Shute N C, Ewens W J
Department of Mathematics, Monash University, Clayton, Victoria, Australia.
Am J Hum Genet. 1988 Oct;43(4):374-86.
The ascertainment problem arises when families are sampled by a nonrandom process and some assumption about this sampling process must be made in order to estimate genetic parameters. Under classical ascertainment assumptions, estimation of genetic parameters cannot be separated from estimation of the parameters of the ascertainment process, so that any misspecification of the ascertainment process causes biases in estimation of the genetic parameters. Ewens and Shute proposed a resolution to this problem, involving conditioning the likelihood of the sample on the part of the data which is "relevant to ascertainment." The usefulness of this approach can only be assessed by examining the properties (in particular, bias and standard error) of the estimates which arise by using it for a wide range of parameter values and family size distributions and then comparing these biases and standard errors with those arising under classical ascertainment procedures. These comparisons are carried out in the present paper, and we also compare the proposed method with procedures which condition on, or ignore, parts of the data.
当通过非随机过程对家庭进行抽样时，就会出现确定问题，并且为了估计遗传参数，必须对这个抽样过程做出一些假设。在经典的确定假设下，遗传参数的估计不能与确定过程的参数估计分开，因此确定过程的任何错误指定都会导致遗传参数估计出现偏差。Ewens和Shute提出了这个问题的一个解决方案，包括将样本的似然性基于数据中“与确定相关”的部分进行条件设定。这种方法的有用性只能通过检查使用它针对广泛的参数值和家庭规模分布所产生的估计的性质（特别是偏差和标准误差），然后将这些偏差和标准误差与经典确定程序下产生的偏差和标准误差进行比较来评估。本文进行了这些比较，并且我们还将所提出的方法与基于数据的部分进行条件设定或忽略数据部分的程序进行了比较。
