ライフサイエンスコーパス: estimator

1 es in emissions (Kernel density distribution estimator).

2 dditive models, nonparametric kernel density estimators).

3 ents a valuable feature of its visual motion estimator.

4 ative incidences were obtained by Turnbull's estimator.

5 % CI, 14.7%-15.7%), using the Aalen-Johansen estimator.

6 sed estimator and the corresponding variance estimator.

7 ilepsy was determined using the Kaplan-Meier estimator.

8 genic flux, but it is a relatively imprecise estimator.

9 more accurate confidence limits than the DL estimator.

10 ously described inverse-probability-weighted estimator.

11 ghting approach and a model-based imputation estimator.

12 mator improve efficiency over the simple IPW estimator.

13 molecule lifetime data and an unbiased ratio estimator.

14 asound Structure Factor Size and Attenuation Estimator.

15 ate problem using an unbiased and consistent estimator.

16 mates with the Respondent-Driven Sampling II estimator.

17 displacement and by the dynamical functional estimator.

18 mputed by using the Lagrangian speckle model estimator.

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

20 residual confounding with the random-effects estimator.

21 consistently outperformed other regularized estimators.

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

23 tissues by employing suitable kernel density estimators.

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

25 precise and more accurate than the original estimators.

26 (IPW) estimators and maximum likelihood (ML) estimators.

27 frequencies, the actual relatedness and the estimators.

28 e energies as it shows faster convergence of estimators.

29 imately equal to the power of traditional IV estimators.

30 d probability of detection on common density estimators.

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

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

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

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

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

36 mean squared error (MMSE) conditional error estimator and demonstrate its computation over the featu

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

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

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

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

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

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

43 adjustment methods, the observed-to-expected estimator and the risk-standardized ratio.

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

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

46 tic independence of the marginal association estimator and various interaction estimators leads to a

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

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

49 ession to case-control data may yield biased estimators and incorrect statistical inference.

50 f various inverse probability weighted (IPW) estimators and maximum likelihood (ML) estimators.

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

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

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

54 pathway generator, a reaction rate constant estimator, and a KMC solver.

55 erminals were obtained through the Cavalieri estimator, and adequate correction factors for vesicle p

56 new separate-sampling cross-validation error estimator, and prove that it satisfies an 'almost unbias

57 bability was evaluated with the Kaplan-Meier estimator, and the agreement of progression detection am

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

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

60 Here we test five widely used non-parametric estimators, and develop and validate a novel method, Div

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

62 rge impacts on the accuracy and precision of estimators, and specialized estimation techniques have b

63 d estimation of an empirical-Bayes shrinkage estimator; and 2-stage tests.

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

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

66 Uncertainty is inherent in any statistical estimator applied to noisy data, so our confidence in su

67 Particular problems with the DL estimator are discussed, and several alternative methods

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

69 Moreover, the doubly robust estimators are easy to implement and provide an attracti

70 We report that the verification-based estimators are meaningful in the light of a feed forward

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

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

73 The estimators are simple, linear, computationally efficient

74 IPW estimators are typically less efficient than ML estimato

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

76 elines on the use of weighted and unweighted estimators, as well as the relevant software.

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

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

79 the anticipated decrease in the precision of estimators at the design stage.

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

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

82 72 for FVsv versus ICP), whereas PIa and the estimator based on FVd did not correlate with ICP signif

83 and evaluated performance of a single-sample estimator based on linkage disequilibrium (LD), which pr

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

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

86 ability of genotypic and phenotypic distance estimators between pairs of maize inbred lines to predic

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

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

89 imators are typically less efficient than ML estimators but are robust against model misspecification

90 (e.g., using an instrumental variables (IV) estimator) but not for other randomized study designs.

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

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

93 This estimator, called independent components regression, con

94 This targeted maximum likelihood estimator can be used to target various parameters of in

95 mple is presented to show that use of the DL estimator can lead to erroneous conclusions.

