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A justification of conditional confidence intervals [PDF]
Electronic Journal of Statistics, 2021 To quantify uncertainty around point estimates of conditional objects such as conditional means or variances, parameter uncertainty has to be taken into account. Attempts to incorporate parameter uncertainty are typically based on the unrealistic assumption of observing two independent processes, where one is used for parameter estimation, and the ...
Alexander Heinemann +2 more
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Significant results: statistical or clinical? [PDF]
Korean Journal of Anesthesiology, 2016 The null hypothesis significance test method is popular in biological and medical research. Many researchers have used this method for their research without exact knowledge, though it has both merits and shortcomings. Readers will know its shortcomings,
Sangil Park
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A review of effect sizes and their confidence intervals, Part I: The Cohen's d family
The Quantitative Methods for Psychology, 2018 The results of the interval estimation method popularized by Steiger and Fouladi (1997), herein called the pivoting method1, were displayed erroneously in Appendix C of our recent paper on effect sizes and corresponding confidence intervals (Goulet ...
Jean‐Christophe Goulet‐Pelletier +1 more
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Better Bootstrap Confidence Intervals
, 1987 We consider the problem of setting approximate confidence intervals for a single parameter θ in a multiparameter family. The standard approximate intervals based on maximum likelihood theory, , can be quite misleading.
B. Efron
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L'établissement d'une norme de qualification sûre dans un contexte non paramétrique [PDF]
Tutorials in Quantitative Methods for Psychology, 2008 La sélection de personnel pour un emploi se base souvent sur une norme psychométrique quun candidat doit atteindre afin dêtre recruté. Or, cette norme est statistique, cest-à-dire calculée sur les mesures dun simple échantillon de la population ...
Louis Laurencelle
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Interval-Valued Intuitionistic Fuzzy Confidence Intervals
Journal of Intelligent Systems, 2019 Confidence intervals are useful tools for statistical decision-making purposes. In case of incomplete and vague data, fuzzy confidence intervals can be used for decision making under uncertainty.
Kahraman Cengiz +2 more
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A note on confidence intervals for deblurred images [PDF]
Opuscula Mathematica, 2020 We consider pointwise asymptotic confidence intervals for images that are blurred and observed in additive white noise. This amounts to solving a stochastic inverse problem with a convolution operator.
Michał Biel, Zbigniew Szkutnik
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Using the confidence interval confidently [PDF]
Journal of Thoracic Disease, 2017 Biomedical research is seldom done with entire populations but rather with samples drawn from a population. Although we work with samples, our goal is to describe and draw inferences regarding the underlying population. It is possible to use a sample statistic and estimates of error in the sample to get a fair idea of the population parameter, not as a
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Generating confidence intervals on biological networks
BMC Bioinformatics, 2007 Background In the analysis of networks we frequently require the statistical significance of some network statistic, such as measures of similarity for the properties of interacting nodes. The structure of the network may introduce dependencies among the
Stumpf Michael PH, Thorne Thomas
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Correlation-adjusted standard errors and confidence intervals for within-subject designs: A simple multiplicative approach [PDF]
Tutorials in Quantitative Methods for Psychology, 2019 In within-subject designs, the multiple scores of a given participant are correlated. This correlation implies that the observed variance can be partitioned into between-subject variance and between-measure variance.
Cousineau, Denis
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