中文摘要：

概括线性模型是为分析 non-Gaussian 反应的一个不可缺少的工具数据，与包括地使用的正规、不在经典中的连接功能。失踪什么时候珍视，是在场的，在文学的许多存在方法重重地取决于失踪的数据机制的一个不能证实的假设，并且当假设被违背时，他们失败。这份报纸建议是的失踪的数据机制尽可能通常适用，它包括可忽略并且失踪的 nonignorable 在反应和 covariate 的失踪的价值的数据案例，以及两种情形。在这一般错过数据机制下面，作者采用一个近似有条件的可能性的方法估计未知参数。作者严厉地证实未知参数在近似有条件的可能性的下面是可看作是相同的整齐在下面条件来临。为可看作是相同的参数，作者证明最大化近似有条件的可能性的获得的评估者的 asymptotic 规度。一些模拟研究被进行从一些存在方法象评估者一样评估建议评估者的有限样品性能。最后，在场的作者在前列腺癌症学习到的 biomarker 分析说明建议方法。

英文摘要：

The generalized linear model is an indispensable tool for analyzing non-Gaussian response data, with both canonical and non-canonical link functions comprehensively used. When missing values are present, many existing methods in the literature heavily depend on an unverifiable assumption of the missing data mechanism, and they fail when the assumption is violated. This paper proposes a missing data mechanism that is as generally applicable as possible, which includes both ignorable and nonignorable missing data cases, as well as both scenarios of missing values in response and covariate. Under this general missing data mechanism, the authors adopt an approximate conditional likelihood method to estimate unknown parameters. The authors rigorously establish the regularity conditions under which the unknown parameters are identifiable under the approximate conditional likelihood approach. For parameters that are identifiable, the authors prove the asymptotic normality of the estimators obtained by maximizing the approximate conditional likelihood. Some simulation studies are conducted to evaluate finite sample performance of the proposed estimators as well as estimators from some existing methods. Finally, the authors present a biomarker analysis in prostate cancer study to illustrate the proposed method.