Index of papers in April 2015 that mention
  • sample size
Nancy K. Drew, Mackenzie A. Eagleson, Danny B. Baldo Jr., Kevin Kit Parker, Anna Grosberg
OOPPCOOPC.
Estimated maximum tolerable error and minimum sample size .
OOPPCOOPC.
Therefore, for the parameter to be useful, the maximum allowable error and minimum sample size have to be experimentally realistic.
OOPPCOOPC.
To estimate the error and sample size , we calculated the propagation of error in COOPu and COOPC, and used them in the student t-test to calculate statistical significance.
sample size is mentioned in 12 sentences in this paper.
Topics mentioned in this paper:
Noa Slater, Yoram Louzoun, Loren Gragert, Martin Maiers, Ansu Chatterjee, Mark Albrecht
Haplotype Distribution Formalism
It is thus of interest to estimate the total size of H, and the relation between the sampling size and the portion of H that we actually observe.
Haplotype Distribution Formalism
Formally, the expected number of unique haplotypes (U(R)) in a sample size (R) can be estimated, assuming a truncated power law formalism as defined above, to be (see Sl—S3 Texts):
Haplotype Numbers in US Sub-populations
Further, estimates of the power law eXponent converged before the full sample size was analyzed (Table 3 and 83—88 Figs).
Haplotype Numbers in US Sub-populations
Comparison of computed and observed expected number of haplotypes in a sample of size R : U(R)forthe European American population for different sample sizes R. The gray squares are observations.
Haplotype Numbers in US Sub-populations
An important aspect of Eq 7 is that it does not saturate until the sample size is close to the full population size.
Introduction
U(R) Expected number of unique haplotypes in a sample size of R.
Introduction
R Sample size .
Methodology Validation
From the population we extract two measures—the haplotype frequency distribution (B) and the number of unique haplotypes as a function of the sample size (C).
Methodology Validation
We then fit the obsen/ed unique haplotype cun/e (C) with an log(observed (R’)) for different values of sample sizes R'.
Methodology Validation
We then validated that the resulting values fit the observed distribution all the way to the full sample size (7.8e6) and extrapolated it to the total European American population as defined by the Census [22].
sample size is mentioned in 24 sentences in this paper.
Topics mentioned in this paper:
Christiaan A. de Leeuw, Joris M. Mooij, Tom Heskes, Danielle Posthuma
Gene-set analysis
in meta-analysis) difference in underlying sample size , if such effects are present.
Introduction
However, despite growing sample sizes , the genetic variants discovered by GWAS generally account for only a fraction of the total heritability of a phenotype [2,3].
Introduction
More than anything, GWAS has shown that many phenotypes, such as height [4], schizophrenia [5] and BMI [6] are highly polygenic and influenced by thousands of genetic variants with small individual effects, requiring very large sample sizes to detect them.
Supporting Information
Gene analysis was performed on the CD data, and a joint empirical distribution gene SSM values was generated using 4,611 permutations of the phenotype (since the sample size of the CD data is 4,611).
Supporting Information
Gene analysis was performed on the CD data, and a joint empirical distribution of the gene SSM values was generated using 4,611 permutations of the phenotype (since the sample size of the CD data is 4,611).
sample size is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
William F. Flynn, Max W. Chang, Zhiqiang Tan, Glenn Oliveira, Jinyun Yuan, Jason F. Okulicz, Bruce E. Torbett, Ronald M. Levy
Correlation analysis in using bound estimates protease captures known pair correlations
These differences are not likely due to sample size effects in the relatively small number of patient samples in this study because the univariate marginals and the bivariate marginal estimates are calculated with high precision in each sample due to the extremely high coverage afforded by deep sequencing and the very narrow bounds imposed on the bivariate probabilities by the univariate probabilities.
Pairwise covariation
But, as the total sample size tends to infinity, ranking based on LR is asymptotically equivalent to ranking based on Fisher’s exact test of independence [64].
Pairwise covariation
Therefore, the proposed procedure for deep sequencing data differs from previous analyses of MSA data [3] mainly in the necessary step of constructing lower and upper probability tables and, to a lesser extent, in the use of mutual information for ranking correlations in probability tables without depending on the total sample size .
sample size is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: