ライフサイエンスコーパス: estimator

1 es in emissions (Kernel density distribution estimator).

2 ks and calculates P-values using an unbiased estimator.

3 % CI, 14.7%-15.7%), using the Aalen-Johansen estimator.

4 ilepsy was determined using the Kaplan-Meier estimator.

5 molecule lifetime data and an unbiased ratio estimator.

6 mates with the Respondent-Driven Sampling II estimator.

7 displacement and by the dynamical functional estimator.

8 mputed by using the Lagrangian speckle model estimator.

9 dence intervals than the difference-of-means estimator.

10 residual confounding with the random-effects estimator.

11 ents a valuable feature of its visual motion estimator.

12 ative incidences were obtained by Turnbull's estimator.

13 ull matching and targeted maximum likelihood estimator.

14 sed estimator and the corresponding variance estimator.

15 sson regression model with a robust variance estimator.

16 azards (Cox PH) model and product-limit (PL) estimator.

17 ler using the RW estimator than using the RR estimator.

18 llustrate how imputations are made by the KM estimator.

19 ed by using nonparametric maximum likelihood estimator.

20 articipants without NAFLD and a fat fraction estimator.

21 locks: a controller, a simulator and a state estimator.

22 nge for state-of-the-art intrinsic dimension estimators.

23 precise and more accurate than the original estimators.

24 frequencies, the actual relatedness and the estimators.

25 d probability of detection on common density estimators.

26 ess of our estimates using three alternative estimators.

27 consistently outperformed other regularized estimators.

28 uclei in both morphometric and stereological estimators.

29 ding method-of-moment and maximum-likelihood estimators.

30 tissues by employing suitable kernel density estimators.

31 y expressed (DE) genes based on the Bayesian estimators.

32 pel-Ziv ('76 & '78) and Titchener complexity estimators.

33 We find broad agreement between all estimators.

34 ncluding normalizing flows and density ratio estimators.

35 gger regression and weighted median and mode estimators.

36 eve higher efficiency than comparably robust estimators.

37 bias of the widely used convex and nonconvex estimators.

38 mary prevention, but the performance of risk estimators (2013 Pooled Cohort Equations [PCE] and Risk

39 pathway generator, a reaction rate constant estimator, a mechanistic reduction module, and a KMC sol

40 d consider, and ultimately reject, the naive estimator, a statistic based on the observed sample data

41 on of gut metabolic modeling and statistical estimators, able to capture their dynamic interplay.

42 The large influence of scale of movement on estimator accuracy emphasizes the importance of effectiv

43 e performed simulation studies to assess our estimators' accuracy and examined potential sources of b

44 The resulting debiased estimators admit nearly precise nonasymptotic distributi

45 The new estimator also reduces the computational time by at leas

46 ement had the greatest impact on accuracy of estimators, although all estimators suffered reduced per

47 a variability index for the AW-Fisher weight estimator and a co-membership matrix to categorize (clus

48 sults in similar estimates as the split half estimator and approaches the true noise ceiling under a

49 did survival analyses with the Kaplan-Meier estimator and evaluated using the log-rank test.

50 changes in net survival with the Pohar-Perme estimator and excess mortality rate with a flexible para

51 imating the distribution of the heritability estimator and for constructing accurate confidence inter

52 nalysis was performed using the Kaplan-Meier estimator and life table.

53 ate the empirical properties of the proposed estimator and the corresponding variance estimator.

54 ogy, while preserving the Mutual Information estimator and the Network inference accuracy of the orig

55 OR is augmented with the proposed time-delay estimator and the predictor for eye position relative to

56 estimator, the maximum estimated likelihood estimator and the semiparametric maximum likelihood esti

57 symptotic properties of the proposed network estimator and the test for pathway enrichment, and inves

58 s a provable guarantee for the Efron-Thisted estimator and, in addition, a variant with stronger theo

59 Here, we apply an ensemble of parametric estimators and a novel technique that includes conspecif

60 zed estimating equations and robust variance estimators and included adjustment for plasma HIV VL.

