Serials Spoken Here

Abstract

This article describes the concepts of Web 2.0 and Lib 2.0 and how they can be applied to libraries and in a library environment.     

The NISO Institutional Identifiers (I2) Working Group

Abstract

Recognizing the importance of the institution as a critical entity in any information model, from information creation to information sharing, NISO (National Information Standards Organization) has formed a working group to develop a standard (Z39.94) for identifying institutions. This installment of “Standards Update” will discuss the mission and objectives of that working group and provide some information about its timelines for developing the standard.     

CORE (Cost of Resource Exchange) Standard

Abstract

This article describes the CORE (Cost of Resource Exchange) standard. CORE defines an XML schema to allow the exchange of financial information relating to library resources among library systems.     

Smooth estimation of a monotone density

We investigate the interplay of smoothness and monotonicity assumptions when estimating a density from a sample of observations. The nonparametric maximum likelihood estimator of a decreasing density on the positive half line attains a rate of convergence of [Formula: See Text] at a fixed point t if the density has a negative derivative at t. The same rate is obtained by a kernel estimator of bandwidth [Formula: See Text], but the limit distributions are different. If the density is both differentiable at t and known to be monotone, then a third estimator is obtained by isotonization of a kernel estimator. We show that this again attains the rate of convergence [Formula: See Text], and compare the limit distributions of the three types of estimators. It is shown that both isotonization and smoothing lead to a more concentrated limit distribution and we study the dependence on the proportionality constant in the bandwidth. We also show that isotonization does not change the limit behaviour of a kernel estimator with a bandwidth larger than [Formula: See Text], in the case that the density is known to have more than one derivative.     

A genearalization of a result of chernoff in large sample theory

A wellknown result of CHERNOFF on the convergence rate of the 2nd kind error in testing two simple hypotheses is generalized to the case of finite hypotheses by help of methods of linear programming and the asymptotic behaviour of the corresponding least favourable distribution is investigated.