96 sment tools so that future iterations of the estimators can be improved to more accurately assess ris

97 geneous studies--the Der Simonian-Laird (DL) estimator--can produce biased estimates with falsely hig

98 e, the Structure Factor Size and Attenuation Estimator cellular imaging method displayed a RBC aggreg

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

100 t, the fixed effects, instrumental variables estimator, controlling for unobserved heterogeneity, fin

101 Finally we discuss different diagnostic estimators defined by formal verification techniques, in

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

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

104 The estimators developed for this purpose assume that marker

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

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

107 nstrated the bias in non-parametric richness estimators (e.g. Chao1 and ACE) and diversity indices wh

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

109 However, currently only few estimators exist for individuals that are admixed, i.e.

110 try from more than one population, and these estimators fail in some situations.

111 Difference-in-differences estimators find that exposure to the campaign increases

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

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

114 oposed regression model provides a practical estimator for attachment efficiencies of C. parvum oocys

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

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

117 relationship between Rose and van der Laan's estimator for case-control data and the one we had previ

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

119 We also develop a new doubly robust estimator for determining the RERI with case-control dat

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

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

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

123 The Kaplan-Meier estimator for local tumor control was 92.1% at 10 years

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

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

126 The guideline also provides a new risk estimator for primary prevention decisions, including st

127 ihood of the model is used to derive a ridge estimator for simultaneous factor learning and detection

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

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

130 Defining an estimator for the LOD in this scenario has shown to be m

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

132 To correct this omission, an estimator for the random error variance in this situatio

133 On the basis of this result, we develop an estimator for the selection coefficient driving a sweep.

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

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

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

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

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

139 We demonstrated that the 2 doubly robust estimators generally outperformed inverse probability we

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

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

142 and intuitive and because maximum likelihood estimators have desirable large-sample properties in the

143 ical refinements to estimating rates, called estimators, have been described to facilitate determinat

144 ated nonparametrically and the augmented IPW estimator improve efficiency over the simple IPW estimat

145 Demographic estimators impute two ancestral population bottlenecks:

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

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

148 se probability weighting and 2 doubly robust estimators in a variety of scenarios.

149 ore accurate than commonly used biodiversity estimators in microbiological and immunological populati

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

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

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

153 ally and numerically how the accuracy of our estimator is affected by the decay of the sweep pattern

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

155 a phase-retrieval enabled maximum-likelihood estimator is implemented.

156 r the study of LD as the distribution of its estimator is less frequency dependent than that of the s

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

158 , an optimal 3D single-molecule localization estimator is presented in a general framework for noisy,

159 The Structure Factor Size and Attenuation Estimator is proposed as a real-time noninvasive monitor

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

161 This estimator is shown to be efficient, meaning it reaches t

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

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

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

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

166 opposed to a widely used maximum-likelihood estimator, it gives clear warning signs when a nonidenti

167 ssociation estimator and various interaction estimators leads to a simple and flexible way of combini

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

169 The IER estimator may underestimate the excess relative risk of

170 factors are incorporated, using the new risk estimators may lead to inaccurate assessment of atherosc

171 phy are performed using a maximum-likelihood estimator method, allowing decoherence, leakage out of t

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

173 been given to the calculation of association estimators, no formal methods have been described for es

174 Further, we review an existing double robust estimator not considered by VanderWeele and Vansteelandt

175 s where we were able to define a factual FDR estimator of 'true' FDR we evaluate several popular vari

176 ding third-moment skewness corrections in an estimator of .

177 This article introduces a new estimator of a LD parameter (rho(2)) that is much easier

178 The MAP Bayesian estimator of all important chromatographic parameters co

179 indicating that this assay may be useful as estimator of astringency.

180 We then describe a robust estimator of functional connectivity based on interregio

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

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

183 istributed and admits a consistent jackknife estimator of its variance.

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

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

186 be less frequency dependent than that of the estimator of r(2), a widely used metric for assessing LD

187 requency dependent than that of the standard estimator of r(2).

188 ecture that the sampling distribution of the estimator of rho(2) could be less frequency dependent th

189 elled simultaneously to produce a population estimator of rho.

