数据聚合可以导致贝叶斯线性混合模型和贝叶斯方差分析中的偏见推断
Daniel J Schad1, Bruno Nicenboim2, Shravan Vasishth3
1Institute for Mind, Brain and Behavior, Health and Medical University (HMU).
Psychological methods
|January 25, 2024
概括
使用聚合数据的贝叶斯式零假设测试可以产生偏差的结果,特别是当假设被违反时. 使用贝叶斯线性混合效应模型 (LMMs) 分析非聚合数据,可以提供更准确的贝叶斯因子.
科学领域:
- 认知科学 认知科学
- 统计 统计 统计 统计
- 心理学 心理学 心理学
背景情况:
- 贝叶斯线性混合效应模型 (LMMs) 和贝叶斯方差分析 (ANOVA) 在认知科学中常见于零假设测试.
- 贝叶斯因子软件是可访问的,但正确的数据和模型规格仍然不清楚.
- 许多研究人员将数据按主体汇总为贝叶斯分析.
研究的目的:
- 为了证明在贝叶斯分析中对聚合数据进行零假设测试的问题.
- 评估违反假设对贝叶斯因子结果的影响.
- 为准确的贝叶斯推理提供建议.
主要方法:
- 基于模拟的校准用于模型推断.
- 适用于几个实验设计的例子.
- 对聚合数据与非聚合数据的分析进行比较.
主要成果:
- 对聚合数据的零假设测试在贝叶斯分析中可能存在问题,反映了频率主义方法中的问题.
- 当球性被侵犯时,根据对比差异差异,聚合数据上的贝叶斯因子过于保守或过于自由.
- 在聚合数据分析中忽略随机项的斜率变异导致偏向的 (过于自由的) 贝叶斯因子.
结论:
- 对于贝叶斯式零假设测试,按主体汇总数据可能会导致有偏见的贝叶斯因子.
- 对非聚合 (个体试验) 数据的贝叶斯式LMM,与随机效应的明确建模,绕过这些问题.
- 准确的贝叶斯推理需要仔细的数据和模型规范,尽可能避免数据聚合.
相关概念视频
Bias
4.2K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
4.2K
Bias in Epidemiological Studies
271
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
271
Biostatistics: Overview
244
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
Discrete variables are...
244
Statistical Methods to Analyze Parametric Data: ANOVA
374
Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
374
One-Way ANOVA
7.9K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
7.9K
One-Way ANOVA: Equal Sample Sizes
3.3K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.3K


