The idea of interval estimates

2022 ◽  
pp. 127-133
Author(s):  
Stephen C. Loftus
Keyword(s):  
Interval Estimates

How to Correct for Chance Agreement in the Estimation of Sensitivity and Specificity of Diagnostic Tests

1994 ◽  
Vol 33 (02) ◽  
pp. 180-186 ◽  
Author(s):  
H. Brenner ◽  
O. Gefeller
Keyword(s):  
Sensitivity And Specificity ◽  
Disease Status ◽  
Disease Prevalence ◽  
Small Sample ◽  
Small Samples ◽  
Interval Estimate ◽  
Traditional Concept ◽  
Interval Estimates ◽  
Major Disadvantage ◽  
Test Result

Abstract:The traditional concept of describing the validity of a diagnostic test neglects the presence of chance agreement between test result and true (disease) status. Sensitivity and specificity, as the fundamental measures of validity, can thus only be considered in conjunction with each other to provide an appropriate basis for the evaluation of the capacity of the test to discriminate truly diseased from truly undiseased subjects. In this paper, chance-corrected analogues of sensitivity and specificity are presented as supplemental measures of validity, which pay attention to the problem of chance agreement and offer the opportunity to be interpreted separately. While recent proposals of chance-correction techniques, suggested by several authors in this context, lead to measures which are dependent on disease prevalence, our method does not share this major disadvantage. We discuss the extension of the conventional ROC-curve approach to chance-corrected measures of sensitivity and specificity. Furthermore, point and asymptotic interval estimates of the parameters of interest are derived under different sampling frameworks for validation studies. The small sample behavior of the estimates is investigated in a simulation study, leading to a logarithmic modification of the interval estimate in order to hold the nominal confidence level for small samples.

Neyman-Pearson and Bayes Interval Estimates for Sampling by Atiributes

1984 ◽  
Vol 31 (6) ◽  
pp. 1576-1579
Author(s):  
Robert M. Mason ◽  
John W. Williams ◽  
Paul Ryl
Keyword(s):  
Interval Estimates

0-event: Point and Interval Estimates

2013 ◽  
Vol 1 (2) ◽  
pp. 53-58
Author(s):  
Sergey Gurov
Keyword(s):  
Interval Estimates

scholarly journals Methodological scheme for ranking interval expert estimates of the territories hydrocarbon potential

2019 ◽  
pp. 35-39
Author(s):  
Mykhailo Popov ◽  
Oleksandr Zaitsev ◽  
Iryna Piestova
Keyword(s):  
Hydrocarbon Potential ◽  
Software System ◽  
Interval Estimates ◽  
Numerical Example ◽  
Second Stage ◽  
A Priority ◽  
Two Stages ◽  
Hydrocarbon Deposit ◽  
Interval Valued

The problem of priorities establishing for expert interval-valued estimations when experts hold the opposite opinion is considered. The whole group of expert estimates is subdivided into subgroups, first of which provides the probability of the deposit presence, and the second one provides the probability of deposit missing. A ranking methodology for interval expert estimates of the territories’ hydrocarbon potential, consisting of two stages, is proposed. At the first stage, an estimates formed by two subgroups of experts are separately aggregated by optimization. Two aggregated interval estimates of the corresponding hypotheses probabilities are obtained as a result. In the second stage, a priority estimate is determined by comparing the results. A numerical example of the test territory evaluating for a hydrocarbon deposit presence was calculated. Interval-valued estimates by five experts were used in this example for the hypotheses of hydrocarbons presence/missing. Various metrics of the distance between interval values were used to calculate persistent minima of aggregating estimates. The results of the calculations indicate the hypothesis’ priority of a hydrocarbon deposit presence within the study area. The proposed methodology for ranking interval-valued expert estimates can be used in the “Geologist’s Computer Assistant” software system.The problem of priorities establishing for expert interval-valued estimations when experts hold the opposite opinion is considered. The whole group of expert estimates is subdivided into subgroups, first of which provides the probability of the deposit presence, and the second one provides the probability of deposit missing. A ranking methodology for interval expert estimates of the territories’ hydrocarbon potential, consisting of two stages, is proposed. At the first stage, an estimates formed by two subgroups of experts are separately aggregated by optimization. Two aggregated interval estimates of the corresponding hypotheses probabilities are obtained as a result. In the second stage, a priority estimate is determined by comparing the results. A numerical example of the test territory evaluating for a hydrocarbon deposit presence was calculated. Interval-valued estimates by five experts were used in this example for the hypotheses of hydrocarbons presence/missing. Various metrics of the distance between interval values were used to calculate persistent minima of aggregating estimates. The results of the calculations indicate the hypothesis’ priority of a hydrocarbon deposit presence within the study area. The proposed methodology for ranking interval-valued expert estimates can be used in the “Geologist’s Computer Assistant” software system.

scholarly journals Statistics for N = 1

2020 ◽  
Vol 30 (1) ◽  
pp. e38066
Author(s):  
Jimmie Leppink
Keyword(s):  
Statistical Approach ◽  
Small Samples ◽  
Outcome Variable ◽  
Interval Estimates ◽  
Linear Relations ◽  
Large Samples ◽  
Different Types ◽  
Research In Education ◽  
Study Designs ◽  
Larger Sample

Research in education is often associated with comparing group averages and linear relations in sufficiently large samples and evidence-based practice is about using the outcomes of that research in the practice of education. However, there are questions that are important for the practice of education that cannot really be addressed by comparisons of group averages and linear relations, no matter how large the samples. Besides, different types of constraints including logistic, financial, and ethical ones may make larger-sample research unfeasible or at least questionable. What has remained less known in many fields is that there are study designs and statistical methods for research involving small samples or even individuals that allow us to address questions of importance for the practice of education. This article discusses one type of such situations and provides a simple coherent statistical approach that provides point and interval estimates of differences of interest regardless of the type of the outcome variable and that is of use in other types of studies involving large samples, small samples, and single individuals.

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