Soviet International Law: An Exemplar for Optimal Decision Theory Analysis. By James L. Hildebrand. (Cleveland: Western Reserve Distributors, 1968; Buffalo: William S. Hein & Co., 1969. pp. xx, 130. $3.50, paper.)

1970 ◽  
Vol 64 (1) ◽  
pp. 215-215
Author(s):  
Harold J. Berman
Keyword(s):  
Decision Theory ◽  
International Law ◽  
Optimal Decision ◽  
Theory Analysis ◽  
Optimal Decision Theory ◽  
Western Reserve

EFFECTS OF MARIHUANA ON PAIN AND VERBAL MEMORY: A SENSORY DECISION THEORY ANALYSIS

1979 ◽  
pp. 665-680 ◽  
Author(s):  
W. CRAWFORD CLARK ◽  
RAYMOND R. GOETZ ◽  
RICHARD H. MCCARTHY ◽  
BRENDA BEMPORAD ◽  
PHILLIP ZEIDENBERG
Keyword(s):  
Decision Theory ◽  
Verbal Memory ◽  
Theory Analysis ◽  
Sensory Decision Theory

scholarly journals Classification under uncertainty: data analysis for diagnostic antibody testing

2021 ◽  
Author(s):  
Paul N Patrone ◽  
Anthony J Kearsley
Keyword(s):  
Optimal Decision ◽  
Classification Error ◽  
Operating Characteristics ◽  
Antibody Testing ◽  
Probability Models ◽  
New Strategy ◽  
Serology Test ◽  
Classification Under Uncertainty ◽  
Optimal Decision Theory ◽  
Estimate Prevalence

Abstract Formulating accurate and robust classification strategies is a key challenge of developing diagnostic and antibody tests. Methods that do not explicitly account for disease prevalence and uncertainty therein can lead to significant classification errors. We present a novel method that leverages optimal decision theory to address this problem. As a preliminary step, we develop an analysis that uses an assumed prevalence and conditional probability models of diagnostic measurement outcomes to define optimal (in the sense of minimizing rates of false positives and false negatives) classification domains. Critically, we demonstrate how this strategy can be generalized to a setting in which the prevalence is unknown by either (i) defining a third class of hold-out samples that require further testing or (ii) using an adaptive algorithm to estimate prevalence prior to defining classification domains. We also provide examples for a recently published SARS-CoV-2 serology test and discuss how measurement uncertainty (e.g. associated with instrumentation) can be incorporated into the analysis. We find that our new strategy decreases classification error by up to a decade relative to more traditional methods based on confidence intervals. Moreover, it establishes a theoretical foundation for generalizing techniques such as receiver operating characteristics by connecting them to the broader field of optimization.

Increasing the consumption of dietary fiber: a decision theory analysis

1990 ◽  
Vol 5 (4) ◽  
pp. 409-420 ◽  
Author(s):  
David Brinberg ◽  
Marta L. Axelson
Keyword(s):  
Decision Theory ◽  
Dietary Fiber ◽  
Theory Analysis

Smoking and pain—A method of limits and sensory decision theory analysis

1984 ◽  
Vol 25 (4) ◽  
pp. 289-296 ◽  
Author(s):  
KIM MUSER ◽  
DICK WALLER ◽  
STEN LEVANDER ◽  
DAISY SCHALLING
Keyword(s):  
Decision Theory ◽  
Theory Analysis ◽  
Sensory Decision Theory
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