The Effects of E-journal Management Tools and Services on Serials Cataloging

E-journal management tools and services such as MARC record services, A-to-Z lists, and link resolvers are changing e-journal cataloging. This column explores these changes in the academic environment through interviews with ten librarians representing eight universities. Three areas of change in serials cataloging are explored: (1) changes to the MARC record, including how libraries are adding/creating MARC records for their catalogs, the number and type of MARC records being created and linking within MARC bibliographic and holdings records; (2) the manner in which serials catalogers are being informed of changes; and (3) the evolving role of the serials cataloger. Future trends and advice for evolving workflow practices conclude the discussion.     

Best Practices for Presentation of E-journal Titles on Provider Web Sites and in Other E-content Products

As a result of inaccurate displays of journal metadata in e-content access tools and on Web sites, researchers and readers do not find the content they are seeking, and the full value of e-content services is not realized. A group will be formed later this year to develop a set of NISO best practices for presentation of e-journal titles, provision of correct ISSN information, and other basic bibliographic data on provider Web sites and in other e-content products.     

Copula-based regression models: A survey

In this review paper we collect several results about copula-based models, especially concerning regression models, by focusing on some insurance applications.     

Characteristic function estimation of non-Gaussian Ornstein–Uhlenbeck processes

Continuous non-Gaussian stationary processes of the OU-type are becoming increasingly popular given their flexibility in modelling stylized features of financial series such as asymmetry, heavy tails and jumps. The use of non-Gaussian marginal distributions makes likelihood analysis of these processes unfeasible for virtually all cases of interest. This paper exploits the self-decomposability of the marginal laws of OU processes to provide explicit expressions of the characteristic function which can be applied to several models as well as to develop efficient estimation techniques based on the empirical characteristic function. Extensions to OU-based stochastic volatility models are provided.