ライフサイエンスコーパス: test statistic

1 ion to estimate the null distribution of the test statistic).

2 on of time and (ii) an associated functional test statistic.

3 re variants from the same gene into a single test statistic.

4 e is proposed to reduce the dimension of the test statistic.

5 d stage, a Z-score or P-value is used as the test statistic.

6 covariance mapping method combined with a t-test statistic.

7 ave much higher power than the standard chi2-test statistic.

8 ut also reduce the degrees of freedom of the test statistic.

9 to approximate the null distribution of the test statistic.

10 test of association and a similar haplotype-test statistic.

11 as accurate for the null distribution of the test statistic.

12 ement in QoL were assessed by Fisher's exact test statistic.

13 ically derived large deviations rate for the test statistic.

14 used here to calculate the likelihood-ratio test statistic.

15 es evaluated for each half as an alternative test statistic.

16 null distribution of the Fisher combination test statistic.

17 iation test statistic to the expected median test statistic.

18 tain an approximate p-value for the observed test statistic.

19 ivated by the above idea, we devised two new test statistics.

20 g Mann-Whitney U, Kruskal-Wallis, and chi(2) test statistics.

21 parisons for independent or weakly dependent test statistics.

22 (CMC) method, and single-marker association test statistics.

23 lepoint approximation (SPA) to calibrate the test statistics.

24 ion) can greatly improve overall accuracy of test statistics.

25 istic which is the maximum of the univariate test statistics.

26 quire independence or weak dependence of the test statistics.

27 or relationship to ICH by using Fisher exact test statistics.

28 ation of population variances which improves test statistics.

29 d this model is used as the basis of several test statistics.

30 ables simultaneous visualization of multiple test statistics.

31 ach is applicable to any data structures and test statistics.

32 stimation procedure based on the linear rank test statistics.

33 e of five test statistics, including two new test statistics.

34 The score statistic comprises two component test statistics.

35 performed by using nonparametric and chi(2) test statistics.

36 the statistical significance of the observed test statistics.

37 t, somewhat correlated, or highly correlated test statistics.

38 was correlated with the lesion site using t-test statistics.

39 e inaccurate asymptotic distributions of the test statistics.

40 rates genomic functional annotation and GWAS test statistics.

41 ifference at the 95% confidence level with t-test statistics.

42 such as a table margin that varies among the test statistics.

43 e shrinkage on the estimated effects and the test statistics.

44 t test) and nonparametric (Wilcoxon rank sum test) statistics.

45 SI in ALS was the lowest among four groups (Test statistic = 40.57, p < 0.001).

46 ociated with lower 25OHD level (n = 2,347, F-test statistic = 49.7, p = 2.4 x 10-12).

47 as strongly associated with 25OHD (n=2347, F-test statistic=49.7, P=2x10(-12)).

48 bled' = 59 [42 to 95]%; Friedman Chi-squared test statistic 6.5, p = 0.04; visit 2 median [IQR] perce

49 bled' = 28 [13 to 63]%; Friedman Chi-squared test statistic 8.4, p= 0.02).

50 Using a multivariate test statistic, a glutathione S-transferase (GST) gene w

51 To improve the power of test statistics, a general statistical framework for con

52 limited, and pooling the permutation-derived test statistics across all genes has been proposed.

53 approximating the joint distribution of the test statistics along the genome.

54 To standardize the test statistic, an empirical variance-covariance estimat

55 estimate that minimizes the variance of the test statistic and (2) maximizing the statistic over a n

56 rametric method based on the direct use of a test statistic and a null statistic.

57 l the three methods depend on constructing a test statistic and a so-called null statistic such that

58 the higher of the two LOD scores as the raw test statistic and corrected for multiple tests.

59 To plot power curves for the test statistic and determine sample sizes for reasonable

60 nalysis should be avoided as it inflates the test statistic and increases the Type I error.

61 Calculation of the new multiallele test statistic and its P-value is very simple and utiliz

62 wer will depend heavily on the choice of the test statistic and on the underlying genetic architectur

63 Profiles of the likelihood-ratio test statistic and the maximum-likelihood estimates (MLE

64 and exponential mechanisms based on the TDT test statistic and the shortest Hamming distance (SHD) s

65 e the relationship between the entropy-based test statistic and the standard chi2 statistic and show

66 ivariate statistics (i.e., components of the test statistic and their covariance matrix), which are d

67 'T' methods that perform well with discrete test statistics and also assesses how well methods devel

68 wer of multivariate tests depend only on the test statistics and are insensitive to the different nor

69 that proposed interferometry experiments to test statistics and computational ability of the state a

70 We investigate the power of the nonlinear test statistics and demonstrate that under certain condi

71 p of people, leading to potentially inflated test statistics and false positives.

