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 null distribution of the Fisher combination test statistic.

9 to approximate the null distribution of the test statistic.

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

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

12 ically derived large deviations rate for the test statistic.

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

14 ds can be efficiently combined in an overall test statistic.

15 ta that is a variant of the likelihood-ratio test statistic.

16 iation test statistic to the expected median test statistic.

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

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

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

20 e inaccurate asymptotic distributions of the test statistics.

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

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

23 quire independence or weak dependence of the test statistics.

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

25 ation of population variances which improves test statistics.

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

27 ables simultaneous visualization of multiple test statistics.

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

29 stimation procedure based on the linear rank test statistics.

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

31 rates genomic functional annotation and GWAS test statistics.

32 The score statistic comprises two component test statistics.

33 the statistical significance of the observed test statistics.

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

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

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

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

38 parisons for independent or weakly dependent test statistics.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

60 Correlations between the test statistic and the width of the confidence interval

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

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

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

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

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

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

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

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

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

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

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

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

73 of data, which are based on goodness-of-fit test statistics and standard errors of parameter estimat

74 APM test statistics and the Gaussian lod score are shown to

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

94 that two individuals are sibs, we propose a test statistic based on the summation, over a large numb

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

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

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

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

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

100 Power and significance studies show that the test statistic calculated by use of 50 unlinked markers

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

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

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

104 The testing statistic can be constructed using any genotype-

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

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

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

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

109 The test statistic crossed the prespecified futility boundar

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

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

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

113 A test statistic derived from L, the efficient score stati

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

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

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

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

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

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

120 d populations but allow the determination of test statistic distributions via simulation or data perm

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

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

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

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

125 The test statistic explicitly accounts for differences in co

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

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

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

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

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

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

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

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

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

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

136 The test statistic for the difference in network connectivit

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

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

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

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

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

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

143 Test statistics for genome association studies that cons

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

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

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

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

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

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

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

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

152 genetic region are involved in disease, the test statistic gives a closer fit to the null expectatio

153 The test statistic has an approximately normal distribution

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

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

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

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

158 Three test statistics have recently been proposed in the liter

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

160 = 0.001) correlation between the Tajima's D test statistic in full resequencing data and Tajima's D

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

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

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

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

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

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

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

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

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

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

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

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

173 The test statistics involve estimating the genewise variance

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

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

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

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

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

179 Power of the test statistic is investigated under an alternative mode

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

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

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

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

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

185 The null distribution of the test statistic is shown to be approximately chi-squared

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

206 The test statistic profiles show some difference between the

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

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

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

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

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

212 All test statistics reached statistical significance at p <

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

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

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

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

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

218 uous data: a continuous chi-square test with test statistic T(CCS) and a test based on Hellinger's di

219 nd a test based on Hellinger's distance with test statistic T(HD).

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

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

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

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

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

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

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

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

228 Test statistics that borrow information from data across

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

247 ng data sequence, it is impossible (with any test statistic) to distinguish perfectly between linear

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

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

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

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

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

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

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

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

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

257 Next, we develop a test statistic using cytonuclear disequilibria via the t

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

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

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

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

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

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

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

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

266 Student t test statistics were applied to report significant findi

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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