61 Kaplan-Meier estimators and log rank test were used to compare the ov

62 r a valid alternative to classical diversity estimators and may have broad applicability in the field

63 gery and matched controls using Kaplan-Meier estimators and stratified, adjusted Cox regression analy

64 with augmented inverse probability weighted estimators and targeted maximum likelihood estimators to

65 ghlight the high theoretical accuracy of our estimators and yield insights into the impact of potenti

66 survival probabilities using a Kaplan-Meier estimator, and a relative risk of patient and graft mort

67 estimator, the inverse probability weighted estimator, and the maximum likelihood estimator for the

68 chieves the minimum variance for an unbiased estimator, and we can compute calibrated estimates of th

69 fficient than the simple difference-of-means estimator, and we provide a conservative estimator of th

70 n, inverse-probability-of-treatment-weighted estimators, and instrumental-variable (IV) analysis.

71 r variance and mean squared error than other estimators; and the structural mean models estimator del

72 f Cardiology/American Heart Association Risk Estimator application as an implementation tool, and add

73 ents of cost functions for which two popular estimators are appropriate, and we implement a stochasti

74 ed and empirical data, that the two modified estimators are much less biased, more precise and more a

75 Relief-based estimators are non-parametric in the statistical sense t

76 All the existing intrinsic dimension estimators are not reliable whenever the dataset is loca

77 These performance advantages of the modified estimators are shown to increase with a decreasing sampl

78 The estimators are simple, linear, computationally efficient

79 Our debiased estimators are tractable algorithms that provably achiev

80 phylogeographic signal and used demographic estimators as a proxy for invasion potency.

81 es over previously known nondynamic distance estimators as determined from electric image blur.

82 e the physiological processes underlying our estimator at the cellular level.

83 mator performs as well as current background estimators at low molecular densities and significantly

84 A new risk estimator based on a pooled cohort equation is presented

85 ry (MCA): MCA pulsatility index (PIa) and an estimator based on diastolic flow velocity (FVd).

86 72 for FVsv versus ICP), whereas PIa and the estimator based on FVd did not correlate with ICP signif

87 It also shows that relatedness estimators become even more biased when they use allele

88 first time, that the widely used relatedness estimators become severely biased when they use allele f

89 d the Wilcoxon rank-sum test, Hodges-Lehmann estimator, Bland-Altman test, multivariable logistic reg

90 We demonstrate the effectiveness of our estimators both by providing theoretical guarantees that

91 ciently computing MEMMs in cases where other estimators break down, including the full thermodynamics

92 mal estimator is not the widely-used plug-in estimator, but one developed via empirical Bayes.

93 tment to improve upon the intention-to-treat estimator, but they are rarely used in practice, probabl

94 s were trained to develop an unbiased carbon estimator by using 92 1-ha ground plots distributed acro

95 A relatively new estimator, called "DNAm GrimAge", is notable for its sup

96 and conservation of wildlife, yet rarely are estimators compared in their robustness to effects of ec

97 r estimators; and the structural mean models estimator delivers the smallest bias, though generally i

98 , e.g. binarization, histogram-based and KNN estimators, depend on known data or domain characteristi

99 n problem and studied the convergence of the estimator, deriving a formula that links the TBR estimat

100 The estimators developed for this purpose assume that marker

101 When fit to the haplotype data, our estimator displayed favorable properties in terms of con

102 We show that the estimator does not depend on the shape of the intrinsic

103 The other method, a quantum kernel estimator, estimates the kernel function on the quantum

104 truncated estimators, the maximum-likelihood estimator exhibited lower root mean square error under s

105 ion of the most efficient correlation matrix estimator for a given neural circuit must be determined

106 In this situation, the best estimator for an individual mean, the sample average, is

107 We describe a maximum-likelihood estimator for autopolyploids, and quantify its statistic

108 os (ORs) and 95% CIs using a median unbiased estimator for binary data in an unconditional logistic r

