bluuebunny/crossref_metadata_2025 huggingface dataset:
The heterogeneity of discourse genres: Implications for development
Authors: Hengst Julie A. , Miller Peggy J.
Published: March 2003

In contrast to traditional views of genre as relatively homogeneous and rule‐ordered forms, situated theories of communication conceptualize discourse genres as powerful and durable resources that are fully situated in the complexities of communicative interactions. From this perspective, discourse genres must be understood within complex and dynamic cultural practices. In this article we focus on such generic practices by tracing the pervasive heterogeneity and the distributed nature of discourse genres‐in‐use. We explore three examples from our research: a father and his two daughters playing Cindy Magic—a family‐created, verbal game; a family's engagement with their two‐year‐old's creative retellings of the tale of Peter Rabbit; and a woman with aphasia playing a barrier game in a research setting with her teenage son. These analyses point to ways that histories of generic practices are sedimented in and distributed across diverse resources, and to the ways that participants weave these traces into specific patterns. It is the pervasive heterogeneity of situated interactions, we argue, that is the ground of the ongoing development of this distributed communicative competence—the development of persons, resources, and practices.
Futures for Hong Kong English
Authors: Bolton Kingsley , Lim Shirley
Published: March 2003

In this paper the authors discuss the themes of autonomy and creativity as they have been expressed in this special issue. At a linguistic level, the emergence of Hong Kong English has been clouded by a cluster of language ideologies within a localised complaint tradition. This has included discourses relatinzg to ‘monolingualism’, the ‘invisibility’ of Hong Kong English, and ‘falling standards’. Parallel discourses have also constrained creative writing in Hong Kong, but the appearance of new Hong Kong writers points the way to a fresh and energetic future for literary creativity. One example of this is the new journal Yuan Yang, largely written and edited by creative writing students at The University of Hong Kong.
Bilingual creativity in Chinese English: Ha Jin’s In the Pond
Authors: Zhang Hang
Published: March 2003

This paper addresses issues related to bilingual creativity in Chinese English and their implications for world Englishes in the Chinese context. The language examined in this paper is drawn from Ha Jin’s novella, In the Pond, in which the author’s use of English is ‘nativized’ to the Chinese context in order to recast the cultural meanings of the language. Rooted in social realism, cultural authenticity, and historical accuracy, Ha Jin describes the lives of ordinary people against the larger political backdrop of China. His skilful use of English successfully transcreates his native Chinese experience to form an indigenized narrative style. In this paper, I employ a theoretical framework derived from Kachru (1982, 1986, etc.) to show how Ha Jin’s work expresses a distinct Chinese cultural identity. Ha Jin’s use of creative innovations in English reflect the texture of Chinese ideological, political and socio–cultural representations. The linguistic exponents of such creativity range from the use of address terms, curse words, to metaphors, proverbs, political expressions, and other aspects of the contemporary Chinese lexicon. Despite a central concern with universal themes in his fiction, Ha Jin simultaneously conveys a distinct sense of ‘Chineseness’ through the use of nativized discourse patterns, rhetorical strategies, speech acts, and a range of other linguistic devices.
The birth of a particle: know in Colloquial Singapore English
Authors: Wee Lionel
Published: March 2003

The discourse marker you know has been widely noted and analyzed in different varieties of English. However, it is less widely recognized that Colloquial Singapore English (CSE), in addition to the marker you know, has also developed the particle know. This paper provides data showing that know has distributional and collocational properties that distinguish it from you know, even though their discourse–pragmatic functions are similar. The differences between you know and know indicate that the latter has become a member of a linguistic category, referred to in this paper as the discourse particle. This category is exemplified by more familiar forms such as lah, lor, and meh. The paper suggests that the mechanism by which know has joined the other particles is that of analogical change, made possible because (1) CSE already contains a rich inventory of such particles, and (2) it is a pro–drop language. The first feature motivates the extraction of a category where members are monosyllabic, occur in clause–final position, and perform discourse–pragmatic functions. You know possesses two of the three characteristics, but is excluded because it is not monosyllabic. However, the pro–drop feature allows the monosyllabic know to emerge and join the category of discourse particle.
Multilingual crews: communication and the operation of ships
Authors: Sampson Helen , Zhao Minghua
Published: March 2003

