The opinions expressed in this section are those of the reviewers. These opinions do not represent positions of the reviewer's organization and may not reflect those of the editors or the sponsoring societies. Listed prices reflect information provided by the publisher and may not be current.The book purchase programs of the American Society for Quality can provide some of these books at reduced prices for members. For information, contact the American Society for Quality at 1-800- 248-1946.

In survival data analysis, the Cox proportional hazards (PH) model is perhaps the most widely used model to feature the dependence of survival times on covariates. While many inference methods have been developed under such a model or its variants, those models are not adequate for handling data with complex structured covariates. High‐dimensional survival data often entail several features: (1) many covariates are inactive in explaining the survival information, (2) active covariates are associated in a network structure, and (3) some covariates are error‐contaminated. To hand such kinds of survival data, we propose graphical PH measurement error models and develop inferential procedures for the parameters of interest. Our proposed models significantly enlarge the scope of the usual Cox PH model and have great flexibility in characterizing survival data. Theoretical results are established to justify the proposed methods. Numerical studies are conducted to assess the performance of the proposed methods.

No abstract

Technological advances associated with data acquisition are leading to the production of complex structured data sets. The recent development on classification with multiclass responses makes it possible to incorporate the dependence structure of predictors. The available methods, however, are hindered by the restrictive requirements. Those methods basically assume a common network structure for predictors of all subjects without taking into account the heterogeneity existing in different classes. Furthermore, those methods mainly focus on the case where the distribution of predictors is normal. In this paper, we propose classification methods which address these limitations. Our methods are flexible in handling possibly class-dependent network structures of variables and allow the predictors to follow a distribution in the exponential family which includes normal distributions as a special case. Our methods are computationally easy to implement. Numerical studies are conducted to demonstrate the satisfactory performance of the proposed methods.

Model selection plays a critical role in statistical inference and a large literature has been devoted to this topic. Despite extensive research attention on model selection, research gaps still remain. An important but relatively unexplored problem concerns truncated and censored data with measurement error. Although analysis of left-truncated and right-censored (LTRC) data has received extensive interests in survival analysis, there has been no research on model selection for LTRC data with measurement error. In this paper, we take up this important problem and develop inferential procedures to handle model selection for LTRC data with measurement error in covariates. Our development employs the local model misspecification framework ([6]; [10]) and emphasizes the use of the focus information criterion (FIC). We develop valid estimators using the model averaging scheme and establish theoretical results to justify the validity of our methods. Numerical studies are conducted to assess the performance of the proposed methods.