convergence of the least squares estimator of autoregressive parameters for supercritical autoregressive processes of order 2 with...
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Rate of Convergence
The fundamental requirement in data analysis is the consistent estimation of a parameter. As the sample size increases, the precision of the...
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New copula families and mixing properties
We characterize absolutely continuous symmetric copulas with square integrable densities in this paper. This characterization is used to create new...
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Mixing Times of a Gibbs Sampler for Probit Hierarchical Models
Hierarchical models are a popular way to borrow information across groups of datapoints, which share similar, but not identical, features. When the...
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Concentration inequalities for Kernel density estimators under uniform mixing
We derive non-asymptotic concentration inequalities for the uniform deviation between a multivariate density function and its non-parametric kernel...
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Estimating a Mixing Distribution on the Sphere Using Predictive Recursion
Mixture models are commonly used when data show signs of heterogeneity and, often, it is important to estimate the distribution of the latent...
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On Berry–Esséen bound of frequency polygon estimation under \(\rho \)-mixing samples
The frequency polygon estimation, which is based on histogram technique, has similar convergence rate as those of non-negative kernel estimators and...
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Strong Convergence for Weighted Sums of Widely Orthant Dependent Random Variables and Applications
In this paper, the complete convergence and the Kolmogorov strong law of large numbers for weighted sums of widely orthant dependent random variables...
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How to Improve MCMC Convergence
When modeling real-world data, MCMC may have poor convergence, which will make the calculation speed of sampling very slow. Poor convergence is...
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Weighted weak convergence of the sequential tail empirical process for heteroscedastic time series with an application to extreme value index estimation
The sequential tail empirical process is analyzed in a stochastic model allowing for serially dependent observations and heteroscedasticity of...
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Perturbations of copulas and mixing properties
This paper explores the impact of perturbations of copulas on the dependence properties of the Markov chains they generate. We consider Markov chains...
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Extending finite mixtures of nonlinear mixed-effects models with covariate-dependent mixing weights
Finite mixtures of nonlinear mixed-effects models have emerged as a prominent tool for modeling and clustering longitudinal data following nonlinear...
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Tail and Quantile Estimation for Real-Valued \(\boldsymbol{\beta}\)-Mixing Spatial Data
AbstractThis paper deals with extreme-value index estimation of a heavy-tailed distribution of a spatial dependent process. We are particularly...
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Directed hybrid random networks mixing preferential attachment with uniform attachment mechanisms
Motivated by the complexity of network data, we propose a directed hybrid random network that mixes preferential attachment (PA) rules with uniform...
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Convergence Details About k-DPP Monte-Carlo Sampling for Large Graphs
This paper aims at making explicit the mixing time found by Anari et al. (
2016 ) for k -DPP Monte-Carlo sampling when it is applied on large graphs....
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Nonparametric regression with warped wavelets and strong mixing processes
We consider the situation of a univariate nonparametric regression where either the Gaussian error or the predictor follows a stationary strong...