Modern merchant shipping increasingly utilises a global labour market for seafarers. In recent years this trend has been accompanied by increasing technological innovation at sea including the introduction of direct voice–based communication technologies which have made traditional and universal forms of communication at sea, such as Morse lamps, wholly redundant. Thus with the introduction of multilingual crews and the loss of universal forms of communication the importance of English as the ‘lingua franca’ of the sea has become paramount. To improve standards of English amongst seafarers and to militate against accidents and incidents at sea caused by poor communication, a ‘top down’ approach to language learning has been utilised by industry regulators and training establishments. This paper considers the effectiveness of ‘top down’ approaches to language development drawing upon ethnographic research conducted aboard vessels with multilingual crews. It considers the importance of communication on board highlighting both job–related and social interaction and communication, and describing how these cannot be considered in isolation. It concludes that with regard to multilingual crews the evidence suggests that ‘bottom up’ learning is more effective aboard than a ‘top down’ approach. This has implications for seafarers and the shipping industry, but the paper's findings also support the work of linguists advocating ‘immersion’ teaching and learning programmes in providing evidence of the ways in which effective learning and communication occur in a ‘real’ work and social context beyond the confines of a classroom.
The “Flypaper Effect” Is Not an Anomaly
Authors: Roemer John E. , Silvestre Joaquim
Published: March 2003

The empirical nonequivalence between grants by a central government and increases in community income (the “flypaper effect”) has been considered anomalous. But the “anomaly” label is naïve: in a multiconsumer community, equivalence demands an unlikely match of tax rules and income‐growth patterns.

We go beyond the single‐policy‐variable, median‐voter model and apply Roemer’s concept of Party Unanimity Nash Equilibrium, which allows for party competition in multidimensional policy spaces. We compute the equilibria for a model with two independent policy variables (intercept and slope of an affine tax schedule) and obtain numerical values that agree with the empirical literature.
Policy Reversals and Electoral Competition with Privately Informed Parties
Authors: Martinelli César , Matsui Akihiko
Published: March 2003

We develop a spatial model of competition between two policy‐motivated parties. The parties know which policies are desirable for voters, while voters do not. The announced positions of the parties serve as signals to the voters concerning the parties’ private information. In all separating equilibria, when the left‐wing party attains power, the policies it implements are to the right of the policies implemented by the right‐wing party when it attains power. Intuitively, when right‐wing policies become more attractive, the left party moves toward the right in order to be assured of winning, while the right‐wing party stays put in a radical stance.
A Note on the Geographic Interdependencies of Retail Market Areas
Authors: Mushinski David , Weiler Stephen
Published: March 2003

Central place theory describes an orderly hierarchy of places, with particular retail services developing for lower‐ordered places as they reach a threshold. Yet it is likely that nearby areas could serve simultaneously as a source of demand and a source of competing supply for retail stores in a place. This paper contributes to the understanding of local economic development by modeling and estimating the geographic interdependence between a place and its neighboring areas. The simultaneous equation Tobit results suggest that such geographical interdependence exists for most retail industries, with spatial competition on the supply side being particularly important.
Pakeha Deaconesses and the New Zealand Methodist Mission to Maori, 1893–1940
Authors: Tennant Margaret
Published: March 2003

This paper focuses on the ﬁrst three decades of deaconess involvement in the New Zealand Methodist Maori Mission. Drawing upon church policy statements, deaconess narratives of experience which entered the public domain, as well as reminiscences which did not, it situates the deaconess missionaries in relation to the assimilationist discourses and gender assumptions of their day. It considers how Pakeha (European) deaconesses’ potential as cultural intermediaries and models of white femininity was shaped by the particular histories of the areas in which they worked, by their own anomalous status within their denomination and by the spread of competing, secular channels of inﬂuence. As the sisters added a social work role to evangelism and education, many were drawn into close association with Maori communities, acting as advocates for Maori causes and challenging derogatory Pakeha constructs of Maori lifestyles.
Silent Reading as Determined by Age and Visual Acuity
Authors: Aberson Dieuwke H. A. , Bouwhuis Don G.
Published: March 2003

