Although sample sizes became sparse, further stratification by season or by sex found significant trend tests in all subgroups of Asians except women alone.
In the current study, the Monte Carlo Mapped Power calculation method (2) (a simulation-based power calculation methodology using the likelihood ratio test) was adapted and developed further to enable the determination of statistical power for varying sample sizes of sparse only, dense only and mixed sparse/dense sampling study designs.
Using CS theory, we show that all markers with nonzero coefficients can be identified (selected) using an efficient algorithm, provided that they are sufficiently few in number (sparse) relative to sample size.
The reason for the underestimation of the median FDR is that the median of distribution that consists of the estimated number of false positives for the large cut-off value in each permutation becomes very sparse when the sample size or the proportion of differentially expressed genes is small.
Experimental results on breast cancer datasets indicate that JSDA outperforms some well-known subspace learning algorithms in prediction accuracy, not matter they are non-sparse or sparse, particularly in the cases of small sample sizes.
However, the function of these SNPs (or the causal variants these SNPs serve as proxies for) remains largely unknown and data describing their correlation with clinical factors or their interplay with other genetic and non-genetic factors are sparse, mainly due to the large sample sizes needed for sufficient statistical power.
Combined pathological and microbiological investigations in free-ranging bats are sparse and often limited by small sample sizes.
Using four diverse sample networks, we demonstrate that this approach performs consistently better at low sample sizes than the Sparse Candidate Algorithm, a representative approach for comparison because it is known to generate Bayesian networks with high positive predictive value.
Combined pathological investigations and pathogen detection in free-ranging bats are sparse [ 6- 9] and often limited by small sample sizes.
In each of these cases, the computation scheme we give is valid at all sample sizes and with all data structures, whether sparse, skewed or otherwise.
