Results 11 to 20 of about 1,618 (122)

Quadratic functional estimation in inverse problems [PDF]

open access: yes, 2009
We consider in this paper a Gaussian sequence model of observations $Y_i$, $i\geq 1$ having mean (or signal) $\theta_i$ and variance $\sigma_i$ which is growing polynomially like $i^\gamma$, $\gamma >0$.
Butucea, Cristina, Méziani, Katia
core   +5 more sources

Empirical likelihood for quantile regression models with response data missing at random

open access: yesOpen Mathematics, 2017
This paper studies quantile linear regression models with response data missing at random. A quantile empirical-likelihood-based method is proposed firstly to study a quantile linear regression model with response data missing at random.
Luo S., Pang Shuxia
doaj   +1 more source

Asymptotic normality of the relative error regression function estimator for censored and time series data

open access: yesDependence Modeling, 2021
Consider a survival time study, where a sequence of possibly censored failure times is observed with d-dimensional covariate The main goal of this article is to establish the asymptotic normality of the kernel estimator of the relative error regression ...
Bouhadjera Feriel, Saïd Elias Ould
doaj   +1 more source

Nonparametric density estimators based on nonstationary absolutely regular random sequences

open access: yesInternational 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

All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples

open access: yesJournal of Causal Inference, 2023
There is a long-standing debate in the statistical, epidemiological, and econometric fields as to whether nonparametric estimation that uses machine learning in model fitting confers any meaningful advantage over simpler, parametric approaches in finite ...
Rudolph Kara E.   +4 more
doaj   +1 more source

A new simulation estimator of system reliability

open access: yesInternational 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

A note on efficient minimum cost adjustment sets in causal graphical models

open access: yesJournal of Causal Inference, 2022
We study the selection of adjustment sets for estimating the interventional mean under an individualized treatment rule. We assume a non-parametric causal graphical model with, possibly, hidden variables and at least one adjustment set composed of ...
Smucler Ezequiel, Rotnitzky Andrea
doaj   +1 more source

Estimation of Gini Index within Pre-Specied Error Bound [PDF]

open access: yes, 2015
Gini index is a widely used measure of economic inequality. This article develops a general theory for constructing a confidence interval for Gini index with a specified confidence coefficient and a specified width.
Chattopadhyay, Bhargab   +1 more
core   +3 more sources

2D score-based estimation of heterogeneous treatment effects

open access: yesJournal of Causal Inference, 2023
Statisticians show growing interest in estimating and analyzing heterogeneity in causal effects in observational studies. However, there usually exists a trade-off between accuracy and interpretability for developing a desirable estimator for treatment ...
Ye Steven Siwei   +2 more
doaj   +1 more source

Geometric View of Measurement Errors [PDF]

open access: yes, 2010
The slope of the best fit line from minimizing the sum of the squared oblique errors is the root of a polynomial of degree four. This geometric view of measurement errors is used to give insight into the performance of various slope estimators for the ...
Copas J.   +10 more
core   +2 more sources

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