ON THE ANALYSIS AND APPLICATION OF MEASURES OF LINKAGE DISEQUILIBRIUM

The maximum likelihood, jackknife and bootstrap estimators of linkage disequilibrium, a measure of association in population genetics, are derived and compared. It is found that for point estimation, the resampling methods generate almost identical mean square errors. The maximum likelihood estimator could have bigger or smaller mean square errors depending on the parameters of the underlying population. However the bootstrap confidence interval is superior to the other two as the length of the intervals is shorter or the probability that the 95% confidence intervals include the true parameter is closer to 0.95. Although the standardised measure of linkage disequilibrium has a range from -1 to 1 regardless of marginal frequencies, it is shown that the distribution of this standardised measure is still not allele frequency independent under the multinomial sampling scheme.     

Some comments on Latin squares and on Graeco-Latin squares,illustrated with postage stamps and old playing cards

We present some comments on Latin squares and on Graeco-Latin squares, with special emphasis on their use in statistics and in a historical context. We also comment on the Knut Vik square, the knight’s move design and the knight’s tour, as well as the Magic Card Puzzle. We consider the well-known 36 officers problem studied by Euler (Verhandelingen uitgegeven door het zeeuwsch Genootschap der Wetenschappen te Vlissingen, vol. 9 (Middleburg 1782), pp. 85–239, 1779/1782), and give two examples of diagonal Latin squares of order 6 due, respectively, to Abbé François-Guillaume Poignard (Chez Guillaume Fricx, Imprimeur &; libraire ruë Bergestract, à l’enseigne des quatre Evangelistes, Bruxelles [4] 79 pp. (p. 71 folded), 1704) and József Dénes (J Lond Math Soc Ser 2, 6(4):679–689, 1970). We illustrate our comments with images of postage stamps and old playing cards. An extensive annotated bibliography ends the paper.     

Robust estimation of covariance parameters in partial linear model for longitudinal data

For longitudinal data, the within-subject dependence structure and covariance parameters may be of practical and theoretical interests. The estimation of covariance parameters has received much attention and been studied mainly in the framework of generalized estimating equations (GEEs). The GEEs method, however, is sensitive to outliers. In this paper, an alternative set of robust generalized estimating equations for both the mean and covariance parameters are proposed in the partial linear model for longitudinal data. The asymptotic properties of the proposed estimators of regression parameters, non-parametric function and covariance parameters are obtained. Simulation studies are conducted to evaluate the performance of the proposed estimators under different contaminations. The proposed method is illustrated with a real data analysis.     

A Note on Local Influence Based on Normal Curvature

Object functions other than the likelihood displacement, such as a parameter estimate or a test statistic, can also be used in local influence analysis. The normal curvatures of these object functions have been studied in situations where the slopes were non-zero. In these situations, we show that the normal curvature is not scale invariant and thus ambiguous conclusions will be drawn. Comments on the application of the general normal curvature formula are presented.     

The Estimation and Inference of a Panel Cointegration Model with a Time Trend

This article investigates the asymptotic properties of coefficient estimators in the panel cointegration model with a time trend. We find that the bias of OLS estimator for the slope coefficient in the panel cointegration model with a time trend is distinct from that in the panel cointegration model without a time trend. Meanwhile, the variance of the limiting distribution for the slope coefficient is larger in the panel cointegration model with a time trend than without a time trend.     

Exact D-optimal designs for a linear log contrast model with mixture experiment for three and four ingredients

This study investigates the exact D-optimal designs of the linear log contrast model using the mixture experiment suggested by Aitchison and Bacon-Shone (1984) and the design space restricted by Lim (1987) and Chan (1988). Results show that for three ingredients, there are six extreme points that can be divided into two non-intersect sets S₁ and S₂. An exact N-point D  -optimal design for N=3p+q,p≥1,1≤q≤2$N=3p+q,p\ge 1,1\le q\le 2$ arranges equal weight n/N,0≤n≤p$n/N,0\le n\le p$ at the points of S₁ (S₂) and puts the remaining weight (N−3n)/N$(N-3n)/N$ on the points of S₂ (S₁) as evenly as possible. For four ingredients and N=6p+q,p≥1,1≤q≤5$N=6p+q,p\ge 1,1\le q\le 5$, an exact N-point design that distributes the weights as evenly as possible among the six supports of the approximate D-optimal design is exact D-optimal.     

Class and Disability: influences on learning expectations

This paper describes some findings from in-depth interviews with 10 adults with disabilities, most of whom had strong working class connections. It places the findings in the context of feminist post-structuralist theory. Whilst the research is not directly to do with gender relationships, the theoretical framework highlights notions of difference, cultural value and meaning which play a significant role in the power relationships between dominant and subordinated groups. The interviews invited participants to tell their life story with regard to educational experiences. Analysis of these stories indicated that expectations, rather than ambitions, had influenced most people's educational activities as adults. The combination of poor childhood experiences, class and disability attitudes, had a powerful influence on personal goals, which appeared to misrepresent real potential or real interest, once stimulated. The extract here is part of a larger research project which is looking at differentials amongst three different learner group cultures. The research is concerned with the mismatch of expectations among university continuing education providers and those of under-represented groups.