scholarly journals MISS: finding optimal sample sizes for approximate analytics

2021 ◽  
Author(s):  
Xuebin Su ◽  
Hongzhi Wang
Keyword(s):  
Sample Sizes ◽  
Optimal Sample

scholarly journals Optimal sample sizes for group testing in two-stage sampling

2014 ◽  
Vol 25 (01) ◽  
pp. 12-28 ◽  
Author(s):  
Osval Antonio Montesinos-López ◽  
Kent Eskridge ◽  
Abelardo Montesinos-López ◽  
José Crossa
Keyword(s):  
Group Testing ◽  
Sample Sizes ◽  
Two Stage ◽  
Optimal Sample

scholarly journals Dimensions of Design Space: A Decision-Theoretic Approach to Optimal Research Design

2009 ◽  
Vol 29 (6) ◽  
pp. 643-660 ◽  
Author(s):  
Stefano Conti ◽  
Karl Claxton
Keyword(s):  
Design Space ◽  
Treatment Options ◽  
Simultaneous Estimation ◽  
Theoretic Approach ◽  
Bayesian Decision ◽  
Sample Sizes ◽  
Net Benefit ◽  
Clinical Endpoints ◽  
Optimal Sample ◽  
Different Types

Bayesian decision theory can be used not only to establish the optimal sample size and its allocation in a single clinical study but also to identify an optimal portfolio of research combining different types of study design. Within a single study, the highest societal payoff to proposed research is achieved when its sample sizes and allocation between available treatment options are chosen to maximize the expected net benefit of sampling (ENBS). Where a number of different types of study informing different parameters in the decision problem could be conducted, the simultaneous estimation of ENBS across all dimensions of the design space is required to identify the optimal sample sizes and allocations within such a research portfolio. This is illustrated through a simple example of a decision model of zanamivir for the treatment of influenza. The possible study designs include: 1) a single trial of all the parameters, 2) a clinical trial providing evidence only on clinical endpoints, 3) an epidemiological study of natural history of disease, and 4) a survey of quality of life. The possible combinations, samples sizes, and allocation between trial arms are evaluated over a range of cost-effectiveness thresholds. The computational challenges are addressed by implementing optimization algorithms to search the ENBS surface more efficiently over such large dimensions.

Optimal sample sizes for comparison of proportions using FBST

2012 ◽  
Author(s):  
Victor Fossaluza ◽  
Patricia Viana da Silva
Keyword(s):  
Sample Sizes ◽  
Optimal Sample

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.

Bootstrap-based Budget Allocation for Nested Simulation

2021 ◽  
Author(s):  
Kun Zhang ◽  
Guangwu Liu ◽  
Shiyu Wang
Keyword(s):  
Confidence Intervals ◽  
Financial Risk ◽  
Budget Allocation ◽  
Risk Measurement ◽  
Allocation Rule ◽  
Sample Sizes ◽  
Bootstrap Sampling ◽  
Asymptotically Optimal ◽  
Optimal Sample ◽  
Asymptotic Validity

Nested simulation (also referred to as two-level simulation) finds a variety of applications such as financial risk measurement, and a central issue of nested simulation is how to allocate a finite amount of simulation budget to achieve the highest accuracy. In “Bootstrap-based Budget Allocation for Nested Simulation”, Zhang, Liu, and Wang propose a bootstrap-based rule for simulation budget allocation for nested simulation. By utilizing the asymptotically optimal inner- and outer-level sample sizes that are typically unknown, the proposed method employs bootstrap sampling on a small amount of initial samples to estimate the unknown optimal sample sizes, thus providing a reasonably good allocation rule for the main simulation. An allocation rule to ensure the asymptotic validity of confidence intervals is also given.

Sign in / Sign up

Export Citation Format

Share Document