Sentence examples for both effect estimation and from inspiring English sources

In fact, the winner's curse can affect all under-powered studies in which the same observations are used for both effect estimation and hypothesis testing, for example eQTL (Choy et al. 2008), haplotype association, G × G or G × E analyses.

We conducted a simulation study and presented two data examples to assess the performance of these methods on feature selection, effect estimation and interaction identification using both cross-sectional and time-series designs.

We explore the impact of dispersion effects on location effect estimation and derive approximate joint confidence regions for pairs of correlated location effect estimates.

We use our approach to develop new methods for three statistical tasks: nonparametric quantile regression, conditional average partial effect estimation and heterogeneous treatment effect estimation via instrumental variables.

Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression.

With an increased sample size, BMA becomes favorable in terms of effect estimation and variable selection.

Full details on the indirect effect estimation and the assumptions required for this model are available in the Appendix.

We examine the performance of each approach with a focus on feature selection, effect estimation and detection of main effects and interactions.

Bayesian approaches offer a coherent perspective by formally accounting for uncertainty in model choice, effect estimation, and imputation uncertainty [Stephens and Balding, 2009].

For the pooled assessment of treatment effects in the three comparisons (CPAP versus SMT, NPPV versus SMT and CPAP versus NPPV) and the three outcome variables (need for ETI, mortality and AMI risk) in this review, we used the Mantel-Haenszel method for fixed effects estimation and the DerSimonian and Laird method for random effects estimation.

The key source of the winner's curse is the double use of the same data for both SNP detection and effect estimation.