Berry-Esseen type bounds of the estimators in a semiparametric model under linear process errors with α-mixing dependent innovations
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Publication:5228858
DOI10.1080/02331888.2019.1624755zbMath1423.62025OpenAlexW2949334113MaRDI QIDQ5228858
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Publication date: 13 August 2019
Published in: Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02331888.2019.1624755
Related Items
The Berry–Esseen-type bound for the G-M estimator in a nonparametric regression model with α-mixing errors, Complete moment convergence of moving-average processes under END assumptions
Cites Work
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- Berry-Esseen type bounds of estimators in a semiparametric model with linear process errors
- Mixing: Properties and examples
- Estimation of the autocorrelation coefficient in the presence of a regression trend
- Berry-Esseen bounds of error variance estimation in partly linear models
- Locally adaptive regression splines
- Local linear estimation in partly linear models
- Jackknifing in partially linear regression models with serially correlated errors
- Semiparametric generalized least squares estimation in partially linear regression models with correlated errors
- Maximal moment inequality for partial sums of strong mixing sequences and application
- On a semiparametric regression model whose errors form a linear process with negatively associated innovations
- Conditions for linear processes to be strong-mixing
- On the Strong Mixing Property for Linear Sequences
- An Adaptive Estimator of the Autocorrelation Coefficient in Regression Models with Autoregressive Errors
- CONVERGENCE RATES OF ESTIMATORS IN PARTIAL LINEAR REGRESSION MODELS WITH MA(∞) ERROR PROCESS
- Analytic Inequalities
