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引用次数: 0
摘要
尽管蒙特卡罗模拟实验广受欢迎且具有灵活性,但关于如何利用蒙特卡罗模拟实验优化解决特定未知变量的问题依然存在。本文回顾了两种常用方法,一种是在目标函数有噪声的情况下进行确定性迭代搜索,另一种是根据拟合代用函数构建插值估计值,强调了这两种方法的低效性和推理问题。为了解决这些局限性,并填补现有蒙特卡罗实验方法的空白,我们提出了一种名为 "带有支撑和插值的概率分段算法"(ProBABLI)的新算法,旨在为蒙特卡罗模拟研究中发现的随机根方程提供高效、一致和无偏的估计值(以及相关的置信区间)。在给定目标幂率、精度标准和包含先验信念的预期幂函数的情况下,ProBABLI 方法通过独立样本 t 检验和结构方程模型的实际样本量规划应用证明了其特性。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
Solving variables with Monte Carlo simulation experiments: A stochastic root-solving approach.
Despite their popularity and flexibility, questions remain regarding how to optimally solve particular unknown variables of interest using Monte Carlo simulation experiments. This article reviews two common approaches based on either performing deterministic iterative searches with noisy objective functions or by constructing interpolation estimates given fitted surrogate functions, highlighting the inefficiencies and inferential concerns of both methods. To address these limitations, and to fill a gap in existing Monte Carlo experimental methodology, a novel algorithm termed the probabilistic bisection algorithm with bolstering and interpolations (ProBABLI) is presented with the goal providing efficient, consistent, and unbiased estimates (with associated confidence intervals) for the stochastic root equations found in Monte Carlo simulation research. Properties of the ProBABLI approach are demonstrated using practical sample size planning applications for independent samples t tests and structural equation models given target power rates, precision criteria, and expected power functions that incorporate prior beliefs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
期刊介绍:
Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.