Results 21 to 30 of about 278 (169)
Nonparametric density estimators based on nonstationary absolutely regular random sequences
International Journal of Stochastic Analysis, Volume 9, Issue 3, Page 233-254, 1996., 1995 In this paper, the central limit theorems for the density estimator and for the integrated square error are proved for the case when the underlying sequence of random variables is nonstationary. Applications to Markov processes and ARMA processes are provided.
Michel Harel, Madan L. Puri
wiley +1 more source
A new simulation estimator of system reliability
International Journal of Stochastic Analysis, Volume 7, Issue 3, Page 331-336, 1994., 1993 A basic identity is proven and applied to obtain new simulation estimators concerning (a) system reliability, (b) a multi‐valued system. We show that the variance of this new estimator is often of the order α2 when the usual raw estimator has variance of the order α and α is small.
Sheldon M. Ross
wiley +1 more source
Local linear approach: Conditional density estimate for functional and censored data
Demonstratio Mathematica, 2022 Let YY be a random real response, which is subject to right censoring by another random variable CC. In this paper, we study the nonparametric local linear estimation of the conditional density of a scalar response variable and when the covariable takes ...
Benkhaled Abdelkader, Madani Fethi
doaj +1 more source
Strong consistency of regression function estimator with martingale difference errors
Open Mathematics, 2021 In this paper, we consider the regression model with fixed design: Yi=g(xi)+εi{Y}_{i}=g\left({x}_{i})+{\varepsilon }_{i}, 1≤i≤n1\le i\le n, where {xi}\left\{{x}_{i}\right\} are the nonrandom design points, and {εi}\left\{{\varepsilon }_{i}\right\} is a ...
Chen Yingxia
doaj +1 more source
On the asymptotic covariance of the multivariate empirical copula process
Dependence Modeling, 2019 Genest and Segers (2010) gave conditions under which the empirical copula process associated with a random sample from a bivariate continuous distribution has a smaller asymptotic covariance than the standard empirical process based on a random sample ...
Genest Christian +2 more
doaj +1 more source
Return level bounds for discrete and continuous random variables
, 2009 Discrete extremes, Empirical processes, Generalized Pareto distribution, Return level, 62G05, 62G20, 62G30,
P. Naveau +7 more
core +1 more source
Dependence Modeling, 2019 We study nonparametric estimators of conditional Kendall’s tau, a measure of concordance between two random variables given some covariates. We prove non-asymptotic pointwise and uniform bounds, that hold with high probabilities.
Derumigny Alexis, Fermanian Jean-David
doaj +1 more source
Pharmacokinetics and Pharmacodynamics Models of Tumor Growth and Anticancer Effects in Discrete Time
Computational and Mathematical Biophysics, 2020 We study the h-discrete and h-discrete fractional representation of a pharmacokinetics-pharmacodynamics (PK-PD) model describing tumor growth and anticancer effects in continuous time considering a time scale h0, where h > 0.
Atıcı Ferhan M. +4 more
doaj +1 more source
Conditional Density Kernel Estimation Under Random Censorship for Functional Weak Dependence Data
Journal of Mathematics, Volume 2025, Issue 1, 2025.The primary objective of this research is to investigate the asymptotic properties of the conditional density nonparametric estimator. The main areas of focus are the estimator’s consistency (with rates), including those involving censored data and quasi‐associated dependent variables, as well as its performance when the covariate is functional in ...
Hamza Daoudi +4 more
wiley +1 more source
An optimal transport approach to estimating causal effects via nonlinear difference-in-differences
Journal of Causal InferenceWe propose a nonlinear difference-in-differences (DiD) method to estimate multivariate counterfactual distributions in classical treatment and control study designs with observational data.
Torous William +2 more
doaj +1 more source