Robust Resampling Confidence Intervals for Empirical Variograms
Mathematical Geosciences, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Clark, Robert Graham +1 more
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Do metabolic factors increase the risk of thyroid cancer? a Mendelian randomization study
Frontiers 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
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Use of the bootstrap in analysing cost data from cluster randomised trials: some simulation results
BMC 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
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Robust Confidence Intervals for Effect Size in the Two-Group Case [PDF]
, 2005 The probability coverage of intervals involving robust estimates of effect size based on seven procedures was compared for asymmetrically trimming data in an independent two-groups design, and a method that symmetrically trims the data.
Algina, James +2 more
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Robust nonparametric confidence intervals for regression-discontinuity designs [PDF]
Econometrica, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Calonico, Sebastian +2 more
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Globally Robust Confidence Intervals for Location
Dhaka University Journal of Science, 2012 Classical inference considers sampling variability to be the only source of uncertainty, and does not address the issue of bias caused by contamination. Naive robust intervals replace the classical estimates by their robust counterparts without considering the possible bias of the robust point estimates. Consequently, the asymptotic coverage proportion
Jafar A Khan, M Ershadul Haque
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Confidence interval based parameter estimation--a new SOCR applet and activity. [PDF]
PLoS 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
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Confidence intervals for robust estimates of measurement uncertainty [PDF]
Accreditation and Quality Assurance, 2020 AbstractUncertainties arising at different stages of a measurement process can be estimated using analysis of variance (ANOVA) on duplicated measurements. In some cases, it is also desirable to calculate confidence intervals for these uncertainties. This can be achieved using probability models that assume the measurement data are normally distributed.
Peter D. Rostron +2 more
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A simple remedy for overprecision in judgment [PDF]
Judgment 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
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Which Robust Regression Technique Is Appropriate Under Violated Assumptions? A Simulation Study
Methodology, 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
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