109 tive value, and accuracy and to evaluate the estimator for correlation, bias, limits of agreements, a

110 commercially available air-cooled CCD, a new estimator for data analysis and a high heralding efficie

111 ficant decrease in the instrumental variable estimator for eGFR (P<0.01) in a Mendelian randomization

112 ating the background, QNB uses a more robust estimator for gene expression by combining information f

113 ed segmentation algorithm, VEGA: Variational estimator for genomic aberrations, which has previously

114 ur hybrid approach exploits a novel accuracy estimator for our core method, which estimates the unkno

115 or and the semiparametric maximum likelihood estimator for parameters in a logistic model.

116 t ALFRED-G, a greedy alignment-free distance estimator for phylogenetic tree reconstruction based on

117 h inverse variance method, DerSimonian-Laird estimator for tau(2), and Cohen's d were calculated.

118 ighted estimator, and the maximum likelihood estimator for the first-stage association and, more impo

119 res of a generic dataset and develop a novel estimator for the intrinsic dimension (ID).

120 We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poiss

121 lementation, we further provide a consistent estimator for the standard error of the treatment effect

122 zed estimating equation with robust variance estimator for the top three differential diagnoses (T3DD

123 od was realizing that the variability of the estimators for AOPD is sometimes greater than the adjust

124 across different genes and provides powerful estimators for evaluating gene expression levels.

125 d the construction of various epigenetic age estimators for human clinical outcomes and health/life s

126 depth explication of 2 of the many potential estimators for illustrative purposes: the Baron and Kenn

127 We used Kaplan-Meier estimators for survival probabilities and cumulative inc

128 A new likelihood-based estimator [Formula: see text] for contemporary effective

129 to evaluate the performance of the proposed estimator [Formula: see text], and the results show that

130 sent and our assumptions hold, we argue that estimators from models that include a negative control e

131 In simulations, effect estimators from models that included the negative contro

132 argest effects were seen for methylation age estimators (GrimAge) and the frailty index (FI).

133 A variety of mathematical estimators have been used to quantify the degree of prot

134 Two-stage residual inclusion (TSRI) estimators have been used when researchers are willing t

135 Illustrating imputations hidden by the KM estimator helps to clarify these assumptions and therefo

136 In joint models, two methylation age estimators (Horvath and GrimAge) and FI remained predict

137 Demographic estimators impute two ancestral population bottlenecks:

138 tudies show good performance of the proposed estimator in terms of bias and coverage probability.

139 de evidence for the existence of a stiffness estimator in the human posterior parietal cortex (PPC).

140 the conditional LEF and the variance of the estimator in the right-censoring setting.

141 n of the estimation accuracy of our debiased estimators in both rate and constant.

142 variance and performs better than all other estimators in simulated correlation analyses.

143 We demonstrate the usefulness of these estimators in the analysis of high-throughput biological

144 tudy analyzes morphometric and stereological estimators in the whole AC and its three main nuclei (la

145 identity and structure of the most efficient estimator informs about the types of dominant dependenci

146 we show that the IV principal stratification estimator is a modified per-protocol estimator that shou

147 The superior performance of the direct estimator is evident both for simulated data and for neu

148 eoretical conditions that guarantee that the estimator is more efficient than the simple difference-o

149 Interestingly, the corresponding optimal estimator is not the widely-used plug-in estimator, but

150 new, to our knowledge, and simple background estimator is proposed, called the local statistical perc

151 at, unlike existing methods, our closed-form estimator is robust across a wide range of architectures

152 The IV principal stratification estimator is simple to implement but has had limited use

153 Our results suggest that the naive estimator is substantially biased under the alternative,

154 is preferable at low polydispersity, the new estimator is the most accurate and precise at intermedia

155 The LSP background estimator is thus suited for single-particle TIRF micros

156 The variance of this estimator is uniformly bounded, achieves the minimum var

157 ross various estimators (the Baron and Kenny estimator (J Pers Soc Psychol.

