Robust confidence intervals
View article: The deleterious effect of deleting <i>ompC</i> in <i>envZ</i>*<sub>L116P</sub> is reverted by integrating an extra <i>ompF</i> gene copy (<i>ompF’</i>) at the original <i>ompC</i> locus.
The deleterious effect of deleting <i>ompC</i> in <i>envZ</i>*<sub>L116P</sub> is reverted by integrating an extra <i>ompF</i> gene copy (<i>ompF’</i>) at the original <i>ompC</i> locus. Open
(A) The relative ion intensities (or counts), defined as the normalized label-free quantification (LFQ) values of each porin (for each replicate), as a proxy for relative porin abundance. Yellow represents OmpC whereas purple indica…
View article: Confidence about inference correctness.
Confidence about inference correctness. Open
(A) Participants’ confidence about the task states inference. Data plotted separately for correct and error trials, highlighting the typical signature of decision confidence, with higher confidence in correct trials compared to erro…
View article: Five independent runs 95% confidence interval (Root Mean Square Error).
Five independent runs 95% confidence interval (Root Mean Square Error). Open
Five independent runs 95% confidence interval (Root Mean Square Error).
View article: Sentiment Distribution with 95 Percent Confidence Intervals.
Sentiment Distribution with 95 Percent Confidence Intervals. Open
Sentiment Distribution with 95 Percent Confidence Intervals.
View article: Confidence regions for weighted quantiles
Confidence regions for weighted quantiles Open
Quantiles are fundamental tools in statistics and risk analysis. While asymptotic and finite-sample results for standard empirical quantiles are well established, analogous results for weighted quantiles remain scarce. In this paper, we e…
View article: Forest plot of global prevalence of e-cigarette with the height of the diamond is the overall effect size (22.65% while the width is the confidence interval at (95% CI: 18.32, 26.92),The y-axis shows the standard error of each study while the x-axis the estimate of effect size of the each study.
Forest plot of global prevalence of e-cigarette with the height of the diamond is the overall effect size (22.65% while the width is the confidence interval at (95% CI: 18.32, 26.92),The y-axis shows the standard error of each study while the x-axis the estimate of effect size of the each study. Open
The verticalline denotes the no effect. The box represents the effect size of each study and the line across the box is confidence interval of each study.
View article: Inter-reliability results represented as Kappa values and Confidence Intervals for individual matches.
Inter-reliability results represented as Kappa values and Confidence Intervals for individual matches. Open
Inter-reliability results represented as Kappa values and Confidence Intervals for individual matches.
View article: Intra-reliability results represented Kappa values and Confidence Intervals for the three-match blocks.
Intra-reliability results represented Kappa values and Confidence Intervals for the three-match blocks. Open
Intra-reliability results represented Kappa values and Confidence Intervals for the three-match blocks.
View article: Intra-reliability results represented as Kappa values and Confidence Intervals for individual matches.
Intra-reliability results represented as Kappa values and Confidence Intervals for individual matches. Open
Intra-reliability results represented as Kappa values and Confidence Intervals for individual matches.
View article: Inter-reliability results represented as Kappa values and Confidence Intervals for the blocks of matches.
Inter-reliability results represented as Kappa values and Confidence Intervals for the blocks of matches. Open
Inter-reliability results represented as Kappa values and Confidence Intervals for the blocks of matches.
View article: ClimLoco1.0: CLimate variable confidence Interval of Multivariate Linear Observational COnstraint
ClimLoco1.0: CLimate variable confidence Interval of Multivariate Linear Observational COnstraint Open
Projections of future climate are key to society's adaptation and mitigation plans in response to climate change. Numerical climate models provide projections, but the large dispersion between them makes future climate very uncertain. To r…
View article: Robust and Adaptive Confidence Intervals with Finite-Sample Validity
Robust and Adaptive Confidence Intervals with Finite-Sample Validity Open
This paper introduces a novel framework for constructing confidence intervals that possess guaranteed finite-sample coverage probability without relying on asymptotic approximations or specific distributional assumptions. Traditional metho…
View article: Robust and Adaptive Confidence Intervals with Finite-Sample Validity
Robust and Adaptive Confidence Intervals with Finite-Sample Validity Open
This paper introduces a novel framework for constructing confidence intervals that possess guaranteed finite-sample coverage probability without relying on asymptotic approximations or specific distributional assumptions. Traditional metho…
View article: Means, standard deviations and estimated values for a 95% confidence interval for Task 3 (tooth numbering) obtained for each architecture.
Means, standard deviations and estimated values for a 95% confidence interval for Task 3 (tooth numbering) obtained for each architecture. Open
Means, standard deviations and estimated values for a 95% confidence interval for Task 3 (tooth numbering) obtained for each architecture.