In this study silent reading by adults ranging in age from 35 to 90 years was investigated. The texts to be read were printed in black on white paper with character sizes varying from 1–9 mm x‐height (visual angle 0.19° to 1.67°). In order to separate age effects from visual‐acuity effects, subjects with different levels of visual acuity (0.1–2.5 decimal acuity) participated in the experiment. Silent reading rate was employed as the dependent variable. Visual acuity affected reading rate most, followed by letter size. In normal‐acuity subjects the variance in reading rates decreased as a function of age. Reading rates initially increased rapidly with increasing letter size, but after reaching an optimum gradually declined again as letters became larger. For the different acuity classes there appeared to be clearly optimal letter sizes, varying from 1.9 for the highest acuity group to 6 mm for the lowest acuity group at the 33 cm reading distance employed. However, the optimal reading rates of visually impaired subjects found in this study remained below those of individuals with normal acuity. This suggests that visual impairment is a more general neural phenomenon rather than merely a deficient optical image. The obtained reading‐rate data could be accurately described by a theoretical model encompassing a decoding process and an integration process. It appeared that the model predictions were entirely determined by the smallest letter size at which reading is just possible with a specified visual acuity. It is concluded that both decoding and integration are dependent on visual acuity and that, in the absence of specific visual defects, ageing effects in reading can be completely explained by gradual lowering of visual acuity having its origin in central mechanisms.
Collaborative Creative Writing in Eight‐Year‐Olds: Comparing Cross‐Ability Fixed Role and Same‐Ability Reciprocal Role Pairing
Authors: Sutherland Jennifer A. , Topping Keith J.
Published: March 2003

This study investigated the effects of a structured procedure for collaborative writing (Paired Writing) on the quality of creative writing and attitudes to writing in 8‐year‐old children. Each of two classes featured an experimental collaborative writing group and a control group, which produced writing individually. In one class the experimental group collaborated in different‐ability pairs with fixed roles as helper and writer; in the other class, in same‐ability pairs reciprocating roles. The 8 week project involved training for the participating children. Analyses were made of quality of individual writing before and after the project and of collaborative writing during the project. Pre‐post gains in individual writing were statistically significant for the cross‐ability experimentals but not for their controls. Pre and post scores for same‐ability experimentals were not significantly different, nor for their controls. However, the same‐ability experimentals improved while their controls deteriorated, and the difference in gain between experimentals and controls was significant. Also, the collaborative writing of same‐ability pairs scored significantly higher than their pre‐project individual writing, although this was not the case for cross‐ability pairs. Both types of Paired Writers reported finding the method easy to use and a majority showed positive attitudes to it. It was concluded that both formats of Paired Writing could be effective. However, ensuring short term gains for the more able helpers in cross‐ability pairs could be problematic. Further research is needed, including trials of cross‐ability reciprocal role pairing.
Handling Missing Data in Diaries of Alcohol Consumption
Authors: Longford Nicholas T. , Ely Margaret , Hardy Rebecca , Wadsworth Michael E. J.
Published: March 2003

Missing data can rarely be avoided in large scale studies in which subjects are requested to complete questionnaires with many items. Analyses of such surveys are often based on the records with no missing items, resulting in a loss of efficiency and, when data are missing not at random, in bias. This paper applies the method of multiple imputation to handle missing data in an analysis of alcohol consumption of the subjects in the Medical Research Council National Survey of Health and Development. The outcomes studied are derived from the entries in diaries of food and drink intake over seven designated days. Background variables and other responses related to alcohol consumption and associated problems are used as collateral information. In conventional analyses, subpopulation means of quantities of alcohol consumed are compared. Since we are interested in the harmful effects of alcohol, we make inferences about the percentages of those who consume more than a given quantity of net alcohol. We assess the contribution to the analyses made by the incomplete records and outline a more integrated way of applying multiple imputation in large scale longitudinal surveys.
Capitation Funding in the Public Sector
Authors: Smith Peter C. , Rice Nigel , Carr-Hill Roy
Published: March 2003

