设总体 $X \sim N\left(0, \sigma^2\right), X_1, X_2, \cdots, X_n$ 为 $X$ 的样本，则 $\sigma^2$ 的无偏估计为
A
$\quad \hat{\sigma}^2=\frac{1}{n-1} \sum_{i=1}^n X_i{ }^2$
B
$\hat{\sigma}^2=\frac{1}{n} \sum_{i=1}^n X_i^2$
C
$\hat{\sigma}^2=\frac{1}{n+1} \sum_{i=1}^n X_i{ }^2$
D
$\hat{\sigma}^2=\frac{1}{(n+1)^2} \sum_{i=1}^n X_i^2$
E
F