Sample Size Determination to Detect Cusp Catastrophe in Stochastic Cusp Catastrophe Model: A Monte-Carlo Simulation-Based Approach

2014 ◽  
pp. 35-41 ◽  
Author(s):  
Ding-Geng(Din) Chen ◽  
Xinguang Chen ◽  
Wan Tang ◽  
Feng Lin
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Sample Size ◽  
Sample Size Determination ◽  
Cusp Catastrophe ◽  
Size Determination ◽  
Catastrophe Model ◽  
Cusp Catastrophe Model ◽  
Simulation Based

scholarly journals Efficient and flexible simulation-based sample size determination for clinical trials with multiple design parameters

2020 ◽  
pp. 096228022097579
Author(s):  
Duncan T Wilson ◽  
Richard Hooper ◽  
Julia Brown ◽  
Amanda J Farrin ◽  
Rebecca EA Walwyn
Keyword(s):  
Sample Size ◽  
Computer Experiments ◽  
Small Sample ◽  
Sample Size Determination ◽  
Design Parameters ◽  
Size Determination ◽  
Parametric Regression ◽  
Software Packages ◽  
Primary Endpoints ◽  
Simulation Based

Simulation offers a simple and flexible way to estimate the power of a clinical trial when analytic formulae are not available. The computational burden of using simulation has, however, restricted its application to only the simplest of sample size determination problems, often minimising a single parameter (the overall sample size) subject to power being above a target level. We describe a general framework for solving simulation-based sample size determination problems with several design parameters over which to optimise and several conflicting criteria to be minimised. The method is based on an established global optimisation algorithm widely used in the design and analysis of computer experiments, using a non-parametric regression model as an approximation of the true underlying power function. The method is flexible, can be used for almost any problem for which power can be estimated using simulation, and can be implemented using existing statistical software packages. We illustrate its application to a sample size determination problem involving complex clustering structures, two primary endpoints and small sample considerations.

scholarly journals Sample size determination for Bayesian hierarchical models commonly used in psycholinguistics

2021 ◽  
Author(s):  
Shravan Vasishth ◽  
Himanshu Yadav ◽  
Daniel Schad ◽  
Bruno Nicenboim
Keyword(s):  
Sample Size ◽  
Hierarchical Models ◽  
Bayes Factor ◽  
Sample Size Determination ◽  
Size Determination ◽  
Bayesian Hierarchical ◽  
Bayesian Data Analysis ◽  
Simulation Based ◽  
Bayesian Sample Size Determination ◽  
Bayesian Sample Size

Although Bayesian data analysis has the great advantage that one need not specify the sample size in advance of running an experiment, there are nevertheless situations where it becomes necessary to have at least an initial ballpark estimate for a target sample size. An example where this becomes necessary is grant applications. In this paper, we adapt a simulation-based method proposed by Wang and Gelfand, 2002 (A simulation-based approach to Bayesian sample size determination for performance under a given model and for separating models. Statistical Science, 193-208) for a Bayes-factor based design analysis. We demonstrate how relatively complex hierarchical models (which are commonly used in psycholinguistics) can be used to determine approximate sample sizes for planning experiments. The code is available for researchers to adapt for their own purposes and applications at https://osf.io/hjgrm/.

Sample Size Determination in NT-Net Quasi-Monte Carlo Simulation

2013 ◽  
Vol 13 (3) ◽  
Author(s):  
Wenzhen Huang
Keyword(s):  
Monte Carlo ◽  
Sample Size ◽  
Tolerance Analysis ◽  
Sample Size Determination ◽  
Combinatorial Theory ◽  
Size Determination ◽  
Quasi Monte Carlo ◽  
Computation Efficiency ◽  
Analysis And Synthesis ◽  
Net Method

Monte Carlo (MC) technique prevails in probabilistic design simulation, such as in statistical tolerance analysis and synthesis. A quasi-Monte Carlo (e.g., number theoretic net method (NT-net)) with better computation efficiency over MC has recently attracted interests in application. In spite of a comprehensive case study (Huang et al., 2004, “Tolerance Analysis for Design of Multistage Manufacturing Processes Using Number-Theoretical Net Method (NT-net),” Int. J. Flexible Manuf. Syst., 16, pp. 65–90 and Zhou et al., 2001, “Sequential Algorithm Based on Number Theoretic Method for Tolerance Analysis and Synthesis,” ASME J. Manuf. Sci. Eng., 123(3), pp. 490–493) for comparison between NT-net and MC, a method for sample size determination of NT-net is still not available. Combinatorial theory and the solution of occupancy problem are used for estimating equivalent sample sizes of MC and NT-net, allowing the NT-net sample size determination in application. A multivariate Chebyshev polynomial with variant coefficients is used to represent generic design functions for validation. The results are verified by case studies.

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