scholarly journals Asymmetric Loss Functions and Sample Size Determination: A Bayesian Approach

2016 ◽  
Vol 35 (1) ◽  
Author(s):  
Hans Peter Stüger
Keyword(s):  
Public Health ◽  
Bayesian Approach ◽  
Loss Functions ◽  
Sample Size Determination ◽  
Diagnostic Classification ◽  
Monitoring Systems ◽  
Size Determination ◽  
Asymmetric Loss ◽  
Optimal Sample ◽  
Binomial Parameter

In designing monitoring systems for public health tasks it can be important to give different weights to the cases of under- and overestimation of a binomial parameter. We show how asymmetric loss functions can be used for this aim. Bayesian interval-based approaches can be combined with these loss functions and with prior knowledge about diagnostic classification errors to determine optimal sample sizes.

scholarly journals Bayesian approach for sample size determination, illustrated with Soil Health Card data of Andhra Pradesh (India)

Geoderma ◽  
2022 ◽  
Vol 405 ◽  
pp. 115396
Author(s):  
D.J. Brus ◽  
B. Kempen ◽  
D. Rossiter ◽  
Balwinder-Singh ◽  
A.J. McDonald
Keyword(s):  
Sample Size ◽  
Bayesian Approach ◽  
Andhra Pradesh ◽  
Soil Health ◽  
Sample Size Determination ◽  
Size Determination ◽  
Health Card

A Bayesian approach for sample size determination in method comparison studies

2008 ◽  
Vol 27 (13) ◽  
pp. 2273-2289 ◽  
Author(s):  
Kunshan Yin ◽  
Pankaj K. Choudhary ◽  
Diana Varghese ◽  
Steven R. Goodman
Keyword(s):  
Sample Size ◽  
Bayesian Approach ◽  
Method Comparison ◽  
Sample Size Determination ◽  
Size Determination

scholarly journals Estimating the parameters of Azzalini model by Bayesian approach under symmetric and asymmetric loss functions

2021 ◽  
Vol 16 (3) ◽  
pp. 567-592
Author(s):  
Janardan Mahanta ◽  
Soma Chowdhury Biswas ◽  
Manindra Kumar Roy
Keyword(s):  
Bayesian Approach ◽  
Loss Functions ◽  
Asymmetric Loss

Value of Information and Hypothesis Testing Approaches for Sample Size Determination in Engineering Component Inspection: A Comparison

2016 ◽  
Author(s):  
Eishiro Higo ◽  
Mahesh D. Pandey
Keyword(s):  
Sample Size ◽  
Value Of Information ◽  
Nuclear Power ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Sample Size Determination ◽  
Size Determination ◽  
Benefit Analysis ◽  
Current State ◽  
Optimal Sample

A sample size determination method is developed for a two-action problem that represents a component maintenance scenario requiring current state estimation. For safety and generation efficiency, each component of a nuclear power plant must be regularly inspected. In terms of safety, the larger the sample size inspected, the less the uncertainty about current and future states of the components; however, such inspections are expensive. Thus, sample size determination becomes an important problem. A key idea for solving this problem is the Value of Information (VoI) and its derivation: the Expected Net Gain of Sampling (ENGS). The ENGS is a function of sample size and represents by how much a decision maker benefits from the observed data. By maximizing the ENGS, the optimal sample size is determined in terms of cost-benefit analysis.

scholarly journals Optimal Sample Size for the Birnbaum–Saunders Distribution under Decision Theory with Symmetric and Asymmetric Loss Functions

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 926
Author(s):  
Eliardo Costa ◽  
Manoel Santos-Neto ◽  
Víctor Leiva
Keyword(s):  
Sample Size ◽  
Real Data ◽  
Loss Functions ◽  
Theoretic Approach ◽  
Optimal Sample Size ◽  
Asymmetric Loss ◽  
Optimal Sample ◽  
Decision Theoretic Approach ◽  
Potential Applications ◽  
Asymmetrical Model

The fatigue-life or Birnbaum–Saunders distribution is an asymmetrical model that has been widely applied in several areas of science and mainly in reliability. Although diverse methodologies related to this distribution have been proposed, the problem of determining the optimal sample size when estimating its mean has not yet been studied. In this paper, we derive a methodology to determine the optimal sample size under a decision-theoretic approach. In this approach, we consider symmetric and asymmetric loss functions for point and interval inference. Computational tools in the R language were implemented to use this methodology in practice. An illustrative example with real data is also provided to show potential applications.

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