A New Family of Nonparametric Quantile Estimators

A new core methodology for creating nonparametric L-quantile estimators is introduced and three new quantile L-estimators (SV1 _p , SV2 _p , and SV3 _p ) are constructed using the new methodology. Monte Carlo simulation was used in order to investigate the performance of the new estimators for small and large samples under normal distribution and a variety of light and heavy-tailed symmetric and asymmetric distributions. The new estimators outperform, in most of the cases studied, the Harrell–Davis quantile estimator and the weighted average at X _([np]) quantile estimator.     

An Interview with Ariel Gore and Bee Lavender of Hip Mama

Abstract

Every small, unassuming zine has the potential to be big. Case in point: Hip Mama, a zine that began in the early '90s as Ariel Gore's senior project using a $1,000 student loan and blossomed into a nationally acclaimed alternative parenting resource. Together with Bee Lavender, Gore continues to publish the journal much as she did with that first issue. But today new readers and new technologies are pushing the zine onto the Web and into larger communities. Read all about how a little zine became a giant in this interview with Ariel Gore and Bee Lavender of Hip Mama. Serials Review 2002; 28:159–162.     

The Balance Point: Keeping Serial Bibliographic Records Viable in a Digital Age

Abstract

Michael Norman, head of serials cataloging at University of Illinois at Urbana-Champaign (UIUC) library, describes his library's development of an integrated management system for serials, the Online Research Resources (ORR). The ORR was designed to draw together and deliver to the public a wide range of information related to the library's serial holdings that previously could only be obtained by consulting a number of sources. With a single search, UIUC patrons can now consult the ORR for serials information such as variant titles, online availability, subject categories, print summary statements, ISI impact factor, where the title is indexed, and whether it is peer reviewed—an innovation sorely needed and highly welcomed by librarians and patrons alike.     

A fortran implementation of univariate fourier series density estimation

A set of Fortran-77 subroutines is described which compute a nonparametric density estimator expressed as a Fourier series. In addition, a subroutine is given for the estimation of a cumulative distribution. Performance measures are given based on samples from a Weibull distribution. Due to small size and modest space demands, these subroutines are easily implemented on most small computers.     

A comparison of some conditional and unconditional exact tests for 2x2 contingency tables

In 1935, R.A. Fisher published his well-known “exact” test for 2x2 contingency tables. This test is based on the conditional distribution of a cell entry when the rows and columns marginal totals are held fixed. Tocher (1950) and Lehmann (1959) showed that Fisher s test, when supplemented by randomization, is uniformly most powerful among all the unbiased tests UMPU). However, since all the practical tests for 2x2 tables are nonrandomized - and therefore biased the UMPU test is not necessarily more powerful than other tests of the same or lower size. Inthis work, the two-sided Fisher exact test and the UMPU test are compared with six nonrandomized unconditional exact tests with respect to their power. In both the two-binomial and double dichotomy models, the UMPU test is often less powerful than some of the unconditional tests of the same (or even lower) size. Thus, the assertion that the Tocher-Lehmann modification of Fisher's conditional test is the optimal test for 2x2 tables is unjustified.     

An empirical study of the type I error rate and power for some selected normal-theory and nonparametric tests of the independence of two sets of variables

Normal-theory tests of the hypothesis of no relationship among two sets of variables require assumptions of independence, hamoscedasticity, and normality. If, however, the assumption of normality is not tenable, there are few guidelines for properly using these tests. Historically, the lack of a comprehensive hypothesis-testing framework in the nonparametric case has provided few alternatives to normal-theory procedures. Fortunately, this situation has changed with the introduction of nonparametric, general linear model-based tests that can be used with existing computing packages. Multivariate-nonparametric tests due to Puri and Sen (1969, 1971, 1985) and Conover and Iman (1981) are outlined, and the results of a simulation study of the performance of three nonparametric and one normal-theory test of the hypothesis of no relationship among two sets of variables are presented. These results suggest that multivariate-nonparametric tests should be considered for a variety of data conditions. especially heavy-tailed and badly skewed data for small samples and a large number of variates.