Optimal sample size allocation for multi-level stress testing with Weibull regression under Type-II censoring

Statistics ◽  
2011 ◽  
Vol 45 (3) ◽  
pp. 257-279 ◽  
Author(s):  
C. Y. Ka ◽  
P. S. Chan ◽  
H. K.T. Ng ◽  
N. Balakrishnan
Keyword(s):  
Sample Size ◽  
Stress Testing ◽  
Type Ii ◽  
Optimal Sample Size ◽  
Weibull Regression ◽  
Type Ii Censoring ◽  
Optimal Sample ◽  
Sample Size Allocation ◽  
Multi Level

scholarly journals Optimal Sample Size Based on Cost Function in Progressively Type II Censoring

2020 ◽  
Vol 14 (1) ◽  
pp. 41-54
Author(s):  
Elham Basiri ◽  
Seyed Mahdi Salehi ◽  
◽  
Keyword(s):  
Cost Function ◽  
Sample Size ◽  
Type Ii ◽  
Optimal Sample Size ◽  
Type Ii Censoring ◽  
Optimal Sample

Estimation of optimum sample size allocation: An illustration with body mass index for evaluating the effect of a dietetic supplement

2019 ◽  
Vol 12 (08) ◽  
pp. 1950086
Author(s):  
Carlos N. Bouza-Herrera ◽  
Sira M. Allende-Alonso ◽  
Gajendra K. Vishwakarma ◽  
Neha Singh
Keyword(s):  
Body Mass Index ◽  
Numerical Methods ◽  
Sample Size ◽  
Body Mass ◽  
Optimal Allocation ◽  
Computing Time ◽  
Optimal Sample Size ◽  
Optimum Sample Size ◽  
Optimal Sample ◽  
Sample Size Allocation

In many medical researches, it is needed to determine the optimal sample size allocation in a heterogeneous population. This paper proposes the algorithm for optimal sample size allocation. We consider the optimal allocation problem as an optimization problem and the solution is obtained by using Bisection, Secant, Regula–Falsi and other numerical methods. The performance of the algorithm for different numerical methods are analyzed and evaluated in terms of computing time, number of iterations and gain in accuracy using stratification. The efficacy of algorithm is evaluated for the response in terms of body mass index (BMI) to the dietetic supplement with diabetes mellitus, HIV/AIDS and cancer post-operatory recovery patients.

scholarly journals Multiple sensitive estimation and optimal sample size allocation in the item sum technique

2017 ◽  
Vol 60 (1) ◽  
pp. 155-173 ◽  
Author(s):  
Pier Francesco Perri ◽  
María del Mar Rueda García ◽  
Beatriz Cobo Rodríguez
Keyword(s):  
Sample Size ◽  
Optimal Sample Size ◽  
Optimal Sample ◽  
Sample Size Allocation

scholarly journals Optimal sample size calculation for null hypothesis significance tests

2019 ◽  
Author(s):  
Joseph F. Mudge ◽  
Jeffrey E. Houlahan
Keyword(s):  
Sample Size ◽  
Estimation Methods ◽  
Sample Size Estimation ◽  
Type I ◽  
Type Ii ◽  
Size Estimation ◽  
Sample Sizes ◽  
Optimal Sample Size ◽  
Critical Effect ◽  
Optimal Sample

AbstractTraditional study design tools for estimating appropriate sample sizes are not consistently used in ecology and can lead to low statistical power to detect biologically relevant effects. We have developed a new approach to estimating optimal sample sizes, requiring only three parameters; a maximum acceptable average of α and β, a critical effect size of minimum biological relevance, and an estimate of the relative costs of Type I vs. Type II errors.This approach can be used to show the general circumstances under which different combinations of critical effect sizes and maximum acceptable combinations of α and β are attainable for different statistical tests. The optimal α sample size estimation approach can require fewer samples than traditional sample size estimation methods when costs of Type I and II errors are assumed to be equal but recommends comparatively more samples for increasingly unequal Type I vs. Type II errors costs. When sampling costs and absolute costs of Type I and II errors are known, optimal sample size estimation can be used to determine the smallest sample size at which the cost of an additional sample outweighs its associated reduction in errors. Optimal sample size estimation constitutes a more flexible and intuitive tool than traditional sample size estimation approaches, given the constraints and unknowns commonly faced by ecologists during study.

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