'Periodogram values' presentation slideshows

Time series power spectral density . frequency-side, ? , vs. time-side, t

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Time series power spectral density . frequency-side, ? , vs. time-side, t

575 views • 42 slides

Time series power spectral density . frequency-side, ? , vs. time-side, t

427 views • 42 slides

The Empirical FT continued .

The Empirical FT continued. What is the large sample distribution of the EFT?. The complex normal. Theorem. Suppose p.p. N is stationary mixing, then. Proof. Write. Evaluate first and second-order cumulants Bound higher cumulants Normal is determined by its moments. Consider.

367 views • 31 slides

The Empirical FT .

The Empirical FT. What is the large sample distribution of the EFT?. The complex normal. Theorem. Suppose p.p. N is stationary mixing, then. Proof. Write. Evaluate first and second-order cumulants Bound higher cumulants Normal is determined by its moments. Consider.

434 views • 31 slides

Second order stationary p.p. dN(t) = ? exp{ i ? t} dZ N ( ? ) dt Kolmogorov

Second order stationary p.p. dN(t) = ? exp{ i ? t} dZ N ( ? ) dt Kolmogorov N(t) = Power spectrum cov{dZ N ( ? ), dZ N ( ? } = ? ( ? - ? ) f NN d ? d ? remember Z complex-valued Bivariate p.p. process {M,N} Coherency

381 views • 30 slides

The Empirical FT .

The Empirical FT. What is the large sample distribution of the EFT?. The complex normal. Theorem. Suppose X is stationary mixing, then. Proof. Write. Evaluate first and second-order cumulants Bound higher cumulants Normal is determined by its moments. Consider. Comments .

354 views • 29 slides

The Empirical FT .

The Empirical FT. What is the large sample distribution of the EFT?. The complex normal. Theorem. Suppose X is stationary mixing, then. Proof. Write. Evaluate first and second-order cumulants Bound higher cumulants Normal is determined by its moments. Consider. Comments .

306 views • 23 slides