190 d latent class analysis (LCA) as an unbiased estimator of test accuracy.

191 resenting a simple closed-form doubly robust estimator of the adjusted odds ratio for a binary exposu

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

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

194 s a limit, and we derive a recently proposed estimator of the narrow sense heritability as a corollar

195 ramework, we also compute the variance of an estimator of the population size that is based on the me

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

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

198 . recently developed so-called doubly robust estimators of an adjusted odds ratio by carefully combin

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

200 Genomic estimators of IBDG included the increase in individual h

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

202 In instances where nonparametric estimators of N(e) such as the skyline struggle to repro

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

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

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

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

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

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

209 Estimators of the number of unseen species are needed to

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

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

212 ies with complex sampling schemes, IPW-based estimators offer flexibility and robustness, and therefo

213 the Oil Production Greenhouse Gas Emissions Estimator (OPGEE) to provide open-source, transparent, r

214 the Oil Production Greenhouse gas Emissions Estimator (OPGEE), an open source engineering-based life

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

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

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

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

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

220 These estimators range from monoprotic, diprotic, and triproti

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

222 etic diversity around the adaptive site, our estimator requires much shorter sequences but sampled at

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

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

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

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

227 Adherence Estimator scores improved in the NAVIGATE group but not

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

229 uber M estimate with Huber weights, a robust estimator similar to a trimmed mean.

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 action curves that did not plateau, existing estimators systematically increased with sample size.

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

237 and highlights the magnitude of biases in an estimator that ignores the effects of an unequal probabi

238 on age were estimated using a double-kernel estimator that incorporates sample weights.

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

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

241 e presumed role of the cerebellum as a state estimator that provides hierarchically lower regions (V5

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

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

244 the correct demographic history, model-based estimators that can draw on prior information about popu

245 Here, we study the precision of sCFR estimators that combine data from several levels of the

246 proposed weighted estimators with unweighted estimators that disregard the sampling design.

247 Difference-in-differences and fixed effects estimators that exploit the panel nature of the data are

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

249 al nonresponse rates, and we derive variance estimators that properly account for the sampling design

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

251 Herein, we develop weighted estimators that reflect unequal selection probabilities

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

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

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

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

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

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

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

259 reaction pathways, a reaction rate constant estimator to estimate the reaction rate constant for eac

260 ion profiles for all species, and a toxicity estimator to estimate the toxicity of major species and

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

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

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

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

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

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

267 ble logistic regression with robust sandwich estimators to estimate odds ratios for infertility, adju

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

269 We demonstrated how to apply these estimators to our motivating example.

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

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 e phase-retrieval enabled maximum-likelihood estimator using a particular engineered PSF microscope d

276 mpared the performance of a Horvitz-Thompson estimator using inverse probability weighting and 2 doub

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

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

279 Since this estimator utilizes the novel variation arising from muta

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

281 st algorithm with a 10-fold cross-validation estimator was used to test accuracy of CV risk classific

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

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

284 Bacterial OTU richness (Chao1 estimator) was highest (> 700) in the upper Hanford form

285 near approximation to the maximum likelihood estimator, we derive the Spectral Meta-Learner (SML), an

286 The Kaplan-Meier estimators were 77.4% for survival and 70.1% for the abs

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

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

289 ore, despite the non-Markovian nature of our estimator when applied sequentially over [Formula: see t

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

291 control-weighted targeted maximum likelihood estimator, which has improved properties in comparison w

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

293 rs support replacing universal use of the DL estimator with analyses based on a critical synthesis th

294 is available for the entire cohort, the IPW estimator with selection probabilities estimated nonpara

295 quently, a simulation study compared the new estimator with that of r(2) using several scenarios of L

296 the standard error, which helps us find the estimator with the best precision given fixed resources.

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

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

299 ly, the performance of the proposed weighted estimators with unweighted estimators that disregard the

300 teered molecular dynamics with the Jarzynski estimator, with an overall good agreement between the th

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