Tables to facilitate fitting lu distributions

This paper is concerned with estimating the parameters of Tadikamalla-Johnson's L_Udistributions based on the method of moments. Tables of the parameters of the L_U distribution are given for selected values of skewness (0.0(0.05) 1.0(0.1)2.0) and for twenty values of kurtosis at intervals of 0.2. The construction and use of these tables is explained with a numerical example.     

Joint-regression Analysis for Incomplete Two-way Tables

Joint-regression analysis of incomplete two-way tables of genotype-by-environment data is usually carried out using the iterative method of Digby (1979). However, the nonlinear analysis method of Ng & Grunwald (1997) can also be used. The speed of convergence for the Ng & Grunwald method compares favourably with that of Digby, and more importantly the method gives valid inference and hence should be preferred.     

混合型P2P网络中的基于Gossip的动态自适应算法

为了提高搜索的性能,出现了混合P2P网络搜索方法。在混合P2P网络中,关键的问题在于确定资源的流行程度。针对该问题,该文提出了一种基于Gossip的动态自适应算法(DAHG),通过抛硬币操作估计节点加入和离开P2P网络时所带入和带走的文档副本数;并通过Gossips传递估计值,获得资源的流行程度。仿真结果表明,该算法具有良好的适应性,能够反映P2P网络中节点和资源的动态性,从而选择出正确的资源搜索方法,减少搜索响应时间和提高资源的命中率。     

Statistical analysis of performance indicators in UK higher education

Summary.  Attempts to measure the quality with which institutions such as hospitals and universities carry out their public mandates have gained in frequency and sophistication over the last decade. We examine methods for creating performance indicators in multilevel or hierarchical settings (e.g. students nested within universities) based on a dichotomous outcome variable (e.g. drop-out from the higher education system). The profiling methods that we study involve the indirect measurement of quality, by comparing institutional outputs after adjusting for inputs, rather than directly attempting to measure the quality of the processes unfolding inside the institutions. In the context of an extended case-study of the creation of performance indicators for universities in the UK higher education system, we demonstrate the large sample functional equivalence between a method based on indirect standardization and an approach based on fixed effects hierarchical modelling, offer simulation results on the performance of the standardization method in null and non-null settings, examine the sensitivity of this method to the inadvertent omission of relevant adjustment variables, explore random-effects reformulations and characterize settings in which they are preferable to fixed effects hierarchical modelling in this type of quality assessment and discuss extensions to longitudinal quality modelling and the overall pros and cons of institutional profiling. Our results are couched in the language of higher education but apply with equal force to other settings with dichotomous response variables, such as the examination of observed and expected rates of mortality (or other adverse outcomes) in investigations of the quality of health care or the study of retention rates in the workplace.     

Book Reviews

Books reviewed:
J.K. Lindsey, Introductory Statistics: A Modelling Approach
D.R. Brillinger, L.T. Fernholz & S. Morgenthaler (eds), The Practice of Data Analysis: Essays in Honor of John W. Tukey
Thomas P. Ryan, Modern Regression Methods
Samuel Kotz & Cynthia R. Lovelace, Process Capability Indices in Theory and Practice
David A. Harville, Matrix Algebra from a Statistician's Perspective
Steven G. Krantz, A Primer of Mathematical Writing
Nicholas J. Higham, Handbook of Writing for the Mathematical Sciences     

A note on the greenwood variance estimator in cohort life tables: Isolating the effect of one source of bias

A modification of the Greenwood variance estimator is defined and shown to be free of bias whenever its constitu­ent interval estimators are conditionally unbiased, given the sample size at the start of the interval. Using the modified estimator as a standard of comparison, the original Greenwood estimator is seen to have an intrinsic positive bias.Under­estimation of variances through the use of Greenwood's formula must be due to bias in the constituent interval estimators and/or, with fixed interval bounds, due to disregarding the random character of the total number of life table intervals to exhaustion of ttje sample. Some easy to prove properties of the modified and the original Greenwood estimators are stated that apply in the absence of censoring. A suggest­ion is made for reducing the bias of the interval variance estimators.     

A month carlo approximation of the distributions of the maximum of various brownjan bridges

A Brownian bridge of order a is the weak limit of a residual partial sum obtained from regression fitting. When q=0, the process is teh usual Brownian bridge, and the distribution of the maximum is known analytically. For q>1, the reflection principle does not easily apply. The supremum distributions are approximated by a Monte Carlo technique. Tables of these distributions, as well as a finite sample size correction are given.     

A new C(α) test for 2×2 tables

Two by two contingency tables which arise from experiments in which each subject serves as a self control are examined, Two types of experimental design are discussed: the confounded design and the counterbalanced design. In the case of the counterbalanced design, the locally asymptotically optimal C(α) test is compared with the binomial test for symmetry in a 2×2 contingency table. The binomial test is found to be less efficient than the C(α) test.     

A Comparison of Two Tests for Equality of Two Proportions

Two procedures for testing equality of two proportions are compared in terms of asymptotic efficiency. The comparison favors use of a statistic equivalent to Goodman's Y ² over the usual X ² statistic in some cases including that of equal sample sizes. Numerical comparisons indicate that the asymptotic results have some relevance for moderate sample sizes.     

An Example of the Large Sample Behavior of the Midrange

A data set in the form of a 2 × 2 × 2 contingency table is presented and analyzed in detail. For instructional purposes, the analysis of the data can be used to illustrate some basic concepts in the loglinear model approach to the analysis of multidimensional contingency tables.