AI 中文总结
研究一般统计假设检验中内在贝叶斯因子和最不利内在先验的界，通过得出随样本量自动调整的内在贝叶斯因子下界及引入新思想，在内在贝叶斯因子与相关方法间架桥，解决假设检验中P值和贝叶斯因子的问题。
AI 中文摘要
假设检验是统计方法中最具争议性的程序。P值过于轻易地拒绝原假设，尤其对于大样本。另一方面，贝叶斯因子依赖于假设，比如关于内在贝叶斯因子，采用哪种平均方式？算术、几何、中位数？我们的界是所有平均方式中的下确界。我们得出了随样本量自动调整的内在贝叶斯因子的下界。此外，我们引入了“最不利内在先验”的新思想，它对应于最不利的训练样本。该界在内在贝叶斯因子与阿德里安·史密斯和大卫·斯皮格尔哈特方法之间架起了桥梁。
英文摘要
Hypothesis Testing is the most contentious procedure in statistical Methodology. P values rejects Null Hypotheses far too easily, specially for large samples. On the other hand, Bayes Factors depends on assumptions, for example regarding Intrinsic Bayes Factors, which average? Arithmetic, Geometric, Median? Our bound is the infimum over all the averages. We develop a lower bound on Intrinsic Bayes Factors that adjust authomatically with the sample size. Furthermore, we introduce the new idea of {\it{\textbf{Least Favorable Intrinsic Prior}}}, which corresponds to the least favourable possible training samples. The bound sets a bridge between Intrinsic Bayes Factors and Adrian Smith and David Spiegelhalter methodology.
Comments21 pages, 3 figures