A fundamental requirement of government at all levels—national and local—is to distribute the limited funds that it wishes to spend on particular public services between geographical areas or institutions, which are effectively competitors for such funds. Increasing use is now being made of capitation methods for such purposes, in which a standard estimate of expected expenditure is attached to a citizen with given characteristics. Statistical methods are playing an important role in determining such capitations, but they give rise to profound methodological problems. This paper examines the rationale for capitation and discusses the associated methodological issues. It illustrates the issues raised with two examples taken from the UK public sector: in personal social services and hospital care. Severe limitations of the data mean that small area data are used as the unit of observation, giving rise to considerable complexity in the model to be estimated. As a result, a range of methodologies including two-stage least squares and multilevel modelling methods are deployed. The paper concludes with a suggestion for an approach which would represent an improvement on current capitation methods, but which would require data on individuals rather than on small areas.
A Simple Method for Estimating a Regression Model for κ Between a Pair of Raters
Authors: Lipsitz Stuart R. , Williamson John , Klar Neil , Ibrahim Joseph , Parzen Michael
Published: March 2003

Agreement studies commonly occur in medical research, for example, in the review of X-rays by radiologists, blood tests by a panel of pathologists and the evaluation of psychopathology by a panel of raters. In these studies, often two observers rate the same subject for some characteristic with a discrete number of levels. The κ-coefficient is a popular measure of agreement between the two raters. The κ-coefficient may depend on covariates, i.e. characteristics of the raters and/or the subjects being rated. Our research was motivated by two agreement problems. The first is a study of agreement between a pastor and a co-ordinator of Christian education on whether they feel that the congregation puts enough emphasis on encouraging members to work for social justice (yes versus no). We wish to model the κ-coefficient as a function of covariates such as political orientation (liberal versus conservative) of the pastor and co-ordinator. The second example is a spousal education study, in which we wish to model the κ-coefficient as a function of covariates such as the highest degree of the father of the wife and the father of the husband. We propose a simple method to estimate the regression model for the κ-coefficient, which consists of two logistic (or multinomial logistic) regressions and one linear regression for binary data. The estimates can be easily obtained in any generalized linear model software program.
Additive Nonparametric Regression With Autocorrelated Errors
Authors: Smith Michael , Wong Chi-Ming , Kohn Robert
Published: March 2003

A Bayesian approach is presented for nonparametric estimation of an additive regression model with autocorrelated errors. Each of the potentially non-linear components is modelled as a regression spline using many knots, while the errors are modelled by a high order stationary autoregressive process parameterized in terms of its autocorrelations. The distribution of significant knots and partial autocorrelations is accounted for using subset selection. Our approach also allows the selection of a suitable transformation of the dependent variable. All aspects of the model are estimated simultaneously by using the Markov chain Monte Carlo method. It is shown empirically that the approach proposed works well on several simulated and real examples.
Ordered Multivariate Extremes
Authors: Nadarajah S. , Anderson C. W. , Tawn J. A.
Published: March 2003

Multivariate extreme value models and associated statistical methods are developed for vector observations whose components are subject to an order restriction. The approach extends the multivariate threshold methodology of Coles and Tawn, Joe and co-workers and Smith and co-workers. The results are illustrated by an analysis of extreme rainfalls of different durations, and by a study of the problem of linking a long series of daily rainfall extremes with a partially overlapping shorter series of hourly extremes.
Minimum Aberration and Model Robustness for Two-Level Fractional Factorial Designs
Authors: Cheng Ching-Shui , Steinberg David M. , Sun Don X.
Published: March 2003