158 macaque data set, coherence and our new MIF estimator largely agree.

159 e results included MR-Egger, weighted-median estimator, 'leave-one-out', and multivariable MR analyse

160 s superior performance, this 'sparse+latent' estimator likely provides a more physiologically relevan

161 underlying probability distribution for the estimator, making it difficult to determine the statisti

162 The IER estimator may underestimate the excess relative risk of

163 s, as long as computing a maximum-likelihood estimator (MLE) is feasible under the null.

164 logistic regression using maximum likelihood estimator (MLE) to infer the odds ratios of SNPs may not

165 A patient-specific estimator of absolute chemotherapy benefit was computed

166 The MAP Bayesian estimator of all important chromatographic parameters co

167 Hedges g was used as an estimator of effect size in group comparisons.

168 dicating that UMI counts are not an unbiased estimator of gene expression levels.

169 ed ultrasound nICP methods, ONSD is the best estimator of ICP.

170 ed feature selection method called Lancaster Estimator of Independence (LEI) that utilizes allele fre

171 based feature selection algorithm, Lancaster Estimator of Independence (LEI), as well as other genoty

172 We introduce a new estimator of particle size polydispersity for dynamic li

173 Here we present an estimator of past mobility that addresses these issues b

174 ans estimator, and we provide a conservative estimator of the asymptotic variance, which can yield ti

175 We employ a regularized estimator of the correlation matrix to ensure Meff is ro

176 es that valid use of the rectified EMG as an estimator of the neural drive requires low contraction l

177 perior to the actual rectified EMG signal as estimator of the neural drive to muscle.

178 ed surface EMG signal is commonly used as an estimator of the neural drive to muscles and therefore t

179 of chemical measurements, since it is a good estimator of the overall uncertainty.

180 Regarding ATI, Tmax (AUC 0.9) was the best estimator of the penumbra (group A), CBF relative to the

181 The Kaplan-Meier (KM) estimator of the survival function imputes event times f

182 ing events: an inverse probability weighting estimator of the survivor average causal effect and the

183 We then obtained the out of bag estimator of Y based on the bagging neighborhood structu

184 The quantitative estimators of AC could be useful to understand the impac

185 ovariates may induce substantial bias in MSM estimators of causal effects of time-varying exposures,

186 genes can be used as biomarkers in PCR-based estimators of Hg-methylator abundance.

187 Genomic estimators of IBDG included the increase in individual h

188 t uses l1-penalization to induce sparsity in estimators of loading vectors.

189 algebraic expressions for maximum-likelihood estimators of model parameters and estimated information

190 Although the resulting estimators of N, T and Vk* are upwardly biased for speci

191 We sought statistically efficient estimators of neural correlation matrices in recordings

192 al performances of five additional polyploid estimators of relatedness were also quantified under ide

193 d kinship inferred from microsatellite-based estimators of relatedness.

194 the construction of confidence intervals and estimators of SEs for REML rely on asymptotic properties

195 Pareto hypervolume and spectral analysis as estimators of short term multi-omic (transcriptomic and

196 lated noise structure, we evaluate different estimators of the variance of the responses and their im

197 population genetic statistics, for instance, estimators of theta or neutrality tests such as Tajima's

198 Understanding and applying estimators of volume, surface, length or number does not

199 This suggests that power-law based estimators offer a valid alternative to classical divers

200 We develop a powerful MIF estimator optimized for correlating frequency coupling w

201 riance-covariance structure of the parameter estimator, or else by a straightforward simulation appro

202 200 features as a generic-disease-likelihood estimator, outperforming published gene-level scores.