72 f each by examining the relationship between test statistics and linkage disequilibrium (LD).

73 It is well established that test statistics and P-values derived from discrete data,

74 distributions of Hardy-Weinberg equilibrium test statistics and P-values.

75 at can be used to integrate, view and browse test statistics and perform genome annotation.

76 y samples may cause substantial inflation of test statistics and possibly spurious associations.

77 the effect of differential hybridization on test statistics and provide a solution to this problem i

78 Therefore, special test statistics and quality control procedures are requi

79 variables by a criterion independent of the test statistic, and then only tests variables which pass

80 Using a t-test statistics approach, we compared gene expression su

81 To evaluate their performance, the nonlinear test statistics are also applied to three real data sets

82 e evolutionary tree stochastically, and then test statistics are calculated to determine whether a co

83 Type I error rates of the proposed test statistics are calculated to show their robustness.

84 ng methods for exact computation of standard test statistics are computationally impractical for even

85 noncentrality parameter approximations of F-test statistics are derived to make power calculation an

86 The possible choices and usages of these test statistics are discussed.

87 erroni and Holm methods, especially when the test statistics are highly correlated.

88 n of differentially expressed genes in which test statistics are learned from data using a simple not

89 The genes identified or the univariate test statistics are often linked to known biological pat

90 On the basis of the two models, F-test statistics are proposed to test association between

91 centrality parameter approximations of the F-test statistics are provided.

92 ls (CIs), and Wilcoxon signed-rank two-sided test statistics are shown for MAE (19.61 [95% CI: 18.83,

93 ipping genetic markers whose upper bounds on test statistics are small.

94 olutions, since theoretical distributions of test statistics are typically unavailable for such desig

95 To check if our model assumptions for the test statistics are valid for various bioinformatics exp

96 We treat each test statistic as a basic attribute, and model the detec

97 reference data, the null distribution of the test statistic as a function of feature length using gen

98 Named SKAT+, this method uses the same test statistic as SKAT but differs in the way the null d

99 nder the null of no genetic association, the test statistic asymptotically follows a chi-square distr

100 dedicated to visualizing population genetic test statistics at the genomic level is needed.

101 ased, can show inflation or deflation of the test statistic attributable to the inclusion of pairs wi

102 alytical derivation, I show that many of the test statistics available in standard linkage analysis p

103 e the drift and the boundary and construct a test statistic based on finite samples.

104 In this article, we propose two new test statistics based on a variance-components approach

105 n, but the evaluation of the significance of test statistics based on asymptotic theory can be imprec

106 In this paper we consider test statistics based on individual genotyping.

107 ll P-values for a broad range of complicated test statistics based on the principle of the cross-entr

108 was assessed using Cochrane Q test and I(2) test statistics based on the random effects model.

109 Popular approaches involve using t-test statistics, based on modelling the data as arising

110 then estimates the null distribution of the test statistic by permuting the observations between the

111 ethod that estimates the distribution of the test statistic by using the saddlepoint approximation.

112 rrent pathway testing methods use univariate test statistics calculated from individual genomic marke

113 he significance of virtually any association test statistic can be evaluated based on simulations or

114 As a consequence, a W-test statistic can be used for testing the significance

115 approach that focuses on the maximum of the test statistics can significantly improve the power to d

116 The testing statistic can be constructed using any genotype-

117 ate the extent of this bias for a variety of test statistics commonly used in qualitative- ("affected

118 We propose two new test statistics-conditional expected IBD (EIBD) and adju

119 nown parent-fragment pairs, which results in test statistics consistent with the null distribution.

120 Commonly used test statistics correspond to using least squares to est

121 The test statistic crossed the prespecified futility boundar

122 ximum value of this excess similarity as our test statistic Delta(m,n,b).

123 odels and demonstrated that the power of the test statistic depends on the measure of gene-gene inter

124 Here we introduce a score test statistic derived from a normal likelihood based on

125 We apply our proposed test statistic derived using gPCA to simulated data and

126 ate, we derive the joint distribution of the test statistics developed in the two phases and obtain t

127 ose with no measurable exposure (Wald chi(2) test statistic [df] = 6.58 [1], P = 0.01; 95% confidence

128 aims to estimate the null distribution of a test statistic directly.