The performance of minimum aberration two-level fractional factorial designs is studied under two criteria of model robustness. Simple sufficient conditions for a design to dominate another design with respect to each of these two criteria are derived. It is also shown that a minimum aberration design of resolution III or higher maximizes the number of two-factor interactions which are not aliases of main effects and, subject to that condition, minimizes the sum of squares of the sizes of alias sets of two-factor interactions. This roughly says that minimum aberration designs tend to make the sizes of the alias sets very uniform. It follows that minimum aberration is a good surrogate for the two criteria of model robustness that are studied here. Examples are given to show that minimum aberration designs are indeed highly efficient.
A Nonparametric Test for Current Status Data With Unequal Censoring
Authors: Sun Jianguo
Published: March 2003

Nonparametric tests for the comparison of different treatments based on current status data are proposed. For this problem, most methods proposed in the literature require that observation times on all subjects follow the same distribution. In other words, censoring distributions are identical between the treatment groups. In this paper, we focus on the situation where the censoring distributions may be different for subjects in different treatment groups and the test that can take this unequal censoring into account is given. The asymptotic distribution of the test proposed is derived. The method proposed is applied to data arising from a tumorigenicity experiment.
Weighted Empirical Adaptive Variance Estimators for Correlated Data Regression
Authors: Lumley T. , Heagerty P.
Published: March 2003

Estimating equations based on marginal generalized linear models are useful for regression modelling of correlated data, but inference and testing require reliable estimates of standard errors. We introduce a class of variance estimators based on the weighted empirical variance of the estimating functions and show that an adaptive choice of weights allows reliable estimation both asymptotically and by simulation in finite samples. Connections with previous bootstrap and jackknife methods are explored. The effect of reliable variance estimation is illustrated in data on health effects of air pollution in King County, Washington.
On a Class of m out of n Bootstrap Confidence Intervals
Authors: Lee Stephen M. S.
Published: March 2003

It is widely known that bootstrap failure can often be remedied by using a technique known as the ‘m out of n’ bootstrap, by which a smaller number, m say, of observations are resampled from the original sample of size n. In successful cases of the bootstrap, the m out of n bootstrap is often deemed unnecessary. We show that the problem of constructing nonparametric confidence intervals is an exceptional case. By considering a new class of m out of n bootstrap confidence limits, we develop a computationally efficient approach based on the double bootstrap to construct the optimal m out of n bootstrap intervals. We show that the optimal intervals have a coverage accuracy which is comparable with that of the classical double-bootstrap intervals, and we conduct a simulation study to examine their performance. The results are in general very encouraging. Alternative approaches which yield even higher order accuracy are also discussed.
Failure Time Regression With Continuous Covariates Measured With Error
Authors: Zhou Halbo , Wang C.-Y.
Published: March 2003

We consider failure time regression analysis with an auxiliary variable in the presence of a validation sample. We extend the nonparametric inference procedure of Zhou and Pepe to handle a continuous auxiliary or proxy covariate. We estimate the induced relative risk function with a kernel smoother and allow the selection probability of the validation set to depend on the observed covariates. We present some asymptotic properties for the kernel estimator and provide some simulation results. The method proposed is illustrated with a data set from an on-going epidemiologic study.
Maximum Likelihood Estimation for Spatial Models by Markov Chain Monte Carlo Stochastic Approximation
Authors: Gu Ming Gao , Zhu Hong-Tu
Published: March 2003

We propose a two-stage algorithm for computing maximum likelihood estimates for a class of spatial models. The algorithm combines Markov chain Monte Carlo methods such as the Metropolis–Hastings–Green algorithm and the Gibbs sampler, and stochastic approximation methods such as the off-line average and adaptive search direction. A new criterion is built into the algorithm so stopping is automatic once the desired precision has been set. Simulation studies and applications to some real data sets have been conducted with three spatial models. We compared the algorithm proposed with a direct application of the classical Robbins–Monro algorithm using Wiebe's wheat data and found that our procedure is at least 15 times faster.
Perfect Slice Samplers
Authors: Mira A. , Møller J. , Roberts G. O.
Published: March 2003

