匹配行为的多层次分析:最大概率和贝叶斯估计的比较
Michael John Ilagan1, Pier-Olivier Caron2, Milica Miočević1
1McGill University, Montréal, QC, Canada.
Journal of the experimental analysis of behavior
|June 16, 2023
概括
这项研究比较了行为分析中的多层模型的最大概率 (ML) 和贝叶斯估计 (BE). ML估计表明在参数恢复和假设测试中表现优异,用于匹配行为研究.
科学领域:
- 行为科学是一种行为科学.
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 考虑主体内部和主体之间的差异对于行为规律至关重要.
- 多级建模越来越多地被提倡用于分析匹配行为.
- 在行为分析中应用多层次建模存在挑战,特别是关于样本大小要求.
研究的目的:
- 为了比较最大概率 (ML) 和贝叶斯估计 (BE) 的参数恢复率和假设拒绝率.
- 在匹配行为研究中评估这些估计方法用于多层模型.
- 为了研究样本大小,每个受试者的测量,灵敏度和随机效应差异的影响.
主要方法:
- 模拟研究,比较ML和BE的多层模型.
- 研究了四个因素:受试者数量,每个受试者的测量,斜率 (灵敏度) 和随机效应差异.
- 评估参数恢复率和假设拒绝率.
主要成果:
- 具有平面前置的ML和BE都显示了固定效应 (截面和斜率) 的可接受的统计性质.
- ML估计通常表现出较少的偏差,较低的根平均平方误差 (RMSE),更高的统计能力和更准确的假阳性率.
- 没有信息的先验 BE 需要更多的信息先验,以便在这种情况下有效使用.
结论:
- 在匹配行为研究中,建议使用ML估计,而不是使用BE估计,用于在匹配行为研究中进行多层次建模.
- 需要进一步的研究来探索在行为分析的贝叶斯多层次建模中信息先验的使用.
相关概念视频
Comparing the Survival Analysis of Two or More Groups
228
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
228
Friedman Two-way Analysis of Variance by Ranks
253
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
253
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
84
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
84
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
587
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
587
Wilcoxon Signed-Ranks Test for Matched Pairs
172
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
172
Expected Frequencies in Goodness-of-Fit Tests
2.6K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
2.6K


