Convergence rates in the weak law of large numbers for weighted sums of i.i.d. random variables and applications in errors-in-variables models

DOI10.1142/S0219493719500412zbMath1436.62071OpenAlexW2920255001WikidataQ128293218 ScholiaQ128293218MaRDI QIDQ5213053

Mingyang Zhang, Soo Hak Sung, Ping Yan Chen

Publication date: 31 January 2020

Published in: Stochastics and Dynamics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0219493719500412

zbMATH Keywords

convergence rateconsistent estimatorweak law of large numbersweighted sumerrors-in-variables model

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05) Central limit and other weak theorems (60F05)

Related Items

Weak convergence for weighted sums of negatively associated random variables and its application in nonparametric regression models, Weak convergence for weighted sums of a class of random variables with related statistical applications, Asymptotic properties for the estimators in heteroscedastic semiparametric EV models with α-mixing errors, Strong consistency of LS estimators in simple linear EV regression models with WOD errors

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• A Note on the Weak Law