Another limitation is the moderate sample size and sparse-data problems in substrata, a problem often occurring in intersectionality-informed analyses Furthermore, while the complete-case and missing indicator methods used in the main analysis have the advantage of being easy to implement, they pose ...
For implementation, we keep a record of history training loss and start to change the sampling distribution when the loss becomes stable (e.g., after 40K iterations shown in Fig.2). At the beginning of training, we only sample the firstBRoIs with high\(L_{cls}\)(i.e., sample from\...
we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals
Data_size is the scale of training or the size of the training sample. In the procedure of the designed experiment, training samples were selected as points, whereas CNN only accepts raster data (small images). Thus, a mutable window centred on sampled training points generated the required da...
[27], with a type-I-error rate of 0.05, and a type-II-error rate of 0.20, the necessary sample size was 85 participants [28]. The inclusion criterion for the study was that the mean age of the participants fell within the World Health Organization’s (WHO) definition of adolescents (...
Regardless of this statistical constraint, the effects of stability can be clearly seen in the sample profiles presented in Figure 7. These sample profiles relate to the oversized data points in Figure 6. The time and date at which each event was measured are provided in Table 2. FFiigguu...
(i) For large sample sizes, we propose two asymptotic statistics, including likelihood ratio and Wald-type tests, to extend the study of homogeneity test in Honda and Ohyama [19] under large sample sizes. Our results show that the likelihood ratio test is more robust than other tests ...
Table 1.Example data where each row describes an observation from a stratified random sample where stratum 1 has two observations, stratum 2 has eight observations, and stratum 3 has four observations. The TOC uses each rank to make a binary diagnosis for each observation. The TOC then compares...