129 osterior model probabilities by modeling the test statistics directly instead of modeling the full da

130 gorithms based on recently published data of test statistics, disease prevalence, and relevant costs:

131 Variation in genome-wide test statistic distributions was noted within studies (l

132 es to detect "ancestry association." The new test statistics do not assume a particular disease model

133 more comprehensively by integrating multiple test statistics, each of which has relatively limited ca

134 esis testing is formulated by defining a new test statistics--energy difference.

135 The test statistic explicitly accounts for differences in co

136 ived a non-centrality parameter for the Wald test statistic for association, which allows analytical

137 We propose a general test statistic for detecting differences between gene cu

138 he parameters of this model, and introduce a test statistic for differential expression similar to a

139 We validate the performance of our test statistic for finite synthetic samples and experime

140 We introduce a novel test statistic for genetic association studies that uses

141 trogen plus progestin vs placebo because the test statistic for invasive breast cancer exceeded the s

142 ze loci testing based on approximations of a test statistic for pairs of locus groups.

143 We propose the combination of a Lomb-Scargle test statistic for periodicity and a multiple hypothesis

144 An improved test statistic for selection mapping was developed, in w

145 simultaneously calculate the Kruskal-Wallis test statistic for several millions of marker-trait comb

146 an [SD] score, 40.2 [8.9] vs 35.1 [7.1]; the test statistic for the difference in IDS sum score was 2

147 The test statistic for the difference in network connectivit

148 multiplied by sample size provides the usual test statistic for the hypothesis of no disequilibrium f

149 erties of the models, and propose a modified test statistic for the Li-Wong model that provides an im

150 To determine the appropriate test statistic for the new measure and derive a formula

151 models; and derives the optimal interaction test statistic for this class of regression models.

152 n transform any population-based association test statistic for use in family-based association tests

153 lepoint approximation (SPA) to calibrate the test statistics for analysis of phenotypes with unbalanc

154 tudies, to evaluate the power of alternative test statistics for complex traits, and to examine gener

155 Test statistics for genome association studies that cons

156 We show that our novel Wald-type test statistics for interactions with and without constr

157 drial genomic inflation factors (mtGIFs) and test statistics for simulated case-control and continuou

158 ihood ratio test and partial R(2) statistics.Test statistics for the combined inclusion of the 4-mode

159 dering the individual Cochran-Armitage trend test statistics for the genotype markers.

160 The distribution of association test statistics for the single variant and gene burden a

161 The test statistics for the Wald test were under-inflated at

162 bining rare mutations and construct suitable test statistics for various biological scenarios.

163 We propose to generate a large number of test statistics from a simulation model which has asympt

164 The test statistic has an approximately normal distribution

165 However, the choice of test statistic has been largely ignored even though it m

166 an analysis prior to the computation of the test statistic has broad and powerful applications in ma

167 Our study shows that nonlinear test statistics have great potential in association stud

168 hat under certain conditions, some nonlinear test statistics have much higher power than the standard

169 Furthermore, extreme positive values of the test statistic identify sibs as MZ twins.

170 = 0.001) correlation between the Tajima's D test statistic in full resequencing data and Tajima's D

171 hybrid approach to obtain the P-value of the test statistic in linear time.

172 Because of the gene selection step, test statistic in SPCA model can no longer be approximat

173 is likely to lead to inflation in the median test statistic in the absence of population structure.

174 tion of local ancestry and admixture mapping test statistics in admixed populations with contribution

175 model association tests can produce inflated test statistics in datasets with related individuals, wh

176 ation, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS

177 ccounts for the majority of the inflation in test statistics in many GWAS of large sample size.

178 as used to estimate the null distribution of test statistics in order to achieve the desired false po

179 racterizes the joint distribution of the two test statistics in two-dimensional space.

180 the q-value method by taking the sign of the test statistics, in addition to the P-values, into accou

181 We investigate the properties of these test statistics, including their powers of detecting het

182 we evaluate the relative performance of five test statistics, including two new test statistics.

