Conditional Generalized Liouville Distributions on the Simplex

Liouville and generalized Liouville distributions on the simplex have been proposed for modeling compositional data and have been shown to be free from the extreme independence structure that characterizes the Dirichlet class. In this article, generalized Liouville distributions are shown to be rich enough to distinguish some lesser modes of independence as well. Unfortunately, it is noted that the applicability of the Liouville family will be limited, owing to the lack of invariance with respect to the chosen fill-up value. As an alternative, a new family of simplex distributions is proposed, one that admits invariance with respect to choice of fill-up value, as well as the ability to differentiate among many forms of independence.     

On computer generation of gamma random variables by rejection and composition procedures 2

The paper presents various algorithms for generating gamma random variables, by combining rejection and composition procedures. Two efficient algorithms are given for the case when the parameter of the gamma distribution is 0<v<l. For the case vl, several algorithms are given but they (except one), work reliably only for small values of v. Results of some computer tests together with FORTRAN subroutines are also presented.     

Number of observations near the maximum in an F α-scheme

Let K _n (a) be the number of observations in the interval (M _n ,?a, M _n ), where M _n is the maximum value in a sequence of size n. We study the asymptotic properties of K _n (a) under the F ^α-scheme and discuss the influence of the associated sequence α _n on the limit behaviour of this random variable.     

Non-ergodic martingale estimating functions and related asymptotics

Well-known estimation methods such as conditional least squares, quasilikelihood and maximum likelihood (ML) can be unified via a single framework of martingale estimating functions (MEFs). Asymptotic distributions of estimates for ergodic processes use constant norm (e.g. square root of the sample size) for asymptotic normality. For certain non-ergodic-type applications, however, such as explosive autoregression and super-critical branching processes, one needs a random norm in order to get normal limit distributions. In this paper, we are concerned with non-ergodic processes and investigate limit distributions for a broad class of MEFs. Asymptotic optimality (within a certain class of non-ergodic MEFs) of the ML estimate is deduced via establishing a convolution theorem using a random norm. Applications to non-ergodic autoregressive processes, generalized autoregressive conditional heteroscedastic-type processes, and super-critical branching processes are discussed. Asymptotic optimality in terms of the maximum random limiting power regarding large sample tests is briefly discussed.     

Parameter measures of skewness

Several different measures of skewness are commonly used in place of γ₁, the third central moment divided by the cube of the standard deviation. The numerical values of these measures are compared in this paper for members of the gamma, lognormal or Weibull family of distributions and shown to vary considerably in most cases even when skewness and kurtosis are moderate.     

Book Reviews

The diagnostic tools examined in this article are applicable to regressions estimated with panel data or cross-sectional data drawn from a population with grouped structure. The diagnostic tools considered include (a) tests for the existence of group effects under both fixed and random effects models, (b) checks for outlying groups, and (c) specification tests for comparing the fixed and random effects models. A group-specific counterpart to the studentized residual is introduced. The methods are illustrated using a hedonic housing price regression.     

Seasonal Adjustment of Time Series Using One-Sided Filters

This article presents a model-based signal extraction seasonal adjustment procedure to extract estimates of the independent unobserved seasonal and nonseasonal components from an observed time series. The decomposition yields a one-sided filter that is optimal for adjusting the most recent observation under the assumption of using only the past observed series. Some advantages of this procedure are that no forecasts are required for implementation and there are no problems of revision of estimates or questions of concurrent adjustment. Comparisons are made with existing procedures using two-sided filters.     

Serials and the OCLC Acquisitions Subsystem

Abstract

Academic library consortia, faced with requirements of assessment and accountability, are increasingly pressured to measure the impact of their networked electronic services. Studies on usage of electronic library resources and electronic journals, in particular, proliferate; relatively few focus on the academic library consortium. This paper gives an overview of one assessment tool, MINES for Libraries®, as implemented in the Scholars Portal service of the Ontario Council of University Libraries (OCUL). The novel contributions of this study are twofold: firstly, the creation of peer groups within an academic consortium for comparison purposes, and secondly, the use of regression analysis to explore the correlation between usage and three separate variables outside the MINES survey — library print holdings, library acquisitions budget, and sponsored research revenue.