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Let $X_t$ be a centered real stationary Gaussian time series with absolutely summable autocovariances and two-sided angular-frequency spectral density $f$. The parent periodogram uses $I_T(\omega )=(2\pi T{)}^{-1}|{\sum }_{t=1}^T{X}_t{e}^{-it\omega }{|}^2$. At an interior positive frequency define the one-sided quantitiesThus the doubling applies to both the statistic and its target spectral density.

For Fourier-grid frequencies ${\omega }_T=2\pi k_T/T\to \omega \in (0,\pi )$ with $f(\omega )>0$, setThen $I_T^{+}({\omega }_T)=A_T^2+B_T^2$. Summability makes their variances tend to $f(\omega )=s(\omega )/2$ and their covariance tend to zero. Joint Gaussianity therefore givesUnder these Gaussian assumptions the fourth moments also converge, so $\mathrm{var}(I_T^{+}({\omega }_T))\to s(\omega {)}^2$. An unsmoothed periodogram is therefore not a consistent estimator of a positive spectral density. At zero and the Nyquist frequency $\pi$, the sine coordinate vanishes and the real-only calibration differs; the interior doubling and two-coordinate argument do not apply there.^[ref]

Peter Bartlett studies the foundations of machine learning, statistical learning theory and reinforcement learning. His Berkeley research description emphasizes the joint demands of computational efficiency and statistical performance, including prediction with complex outputs and sequential decisions.^[ref]

His Introduction to Time Series Analysis, Lecture 20 by Peter Bartlett gives a concrete lesson about estimation. The raw periodogram can have an approximately correct expected value while its variance remains substantial as the sample grows. For Gaussian white noise, an interior-frequency periodogram ordinate is distributed as a scaled chi-squared distribution with two degrees of freedom. Spectral smoothing is therefore doing statistical work, rather than merely making a graph look smoother.^[ref]