论文标题
局部适合自动化正态性测试的力量
Local Power of Tests of Fit for Normality of Autoregression
论文作者
论文摘要
我们考虑固定的$ ar（p）$型号。自动进展参数尚不清楚，也是创新的分布。根据参数估计值的残差，定义了经验分布函数的类似物，并构建了Kolmogorov的测试和$ω^2 $类型，用于测试创新正常的假设。我们在局部替代方案下获得了这些测试的渐近力。
We consider a stationary $AR(p)$ model. The autoregression parameters are unknown as well as the distribution of innovations. Based on the residuals from the parameter estimates, an analog of empirical distribution function is defined and the tests of Kolmogorov's and $ω^2$ type is constructed for testing hypotheses on the normality of innovations. We obtain the asymptotic power of these tests under local alternatives.