View article: Evaluating Variance Estimates with Relative Efficiency
Evaluating Variance Estimates with Relative Efficiency Open
Experimentation platforms in industry must often deal with customer trust issues. Platforms must prove the validity of their claims as well as catch issues that arise. As a central quantity estimated by experimentation platforms, the valid…
View article: Evaluating Variance Estimates with Relative Efficiency
Evaluating Variance Estimates with Relative Efficiency Open
Experimentation platforms in industry must often deal with customer trust issues. Platforms must prove the validity of their claims as well as catch issues that arise. As a central quantity estimated by experimentation platforms, the valid…
View article: Large-scale model comparison with fast model confidence sets
Large-scale model comparison with fast model confidence sets Open
View article: THE LOCAL PROJECTION RESIDUAL BOOTSTRAP FOR AR(1) MODELS
THE LOCAL PROJECTION RESIDUAL BOOTSTRAP FOR AR(1) MODELS Open
This article proposes a local projection (LP) residual bootstrap method to construct confidence intervals for impulse response coefficients of AR(1) models. Our bootstrap method is based on the LP approach and involves a residual bootstrap…
View article: Shortest fixed-width confidence intervals for a bounded parameter: The Push algorithm
Shortest fixed-width confidence intervals for a bounded parameter: The Push algorithm Open
We present a method for computing optimal fixed-width confidence intervals for a single, bounded parameter, extending a method for the binomial due to Asparaouhov and Lorden, who called it the Push algorithm. The method produces the shorte…
View article: Shortest fixed-width confidence intervals for a bounded parameter: The Push algorithm
Shortest fixed-width confidence intervals for a bounded parameter: The Push algorithm Open
We present a method for computing optimal fixed-width confidence intervals for a single, bounded parameter, extending a method for the binomial due to Asparaouhov and Lorden, who called it the Push algorithm. The method produces the shorte…
View article: Sample size determination for hypothesis testing on the intraclass correlation coefficient in a two‐way analysis of variance model
Sample size determination for hypothesis testing on the intraclass correlation coefficient in a two‐way analysis of variance model Open
Reliability evaluation is critical in fields such as psychology and medicine to ensure accurate diagnosis and effective treatment management. When participants are evaluated by the same raters, a two‐way ANOVA model is suitable to model th…
View article: Robust and Adaptive Confidence Intervals with Finite-Sample Validity
Robust and Adaptive Confidence Intervals with Finite-Sample Validity Open
This paper introduces a novel framework for constructing confidence intervals that possess guaranteed finite-sample coverage probability without relying on asymptotic approximations or specific distributional assumptions. Traditional metho…
View article: Predicted probability of following each informant.
Predicted probability of following each informant. Open
Predicted probability of participants in each condition following the evidence provided by each informant. Results are averaged over the effects of action side. Confidence intervals (95%) are shown for each informant-condition combination.…
View article: Rank comparison of calculated ‘R’ with confidence intervals of survey-based ranks calculated with the Cusanus-Borda scoring method.
Rank comparison of calculated ‘R’ with confidence intervals of survey-based ranks calculated with the Cusanus-Borda scoring method. Open
Rank comparison of calculated ‘R’ with confidence intervals of survey-based ranks calculated with the Cusanus-Borda scoring method.
View article: DoY estimation error and number of empty species estimates as a function of the confidence threshold.
DoY estimation error and number of empty species estimates as a function of the confidence threshold. Open
DoY estimation error and number of empty species estimates as a function of the confidence threshold.
View article: Analyzing meteorological data with bootstrap-based confidence intervals for Poisson-Rani distribution parameter estimation
Analyzing meteorological data with bootstrap-based confidence intervals for Poisson-Rani distribution parameter estimation Open
The Poisson distribution is commonly used when events are assumed to be independent and occur at a consistent rate. This may not be generally applicable, and the Poisson distribution is not appropriate in situations where the underlying ra…
View article: Confidence Intervals for Linear Models with Arbitrary Noise Contamination
Confidence Intervals for Linear Models with Arbitrary Noise Contamination Open
We study confidence interval construction for linear regression under Huber's contamination model, where an unknown fraction of noise variables is arbitrarily corrupted. While robust point estimation in this setting is well understood, sta…
View article: Confidence Intervals for Linear Models with Arbitrary Noise Contamination
Confidence Intervals for Linear Models with Arbitrary Noise Contamination Open
We study confidence interval construction for linear regression under Huber's contamination model, where an unknown fraction of noise variables is arbitrarily corrupted. While robust point estimation in this setting is well understood, sta…
View article: Confidence Intervals Based on the Modified Chi-Squared Distribution and its Applications in Medicine
Confidence Intervals Based on the Modified Chi-Squared Distribution and its Applications in Medicine Open
Small sample sizes in clinical studies arises from factors such as reduced costs, limited subject availability, and the rarity of studied conditions. This creates challenges for accurately calculating confidence intervals (CIs) using the n…
View article: A New Approach to the Nonparametric Behrens–Fisher Problem With Compatible Confidence Intervals
A New Approach to the Nonparametric Behrens–Fisher Problem With Compatible Confidence Intervals Open
We propose a new method to address the nonparametric Behrens–Fisher problem, allowing for unequal distribution functions across the two samples. The procedure tests the null hypothesis , where denotes the Mann–Whitney effect. Apart from th…