Results 51 to 60 of about 1,439,158 (354)

A simple remedy for overprecision in judgment [PDF]

open access: yesJudgment and Decision Making, 2010
Overprecision is the most robust type of overconfidence. We present a new method that significantly reduces this bias and offers insight into its underlying cause.
Uriel Haran   +2 more
doaj   +2 more sources

Do metabolic factors increase the risk of thyroid cancer? a Mendelian randomization study

open access: yesFrontiers in Endocrinology, 2023
BackgroundEpidemiological studies emphasize the link between metabolic factors and thyroid cancer. Using Mendelian randomization (MR), we assessed the possible causal impact of metabolic factors on thyroid cancer for the first time.MethodsSummary ...
Weiwei Liang, FangFang Sun
doaj   +1 more source

Brain magnetic resonance imaging predictors in anti‐N‐methyl‐D‐aspartate receptor encephalitis

open access: yesAnnals of Clinical and Translational Neurology, Volume 9, Issue 12, Page 1974-1984, December 2022., 2022
Abstract Objective Brain magnetic resonance imaging (MRI) findings in anti‐N‐methyl‐D‐aspartate receptor (NMDAR) encephalitis are nonspecific and rarely have obvious associations with clinical characteristics and outcomes. This study aimed to comprehensively describe the MRI features of patients with NMDAR encephalitis, examine their associations with ...
Ying‐Ying Zhao   +8 more
wiley   +1 more source

Wild Bootstrap for Fuzzy Regression Discontinuity Designs: Obtaining Robust Bias-Corrected Confidence Intervals

open access: yesEconometrics Journal, 2020
This paper develops a novel wild bootstrap procedure to construct robust bias-corrected valid confidence intervals for fuzzy regression discontinuity designs, providing an intuitive complement to existing robust bias-corrected methods.
Yang He, Otávio Bartalotti
semanticscholar   +1 more source

A Conditional-Heteroskedasticity-Robust Confidence Interval for the Autoregressive Parameter [PDF]

open access: yesReview of Economics and Statistics, 2011
This paper introduces a new confidence interval (CI) for the autoregressive parameter (AR) in an AR(1) model that allows for conditional heteroskedasticity of general form and AR parameters that are less than or equal to unity. The CI is a modification of Mikusheva's (2007a) modification of Stock's (1991) CI that employs the least squares estimator and
Donald W.K. Andrews, Patrik Guggenberger
openaire   +5 more sources

Robust Confidence Intervals for Average Treatment Effects Under Limited Overlap

open access: yesSocial Science Research Network, 2017
Estimators of average treatment effects under unconfounded treatment assignment are known to become rather imprecise if there is limited overlap in the covariate distributions between the treatment groups.
C. Rothe
semanticscholar   +1 more source

Confidence interval based parameter estimation--a new SOCR applet and activity. [PDF]

open access: yesPLoS ONE, 2011
Many scientific investigations depend on obtaining data-driven, accurate, robust and computationally-tractable parameter estimates. In the face of unavoidable intrinsic variability, there are different algorithmic approaches, prior assumptions and ...
Nicolas Christou, Ivo D Dinov
doaj   +1 more source

Correction to: Confidence intervals for robust estimates of measurement uncertainty [PDF]

open access: yesAccreditation and Quality Assurance, 2021
The notations Fp,ν1,ν2 and χ2p,ν in Eqs. (1), (2) and (3)
Peter D. Rostron   +2 more
openaire   +1 more source

Use of the bootstrap in analysing cost data from cluster randomised trials: some simulation results

open access: yesBMC Health Services Research, 2004
Background This work has investigated under what conditions confidence intervals around the differences in mean costs from a cluster RCT are suitable for estimation using a commonly used cluster-adjusted bootstrap in preference to methods that utilise ...
Flynn Terry N, Peters Tim J
doaj   +1 more source

Which Robust Regression Technique Is Appropriate Under Violated Assumptions? A Simulation Study

open access: yesMethodology, 2023
Ordinary least squares (OLS) regression is widely employed for statistical prediction and theoretical explanation in psychology studies. However, OLS regression has a critical drawback: it becomes less accurate in the presence of outliers and non-random ...
Jaejin Kim, Johnson Ching-Hong Li
doaj   +1 more source

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