Perfect sampling allows the exact simulation of random variables from the stationary measure of a Markov chain. By exploiting monotonicity properties of the slice sampler we show that a perfect version of the algorithm can be easily implemented, at least when the target distribution is bounded. Various extensions, including perfect product slice samplers, and examples of applications are discussed.
Detection of Interactions in Experiments on Large Numbers of Factors
Authors: Lewis S. M. , Dean A. M.
Published: March 2003

One of the main advantages of factorial experiments is the information that they can offer on interactions. When there are many factors to be studied, some or all of this information is often sacrificed to keep the size of an experiment economically feasible. Two strategies for group screening are presented for a large number of factors, over two stages of experimentation, with particular emphasis on the detection of interactions. One approach estimates only main effects at the first stage (classical group screening), whereas the other new method (interaction group screening) estimates both main effects and key two-factor interactions at the first stage. Three criteria are used to guide the choice of screening technique, and also the size of the groups of factors for study in the first-stage experiment. The criteria seek to minimize the expected total number of observations in the experiment, the probability that the size of the experiment exceeds a prespecified target and the proportion of active individual factorial effects which are not detected. To implement these criteria, results are derived on the relationship between the grouped and individual factorial effects, and the probability distributions of the numbers of grouped factors whose main effects or interactions are declared active at the first stage. Examples are used to illustrate the methodology, and some issues and open questions for the practical implementation of the results are discussed.
Model Selection in Spline Nonparametric Regression
Authors: Wood Sally , Kohn Robert , Shively Tom , Jiang Wenxin
Published: March 2003

A Bayesian approach is presented for model selection in nonparametric regression with Gaussian errors and in binary nonparametric regression. A smoothness prior is assumed for each component of the model and the posterior probabilities of the candidate models are approximated using the Bayesian information criterion. We study the model selection method by simulation and show that it has excellent frequentist properties and gives improved estimates of the regression surface. All the computations are carried out efficiently using the Gibbs sampler.
Models for Survival Data from Cancer Prevention Studies
Authors: Sinha Debajyoti , Ibrahim Joseph G. , Chen Ming-Hui
Published: March 2003

We propose a new parametric survival model for cancer prevention studies. The formulation of the model is in the spirit of stochastic modelling of the occurrences of tumours through two stages: initiation of an undetected tumour and promotion of the tumour to a detectable cancer. Several novel properties of the model proposed are derived. In addition, we examine the relationship of our model with the existing lagged regression model of Zucker and Lakatos. Also, we bridge the difference between two distinct stochastic modelling methods for cancer data, one used primarily for cancer therapeutic trials and the other used for cancer prevention trials.
Operating Characteristics and Extensions of the False Discovery Rate Procedure
Authors: Genovese Christopher , Wasserman Larry
Published: March 2003

We investigate the operating characteristics of the Benjamini–Hochberg false discovery rate procedure for multiple testing. This is a distribution-free method that controls the expected fraction of falsely rejected null hypotheses among those rejected. The paper provides a framework for understanding more about this procedure. We first study the asymptotic properties of the ‘deciding point' D that determines the critical p-value. From this, we obtain explicit asymptotic expressions for a particular risk function. We introduce the dual notion of false non-rejections and we consider a risk function that combines the false discovery rate and false non-rejections. We also consider the optimal procedure with respect to a measure of conditional risk.
Bayes Model Averaging with Selection of Regressors
Authors: Brown P. J. , Vannucci M. , Fearn T.
Published: March 2003

When a number of distinct models contend for use in prediction, the choice of a single model can offer rather unstable predictions. In regression, stochastic search variable selection with Bayesian model averaging offers a cure for this robustness issue but at the expense of requiring very many predictors. Here we look at Bayes model averaging incorporating variable selection for prediction. This offers similar mean-square errors of prediction but with a vastly reduced predictor space. This can greatly aid the interpretation of the model. It also reduces the cost if measured variables have costs. The development here uses decision theory in the context of the multivariate general linear model. In passing, this reduced predictor space Bayes model averaging is contrasted with single-model approximations. A fast algorithm for updating regressions in the Markov chain Monte Carlo searches for posterior inference is developed, allowing many more variables than observations to be contemplated. We discuss the merits of absolute rather than proportionate shrinkage in regression, especially when there are more variables than observations. The methodology is illustrated on a set of spectroscopic data used for measuring the amounts of different sugars in an aqueous solution.
Modelling Spatially Correlated Data via Mixtures: A Bayesian Approach
Authors: Fernández Carmen , Green Peter J.
Published: March 2003

