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We consider a stationary linear AR($p$) model with observations subject togross errors (outliers). The autoregression parameters as well as thedistribution function (d.f.) $G$ of innovations are unknown. The distributionof outliers $\Pi$ is unknown and arbitrary, their intensity is $\gamman^{-1/2}$ with an unknown $\gamma$, $n$ is the sample size. We test thehypothesis for normality of innovations $$\mathbf{H}_\Phi \colon G \in\{\Phi(x/\theta),\,\theta>0\},$$ $\Phi(x)$ is the d.f. $\mathbf{N}(0,1)$. Ourtest is the special symmetrized Pearson's type test. We find the power of thistest under local alternatives $$\mathbf{H}_{1n}(\rho)\colonG(x)=A_n(x):=(1-\rho n^{-1/2})\Phi(x/\theta_0)+\rho n^{-1/2}H(x), $$ $\rho\geq0,\,\theta_0$ is the unknown (under $\mathbf{H}_\Phi$) variance of innovations.First of all we estimate the autoregression parameters and then using theresiduals from the estimated autoregression we construct a kind of empiricaldistribution function (r.e.d.f.), which is a counterpart of the (inaccessible)e.d.f. of the autoregression innovations. After this we construct thesymmetrized variant r.e.d.f. Our test statistic is the functional fromsymmetrized r.e.d.f. We obtain a stochastic expansion of this symmetrizedr.e.d.f. under $\mathbf{H}_{1n}(\rho)$ , which enables us to investigate ourtest. We establish qualitative robustness of this test in terms of uniformequicontinuity of the limiting power (as functions of $\gamma,\rho$ and $\Pi$)with respect to $\gamma$ in a neighborhood of $\gamma=0$.

On Symmetrized Pearson's Type Test for Normality of Autoregression: Power under Local Alternatives