“Shelf-Ready” Print Serials Acquisitions

The focus of this installment of “The Balance Point” is “shelf-ready” print serials acquisitions, including functions associated with traditional consolidation services (ordering, receiving, check-in, labeling, claiming and batch shipments), and the newer capabilities of uploading check-in data automatically into library systems. Featured authors discuss traditional vendor consolidation services, pilot projects and experiments in the pursuit of what they define as genuinely “shelf-ready” periodicals. The authors view obtaining “shelf-ready” print journal issues as an effective and efficient means of managing print serials operations while coping with the demands of managing digital resource acquisitions with limited financial resources.     

The “Green” and “Gold” Roads to Open Access: The Case for Mixing and Matching

Recent discussions on Open Access (OA) have tended to treat OA journals and self-archiving as two distinct routes. Some supporters of self-archiving even suggest that it alone can bring about full Open Access to the world's scientific literature. In this paper, it is argued that each route actually corresponds to a phase in the movement toward Open Access; that the mere fact of self-archiving is not enough; that providing some branding ability to the repositories is needed. However, doing so will eventually bring about the creation of overlay (or database) journals. The two roads, therefore, will merge to create a mature OA landscape.     

“Weak” stochastic orderings and dependence measures

Many notions of dependence rely upon orderings of random pairs. These orderings are generally partial orders, and thus there are many pairs of random vectors which are not comparable. By using a weakened version of stochastic dominance, many new orderings, as well as corresponding dependence measures, are created. The application to stock market data is explored.     

Comments on “bootstrapping time series models”

We take issue with the main suggestion in Li and Maddala (LiMa) that bootstrapping residuals is always the preferred approach and question some of their guidelines. We show that it can be potentially misleading to mimic the autocovariance structure of residuals, since it can be very different from that of true errors. We emphasize that the residuals are sensitive to model misspecification and generally not a part of the information set. We make constructive suggestions and propose a semiparametric method.     

In search of the “best” growth inhibitor

In searching for the “best” growth inhibitor, we decided to consider growth inhibition in terms of the lengths of the terminal sprouts. For it is logical to infer that the trees with the longer sprouts (after a 20-month period) will most likely be the ones that will need trimming in the future. Additionally, we reasoned that if a particular treatment produced a smaller proportion of “long” sprouts, then it would be a more effective growth inhibitor. It was now necessary to define what was meant by “long”. After consultation with foresters we chose cutoff lengths of 15.0, 25.0 and 35.0 cm. Hence the response variable was chosen to be the proportion of the terminal sprouts on a tree that exceeded a specified cutoff length. By varying the cutoff lengths, we would minimize the effect of the arbitrariness involved in choosing one particular length.     

Discussion of “Statistical disease mapping for heterogeneous neuroimaging studies”

This is a discussion of Liu & Zhu (2021), which develops a novel statistical disease mapping framework for neuroimaging data analysis.     

Rejoinder: “Statistical disease mapping for heterogeneous neuroimaging studies”

We thank all the discussants for sharing their valuable viewpoints on the proposed statistical disease mapping (SDM) framework. In our article, we addressed the issue of imaging heterogeneity at both the global and local scales by efficiently borrowing common information shared among a large number of diseased and normal subjects. Understanding such imaging heterogeneity is critical in the development of urgently needed analytic approaches to the prevention, diagnosis, treatment, and prognosis of many diseases (e.g., Alzheimer's disease, brain cancer, and lung cancer), as well as precision medicine broadly. The discussants emphasized improvements to disease mapping by introducing some alternative modelling strategies and many possible future directions in this research topic. The sections of this rejoinder are organized by discussant to address each of their comments separately.     

Almost Consistent Estimation of Panel Probit Models with “Small” Fixed Effects

We propose four different GMM estimators that allow almost consistent estimation of the structural parameters of panel probit models with fixed effects for the case of small Tand large N. The moments used are derived for each period from a first order approximation of the mean of the dependent variable conditional on explanatory variables and on the fixed effect. The estimators differ w.r.t. the choice of instruments and whether they use trimming to reduce the bias or not. In a Monte Carlo study, we compare these estimators with pooled probit and conditional logit estimators for different data generating processes. The results show that the proposed estimators outperform these competitors in several situations.