The paper develops mixture models for spatially indexed data. We confine attention to the case of finite, typically irregular, patterns of points or regions with prescribed spatial relationships, and to problems where it is only the weights in the mixture that vary from one location to another. Our specific focus is on Poisson-distributed data, and applications in disease mapping. We work in a Bayesian framework, with the Poisson parameters drawn from gamma priors, and an unknown number of components. We propose two alternative models for spatially dependent weights, based on transformations of autoregressive Gaussian processes: in one (the logistic normal model), the mixture component labels are exchangeable; in the other (the grouped continuous model), they are ordered. Reversible jump Markov chain Monte Carlo algorithms for posterior inference are developed. Finally, the performances of both of these formulations are examined on synthetic data and real data on mortality from a rare disease.
Least Squares Variogram Fitting by Spatial Subsampling
Authors: Lee Yoon Dong , Lahiri Soumendra N.
Published: March 2003

Least squares methods are popular for fitting valid variogram models to spatial data. The paper proposes a new least squares method based on spatial subsampling for variogram model fitting. We show that the method proposed is statistically efficient among a class of least squares methods, including the generalized least squares method. Further, it is computationally much simpler than the generalized least squares method. The method produces valid variogram estimators under very mild regularity conditions on the underlying random field and may be applied with different choices of the generic variogram estimator without analytical calculation. An extension of the method proposed to a class of spatial regression models is illustrated with a real data example. Results from a simulation study on finite sample properties of the method are also reported.
Pattern–Mixture and Selection Models for Analysing Longitudinal Data with Monotone Missing Patterns
Authors: Birmingham Jolene , Rotnitzky Andrea , Fitzmaurice Garrett M.
Published: March 2003

We examine three pattern–mixture models for making inference about parameters of the distribution of an outcome of interest Y that is to be measured at the end of a longitudinal study when this outcome is missing in some subjects. We show that these pattern–mixture models also have an interpretation as selection models. Because these models make unverifiable assumptions, we recommend that inference about the distribution of Y be repeated under a range of plausible assumptions. We argue that, of the three models considered, only one admits a parameterization that facilitates the examination of departures from the assumption of sequential ignorability. The three models are nonparametric in the sense that they do not impose restrictions on the class of observed data distributions. Owing to the curse of dimensionality, the assumptions that are encoded in these models are sufficient for identification but not for inference. We describe additional flexible and easily interpretable assumptions under which it is possible to construct estimators that are well behaved with moderate sample sizes. These assumptions define semiparametric models for the distribution of the observed data. We describe a class of estimators which, up to asymptotic equivalence, comprise all the consistent and asymptotically normal estimators of the parameters of interest under the postulated semiparametric models. We illustrate our methods with the analysis of data from a randomized clinical trial of contracepting women.
Markov Chain Monte Carlo Methods for Fitting Spatiotemporal Stochastic Models in Plant Epidemiology
Authors: Gibson Gavin J.
Published: March 2003

Strategies for controlling plant epidemics are investigated by fitting continuous time spatiotemporal stochastic models to data consisting of maps of disease incidence observed at discrete times. Markov chain Monte Carlo methods are used for fitting two such models to data describing the spread of citrus tristeza virus (CTV) in an orchard. The approach overcomes some of the difficulties encountered when fitting stochastic models to infrequent observations of a continuous process. The results of the analysis cast doubt on the effectiveness of a strategy identified from a previous spatial analysis of the CTV data. Extensions of the approaches to more general models and other problems are also considered.
Scaling unobservable constructs in social science
Authors: Bartholomew D. J.
Published: March 2003