183 Our proposed Quaternary test statistic incorporates all available evidence on th

184 rare-variant-tailored methodology to reduce test statistic inflation, we identify 64 statistically s

185 We will show that the penalized test statistics intuitively makes sense and through appl

186 The test statistics involve estimating the genewise variance

187 Moreover, the underlying mathematics of our test statistic is a general technique, which can be appl

188 ations, the null distribution for a discrete test statistic is approximated with a continuous distrib

189 A test statistic is defined as the dot-product of the vect

190 We show that, under the null, the resulting test statistic is distributed as a weighted sum of Poiss

191 ith the other affected sibs in families, the test statistic is increased by >20%, on average, for add

192 valid, because the null distribution of the test statistic is not standard normal, even in large sam

193 ber of computations required for the maximal test statistic is O(N2), where N is the number of marker

194 By definition, this test statistic is proportional to the length of the proj

195 A new transmission/disequilibrium-test statistic is proposed for situations in which trans

196 ry-trait-based linkage analysis and that our test statistic is robust with regard to certain paramete

197 Our test statistic is the rate of success that our methods a

198 s of no effect, when the distribution of the test statistic is unknown.

199 how that a class of similarity measure-based test statistics is based on the quadratic function of al

200 some conditions the power of the non-linear test statistics is higher than that of the T2 statistic.

201 Overall, no deviation of the distribution of test statistics is observed from that expected under the

202 y large sample sizes feature selection using test statistics is similar for M and beta-values, but th

203 In the first step, a test-statistic is computed for each probe based on a hie

204 to low quality; (ii) inflation factor of the test statistics (lambda); (iii) number of false associat

205 is shown to be consistent with a multilocus test statistic, ln RV, proposed for identifying microsat

206 The test statistics lnRV and lnRH were used to find regions

207 We call this test statistic "MMLS-C." We found that the ELODs for MML

208 A few test statistics--most notably the nonparametric linkage

209 We found evidence of inflation in the median test statistics of the likelihood ratio and score tests

210 It is interesting to note that the test statistics of the ordinary ridge regression (ORR) h

211 ies, is introduced by simply adding the chi2 test statistics of the two haplotype blocks together.

212 In the second step, the test-statistics of probes within a genomic region are us

213 rior weights may also be used when combining test statistics or to informatively weight p values whil

214 include Laplace mechanisms based on the TDT test statistic, P-values, projected P-values and exponen

215 uantified as a continuous score-fold-change, test-statistic, P-value-comparing biological classes.

216 We show that use of some filter/test statistics pairs presented in the literature may, h

217 proximation to linear and quadratic gene set test statistics' permutation distribution.

218 Apart from variant weights in test statistics, prior weights may also be used when com

219 n of sequences and integration of additional test statistics proposed by other groups.

220 choice of the distribution of the underlying test statistic provide spurious detection of association

221 recombination-fraction estimate, leaving the test statistic quite robust.

222 performed using the likelihood ratio as its test statistic rather than the more commonly used probab

223 Typically, QTL are reported only when the test statistics reach a predetermined critical value.

224 All test statistics reached statistical significance at p <

225 as well as affected sibs, we introduce a new test statistic (referred to as TDS), which contrasts the

226 s effect sizes for comparative analyses, yet test statistics require more observations than variables

227 or population structure and inflation of the test statistic, resolved significant associations only w

228 Wilcoxon test statistics reveals that a subset of primate LINE-1

229 The choice of a TD-based test statistic should be dependent on the predominant fa

230 enables our approach to also evaluate other test statistics such as SKATs, higher criticism approach

231 Based on the p-values, combined test statistics such as the aggregated Cauchy associatio

232 t-Fisher neutral model, and distributions of test statistics (t and Mann-Whitney U) were derived by a

233 ions have a lower fdr for a given value of a test statistic than SNPs in unenriched categories.

234 ults indicated 1.67/1.84 times higher median test statistics than expected under the null hypothesis

235 each marker separately, we propose a single test statistic that follows a chi(2) distribution with 1

236 current report we propose and derive a score test statistic that identifies genes that are associated

237 This results in a test statistic that is a minor variation of those used i

238 We describe a test statistic that uses gPCA to test whether a batch ef

239 e affected sib-pair study design and develop test statistics that are variations on the usual allele-

240 ncrease can be achieved by using alternative test statistics that average enrichment scores calculate

241 Test statistics that borrow information from data across

242 a novel empirical Bayes adjustment to the t-test statistics that can be incorporated into the step-d

243 lated graph can be used to compare different test statistics that can be used to analyze the same exp

