We provide uniform rates of convergence in the central limit theorem for linear negative quadrant dependent (LNQD) random
variables. Let \documentclass{aastex}
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$\{X_{n},\allowbreak
n\ge1\}$
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$EX_{n}=0$
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\begin{document}
$S_{n}=\sum_{j=1}^{n}X_{j}$
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$B_{n}^{2}=\text{Var}\, (S_{n})$
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We show that \begin{gather*} \sup_{x} \left|P\left(\frac{S_{n}}{B_{n}}<x\right)-\Phi(x)\right|= O\bigg(n^{-\delta/(2+3\delta)}\vee
\frac{n^{3\delta^{2}/(4+6\delta)}}{B^{2+\delta}_{n}} \sum_{i=1}^{n} E{|X_{i}|}^{2+\delta}\bigg) \end{gather*} under finite
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$(2+\delta)$
\end{document}th moment and a power decay rate of covariances. Moreover, by the truncation method, we obtain a Berry--Esseen
type estimate for negatively associated (NA) random variables with only finite second moment. As applications, we obtain another
convergence rate result in the central limit theorem and precise asymptotics in the law of the iterated logarithm for NA sequences,
and also for LNQD sequences.