在群体分析中考虑选择偏差：似然内方法与后处理方法的等价性
Accounting for selection biases in population analyses: equivalence of the in-likelihood and post-processing approaches
AI总结：
本文证明群体研究中直接将选择效应纳入似然与先推断观测分布再事后消除选择效应两种方法在适当假设下等价，并澄清后一种方法不会产生所谓的潜在偏差。
摘要：
在本文中，我证明了在适当假设下，群体研究中用于解释选择偏差（也称为选择效应）存在的两种替代方法的等价性：一种是直接将选择效应纳入似然函数；另一种遵循先推断观测分布、再在事后消除选择效应的流程。此外，我研究了据称由后一种方法引发的潜在偏差：我证明该流程若在适当假设下应用，并不会产生上述偏差。
英文摘要：
In this paper I show the equivalence, under appropriate assumptions, of two alternative methods to account for the presence of selection biases (also called selection effects) in population studies: one is to include the selection effects in the likelihood directly; the other follows the procedure of first inferring the observed distribution and then removing selection effects a posteriori. Moreover, I investigate a potential bias allegedly induced by the latter approach: I show that this procedure, if applied under the appropriate assumptions, does not produce the aforementioned bias.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