244 The objective is to explore test statistics that combine information from haplotype

245 ly observed in the empirical distribution of test statistics that results from the analysis of gene e

246 ts is presented, and two specific non-linear test statistics that use non-linear transformations of m

247 rrays) the same correlation structure as the test statistics that will be calculated from the given d

248 We present a test statistic, the quantitative LOD (QLOD) score, for t

249 ardy-Weinberg Equilibrium (HWE) in NGHS, two test statistics, the CCS method [1] and the QS method [2

250 For discrete test statistics, the P values come from a discrete distr

251 Three commonly used test statistics, the sample mean, SAM statistic and Stud

252 n practice the performance of the non-linear test statistics, they are applied to two real datasets.

253 eses (maximum likelihood ratio) is used as a test statistic to discriminate between true and false id

254 We introduce a test statistic to select genes with significant dose-res

255 s to compare the observed median association test statistic to the expected median test statistic.

256 Finally, we apply the new entropy-based test statistic to two real data sets, one for the COMT g

257 ected null distribution may cause truly null test statistics to appear nonnull.

258 The application of different test statistics to biological data reveals that three st

259 result of two simple measures: (i) adjusting test statistics to exploit information from identifiable

260 n can bias traditional nonparametric linkage test statistics toward the null hypothesis of no locus e

261 statistical power of five association study test statistics (two haplotype-based tests, two marker-b

262 ype I error was appropriate for nearly every test statistic under all conditions.

263 hm is based on modeling the distributions of test statistics under both null and alternative hypothes

264 However, the null distribution of the test statistics under permutation is not the same for eq

265 nvestigate three one-sided and two two-sided test statistics under Q1 and Q2.

266 Asymptotical distributions of the non-linear test statistics under the null and alternative hypothesi

267 e expectation of a wide range of association test statistics under the null hypothesis that there is

268 ticular, we give an explicit formula for the test statistic used in the regression approach.

269 dividual gene level, we adjusted each gene's test statistic using the square root of transcript lengt

270 especially those that enable exploration of test statistics using auxiliary information (covariates)

271 lity that a marker has (no) effect given its test statistic value, also called the local false discov

272 A weighted t-test statistic was applied to calculate probabilities (p

273 We found that the new test statistic was more powerful than the traditional lo

274 Two-sample proportional test statistic was used to evaluate differences between

275 A two-tailed independent sample t test statistic was used to evaluate the relationship bet

276 The null distribution of the test statistics was simulated for the desired false posi

277 part of the significant +11.2% inflation of test statistics we observed in an analysis of 6,322 nons

278 Using voxel-wise t-test statistics, we showed associations between deterior

279 Student t test statistics were applied to report significant findi

280 each participating study, and the resulting test statistics were combined in a meta-analysis.

281 Standard errors and test statistics were corrected for weighting, clustering

282 rovide approximations to the distribution of test statistics when the Newcomb-Benford law does not ho

283 ndard conditioning produces a severe drop in test statistics whereas our approach generally performs

284 of two means, a permutation test might use a test statistic which is the difference of the two sample

285 In the multivariate case, it might use a test statistic which is the maximum of the univariate te

286 y to result in under-inflation of the median test statistic which may mask the presence of population

287 the Hamming distance and develop a suitable test statistic, which is expected to be large for a caus

288 cept of mutual information to derive a novel test statistic, which we can evaluate by computing Jense

289 the correlation matrix of the single-variant test statistics, which can be estimated from one of the

290 for estimating pi0 developed for continuous test statistics, which depend on a uniform or identical

291 For those complicated test statistics whose cumulative distribution functions

292 rmine a formula for the probability that the test statistic will reject the null hypothesis and morta

293 information in a region and it can produce a test statistic with an adaptively estimated number of de

294 Ps simultaneously in analysis but produces a test statistic with reduced degrees of freedom compared

295 e-wide association tests is to develop novel test statistics with high power.

296 ayesian framework Smyth formally derived the test statistics with shrinkage using the hierarchical mo

297 noncentrality parameter approximations of F-test statistics work very well.

298 ccept the null hypothesis of futility if the test statistic z < 0.39 (P >/= .348) and reject the null

299 All the methods depend on constructing a test statistic Z and a so-called null statistic z.

300 t first word on chromosome 7q (nonparametric test statistic [Z] 2.98; P=.001), and subsequent linkage