For a cross-over study design, 7 (dense sampling only), 9 (5 assigned to dense sampling/4 assigned to sparse sampling), 10 (3 assigned to dense sampling, 7 assigned to sparse sampling) or 13 (sparse sampling only) patients were required to detect a binary covariate (30% proportional increase in elimination clearance) with at least 80% statistical power (Fig. 2a, c).
Sparse sampling points are sufficient.
which is much greater than that with sparse sampling.
With these two sparse sampling algorithms, the data rate is proved to be much less than that without sparse sampling.
The rate R(D) at a given distortion for both sparse sampling algorithms is less than that without sparse sampling.
It is showed that with these two sparse sampling algorithms, the data rate is proved to be much less than that without sparse sampling at a given distortion.
We will show that with these two sparse sampling algorithms, the data rate is much less than that without sparse sampling for a given distortion.
The recent development of sparse sampling methods also appears to be extremely promising [15].
Differently, nested sparse sampling is an non-uniform sampling, using two different samplers in each period.
The time domain autocorrelation could also be obtained from sparse sampling with nested sampling structure [11].
In [8], a two user case of sparse sampling, coprime sampling is also introduced.
