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mixed sampling - ترجمة إلى الروسية
REGRESSION METHOD IN ECONOMETRICS
Mixed data sampling
mixed sampling
смешанный [комбинированный] выбор
mixed breed
![[https://germanshepherdsmix.com/ German Shepherd Mix] - Possibly Mixed with Husky.](https://commons.wikimedia.org/wiki/Special:FilePath/German Shepherd Mix.jpg?width=200 "[https://germanshepherdsmix.com/ German Shepherd Mix] - Possibly Mixed with Husky.")
LACK OF SPECIFIC ANIMAL BREED
Mixed-breed
общая лексика
смешанная порода
agitated depression
STATE WITH FEATURES UNIQUE TO BOTH DEPRESSION AND MANIA
Dysphoric mania; Mixed episode; Agitated depression; Mixed state (psychology); Depressive mixed state; Template:Mixed states; Mixed Episodes; Template:Mixed Episodes; Mixed episodes; Mixed Episode; Mixed affective episode; Mixed mania; Mixed state (psychiatry); With mixed features
медицина
ажитированная депрессия
تعريف
Nyquist Theorem
<communications> A theorem stating that when an analogue
waveform is digitised, only the frequencies in the waveform
below half the sampling frequency will be recorded. In
order to reconstruct (interpolate) a signal from a sequence of
samples, sufficient samples must be recorded to capture the
peaks and troughs of the original waveform. If a waveform is
sampled at less than twice its frequency the reconstructed
waveform will effectively contribute only noise. This
phenomenon is called "aliasing" (the high frequencies are
"under an alias").
This is why the best digital audio is sampled at 44,000 Hz -
twice the average upper limit of human hearing.
The Nyquist Theorem is not specific to digitised signals
(represented by discrete amplitude levels) but applies to any
sampled signal (represented by discrete time values), not just
sound.
{Nyquist
(http://geocities.com/bioelectrochemistry/nyquist.htm) }
(the man, somewhat inaccurate).
(2003-10-21)
ويكيبيديا
Mixed-data sampling
Econometric models involving data sampled at different frequencies are of general interest. Mixed-data sampling (MIDAS) is an econometric regression developed by Eric Ghysels with several co-authors. There is now a substantial literature on MIDAS regressions and their applications, including Ghysels, Santa-Clara and Valkanov (2006), Ghysels, Sinko and Valkanov, Andreou, Ghysels and Kourtellos (2010) and Andreou, Ghysels and Kourtellos (2013).
