在复杂的调查采样下对合的概率估计和有限信息的适合性测试统计数据对二元因素分析模型进行复杂调查采样
Haziq Jamil1,2, Irini Moustaki2, Chris Skinner2
1Universiti Brunei Darussalam, Gadong, Brunei Darussalam.
The British journal of mathematical and statistical psychology
|October 12, 2024
概括
本研究介绍了在因子模型中分析二进制数据的改进方法,提高了复杂调查数据的准确性. 开发和验证新的统计测试,以在各种采样场景中获得更好的性能.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学 计量经济学
- 心理测量 心理测量 心理测量
背景情况:
- 因子模型被广泛用于分析数据中的潜在结构.
- 传统的方法经常与二进制结果和复杂的调查设计作斗争.
- 配对概率估计为此类数据提供了一种灵活的方法.
研究的目的:
- 将二进制数据的因子模型的双对概率估计扩展到复杂的抽样.
- 为这些模型引入和评估有限信息的合适性测试 (皮尔森基平方,沃尔德).
- 提高这些统计测试的计算效率.
主要方法:
- 适应复杂的调查设计的双对概率估计.
- 开发修改的皮尔森基平方和沃尔德测试统计数据.
- 使用简单随机抽样和不平等概率抽样进行模拟研究.
主要成果:
- 提出的方法有效地处理复杂采样下的二进制数据的因子模型.
- 修改后的测试统计数据显示,计算效率有所提高.
- 在不同的采样方案下,估计和测试表现良好.
结论:
- 配对概率估计和新的合适性测试是对二进制数据的因子分析有价值的工具,特别是在复杂的调查数据中.
- 改进的方法在统计建模和分析方面提供了实用优势.
- 该研究验证了在各种采样设计中提出的技术的稳定性.
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