Geodesic
The Berry--Esseen inequality for the distribution of the least square estimate
Matematičeskie zametki, Tome 20 (1976) no. 2, pp. 293-303

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A nonlinear regression model $x_t=g_t(\theta_0)+\varepsilon_t$, $t\geqslant1$, is considered. Under a number of conditions on its elements $\varepsilon_t$ and $g_t(\theta_0)$ it is proved that the distribution of the normalized least square estimate of the parameter $\theta_0$ converges uniformly on the real axis to the standard normal law at least as quickly as a quantity of the order $T^{-1/2}$ as $T\to\infty$, where $T$ is the size of the sample, by which the estimate is formed. 
@article{MZM_1976_20_2_a14,
     author = {A. V. Ivanov},
     title = {The {Berry--Esseen} inequality for the distribution of the least square estimate},
     journal = {Matemati\v{c}eskie zametki},
     pages = {293--303},
     publisher = {mathdoc},
     volume = {20},
     number = {2},
     year = {1976},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/MZM_1976_20_2_a14/}
}
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A. V. Ivanov. The Berry--Esseen inequality for the distribution of the least square estimate. Matematičeskie zametki, Tome 20 (1976) no. 2, pp. 293-303. http://geodesic.mathdoc.fr/item/MZM_1976_20_2_a14/