203 ur method outperforms current genome quality estimators, particularly for estimating contamination, a

204 atients vs 156 of 296 patients (Kaplan-Meier estimator percentages, 51.2% vs 53.6%; unadjusted differ

205 Fat fraction estimator performance was evaluated by correlation, line

206 The estimators' performance was evaluated in terms of PS pre

207 The RW imputation variance estimator performed much better and should be employed w

208 We show that the LSP background estimator performs as well as current background estimat

209 Our potency estimator (Point of Departure, PODWES) is defined as the

210 clerotic cardiovascular disease (ASCVD) risk estimator (pooled cohort equation [PCE]) is untested.

211 a good trade-off in term of quality of point estimator precision and credible interval estimations fo

212 The estimator-predicted fat fraction correlated with MRI PDF

213 Our multitaper MIF estimator produces low variance and performs better than

214 te the calculation of an imputation variance estimator proposed by Robins and Wang (RW) in a scenario

215 on, the popular multiple-imputation variance estimator proposed by Rubin ("Rubin's rules" (RR)) is bi

216 Moreover, heritability estimators provided by existing methods may have large s

217 ed controlled trials, the intention-to-treat estimator provides an unbiased estimate of the causal ef

218 These estimators range from monoprotic, diprotic, and triproti

219 The consistency of propensity score (PS) estimators relies on correct specification of the PS mod

220 dard errors for the linear and logistic TSRI estimators, respectively.

221 our methods to real data and demonstrate our estimators' retained accuracy after filtering SNPs by sa

222 d 95th percentile of the distribution of the estimator (rho((95%))) at 0.0056 (p-value = 0.0026) and

223 this range is the best possible and that the estimator's mean-square error is near optimal for any t

224 od samples were collected, and the Adherence Estimator scale was completed.

225 abolic outcomes, and scores on the Adherence Estimator scale, which assesses beliefs related to nonad

226 Adherence Estimator scores improved in the NAVIGATE group but not

227 isely, we show theoretically that a Bayesian estimator should reduce the weight of sensory informatio

228 uantitation in GI-NETs: (1) Synaptophysin-KI-Estimator (SKIE), a pipeline automating Ki-67 index quan

229 racy of the single-point insulin sensitivity estimator (SPISE) to diagnose cardiometabolic risk in Ch

230 rvals were calculated using the Kaplan-Meier estimator stratified by the initial CD4 cell count at th

231 In particular, scale of movement impacted estimators substantially, such that area covered and spa

232 of our power-law versus classical diversity estimators such as Capture recapture, Chao, ACE and Jack

233 pact on accuracy of estimators, although all estimators suffered reduced performance when detection p

234 the UK-1 contract area but species-richness estimators suggest this could be as high as 229.

235 or the log odds was 29% smaller using the RW estimator than using the RR estimator.

236 ximum likelihood estimation, a double robust estimator that accounts for associations between confoun

237 f large emission sources using a statistical estimator that integrates observations from multiple gro

238 Here we introduce a new intrinsic dimension estimator that leverages on simple properties of the tan

239 , Good and Toulmin constructed an intriguing estimator that predicts U for all [Formula: see text] Su

240 We derive a robust variance estimator that reflects the true variability of the scor

241 ication estimator is a modified per-protocol estimator that should be obtainable from any randomized

242 Consequently, naive connectivity estimators that neglect these common input effects are h

243 We derive a class of estimators that provably predict U all of the way up to

244 amily of STatistical Inference Relief (STIR) estimators that retains the ability to identify interact

245 e direct and indirect effects across various estimators (the Baron and Kenny estimator (J Pers Soc Ps

246 opment and simulations, we compare the naive estimator, the inverse probability weighted estimator, a

247 ed by randomization, including the case-only estimator, the maximum estimated likelihood estimator an

248 When comparing truncated estimators, the maximum-likelihood estimator exhibited l

249 This article introduces a number of pi0 estimators, the regression and 'T' methods that perform

250 l parameters given by the maximum likelihood estimators, the relative precisions are given as explici

251 in bias in the numerator for the standard IV estimator; the bias is amplified in the treatment effect

252 Application of our estimator to 6.59 million donors in the Be The Match Reg

253 d and extends the usual correlation integral estimator to alleviate the extreme undersampling problem

254 Then, we use the madogram estimator to calculate the scaling exponent of the corre

255 Computer simulations showed this estimator to have very little bias for realistic amounts

256 Here we propose an estimator to infer numbers of molecules from fluctuation

257 therefore, have developed a power-law based estimator to measure allele and haplotype diversity that

258 sample instrumental variable (SSIV) analysis estimator to minimize confounding and reverse causality.

259 se NAFLD (MRI PDFF >= 5%) and a fat fraction estimator to predict MRI PDFF.

260 uting, and machine learning to create a risk estimator to stratify new and existing drugs according t

261 We applied our estimator to ultra-large-scale GWAS summary data of 30 c

262 We used inverse probability-weighted estimators to adjust for differences in treatment alloca

263 rams such as Structure make use of heuristic estimators to approximate this quantity.

264 d estimators and targeted maximum likelihood estimators to generate more efficient and unbiased estim

265 mmings (n = 2000) and non-parametric species estimators to investigate the species composition of the

266 trics using kernel utilization density (KUD) estimators to measure isotopic niche size and overlap.

267 aution is warranted when using nonparametric estimators to quantify exposure effects.

268 This study modifies two estimators to suit small samples and shows, both analyti

269 learning tool which uses deep neural density estimators-trained using model simulations-to carry out

270 The Kaplan-Meier estimator, U test, and Cox regression analysis were used

271 y the resulting Lasso-based treatment effect estimator under the Neyman-Rubin model of randomized exp

272 d the distribution of the maximum likelihood estimator under the null distribution.

273 statistical performances of three polyploid estimators under both ideal and actual conditions (inclu

274 mance of different aperture-based background estimators used particularly in single-molecule Forster

275 We demonstrate the efficacy of this estimator using spatiotemporally explicit simulations an

276 certainty in imputation through the variance estimator using the jackknife, one of resampling techniq

277 ear OS were determined by using Kaplan-Meier estimators using the log-rank test and multivariate Cox

278 causal effects using propensity score-based estimators via extensive simulation studies and real dat

279 An unbiased statistical estimator was applied to the sample of reference observa

280 ion predicted by a two-variable fat fraction estimator was correlated with MRI PDFF (Spearman rho = 0

281 Kaplan-Meier estimator was used to assess survival stratified by IEp

282 The Mantel-Haenszel estimator was used to compare risks of arterial thromboe

283 In addition, a cluster sandwich estimator was used to determine if any differences in be

284 cts model with restricted maximum-likelihood estimator was used to synthesise the effect size (assess

285 The MAP Bayesian estimator was validated using two external-validation da

286 del with initial conditions (Wooldridge-type estimator) was adopted for the estimation.

287 of 95% confidence intervals based on the RR estimator were too high and became worse as more observa

288 The SL-based estimators were associated with the smallest bias in cas

289 DNA methylation age estimators were derived using a transformed version of c

290 hood estimation and robust sandwich variance estimators were implemented.

291 , Chao 1 index, and abundance-based coverage estimator, were 0.62 (0.39-0.99), 0.61 (0.38-0.98), and

292 cost-effective compared to conventional DoA estimators where multiple antennas and receivers are cla

293 We describe in this article a doubly robust estimator which combines both models propitiously to off

294 ore we propose generalizations of the direct estimator which measure changes in stimulus encoding acr

295 Rather, a shrinkage estimator, which shrinks individual sample averages towa

296 ower bias than previously proposed shrinkage estimators, while still reducing variance for those gene

297 variance with restricted maximum likelihood estimator with Hartung-Knapp adjustment.

298 ross-validation tests, the covariance matrix estimator with this structure consistently outperformed

299 We further compared these 2 variance estimators with a simulation study which showed that cov

300 We show that TSRI estimators with modified standard errors have correct ty

301 of no causal effect; the maximum likelihood estimator yields smaller variance and mean squared error