The use of the logit–probit model for constructing social measurement scales is explored by using two data sets. The first was specifically designed for scaling and consists of answers by 1490 individuals to five questions on economic self-determination. The second is from the Workplace Industrial Relations Survey and relates to answers to six questions addressed to 1005 firms about innovations. It is shown that although scales can be constructed their precision (reliability) is likely to be low. Further, for examples like these, latent class models may give an equally satisfactory account of the data.
The Muscatine Children’s Obesity Data Reanalysed Using Pattern Mixture Models
Authors: Ekholm Anders , Skinner Chris
Published: March 2003

A set of longitudinal binary, partially incomplete, data on obesity among children in the USA is reanalysed. The multivariate Bernoulli distribution is parameterized by the univariate marginal probabilities and dependence ratios of all orders, which together support maximum likelihood inference. The temporal association of obesity is strong and complex but stationary. We fit a saturated model for the distribution of response patterns and find that non-response is missing completely at random for boys but that the probability of obesity is consistently higher among girls who provided incomplete records than among girls who provided complete records. We discuss the statistical and substantive features of, respectively, pattern mixture and selection models for this data set.
Extremal Analysis of Short Series with Outliers: Sea-Levels and Athletics Records
Authors: Barão M. Isabel , Tawn Jonathan. A.
Published: March 2003

The analysis of extreme values is often required from short series which are biasedly sampled or contain outliers. Data for sea-levels at two UK east coast sites and data on athletics records for women’s 3000 m track races are shown to exhibit such characteristics. Univariate extreme value methods provide a poor quantification of the extreme values for these data. By using bivariate extreme value methods we analyse jointly these data with related observations, from neighbouring coastal sites and 1500 m races respectively. We show that using bivariate methods provides substantial benefits, both in these applications and more generally with the amount of information gained being determined by the degree of dependence, the lengths and the amount of overlap of the two series, the homogeneity of the marginal characteristics of the variables and the presence and type of the outlier.
Analysing Drug Abuse with British Crime Survey Data: Modelling and Questionnaire Design Issues
Authors: MacDonald Ziggy , Pudney Stephen
Published: March 2003

We use the British Crime Survey (BCS) to analyse the demand for illicit drugs and the implications of drug use for the probability of subsequent unemployment. We demonstrate that the BCS questionnaire has a serious design flaw for this purpose, and we propose some simple modifications. We also develop a modelling technique that is suitable for existing BCS data and apply it to the 1994 and 1996 samples. We find evidence that soft drug use is associated with a greatly increased probability of later hard drug use and that past drug use is associated with increased probabilities of unemployment.
Space–Time Calibration of Radar Rainfall Data
Authors: Brown Patrick E. , Diggle Peter J. , Lord Martin E. , Young Peter C.
Published: March 2003

Motivated by a specific problem concerning the relationship between radar reflectance and rainfall intensity, the paper develops a space–time model for use in environmental monitoring applications. The model is cast as a high dimensional multivariate state space time series model, in which the cross-covariance structure is derived from the spatial context of the component series, in such a way that its interpretation is essentially independent of the particular set of spatial locations at which the data are recorded. We develop algorithms for estimating the parameters of the model by maximum likelihood, and for making spatial predictions of the radar calibration parameters by using realtime computations. We apply the model to data from a weather radar station in Lancashire, England, and demonstrate through empirical validation the predictive performance of the model.
Joint Analysis of Longitudinal Data Comprising Repeated Measures and Times to Events
Authors: Xu Jane , Zeger Scott L.
Published: March 2003

In biomedical and public health research, both repeated measures of biomarkers Y as well as times T to key clinical events are often collected for a subject. The scientific question is how the distribution of the responses [T, Y|X] changes with covariates X. [T|X] may be the focus of the estimation where Y can be used as a surrogate for T. Alternatively, T may be the time to drop-out in a study in which [Y|X] is the target for estimation. Also, the focus of a study might be on the effects of covariates X on both T and Y or on some underlying latent variable which is thought to be manifested in the observable outcomes. In this paper, we present a general model for the joint analysis of [T, Y|X] and apply the model to estimate [T|X] and other related functionals by using the relevant information in both T and Y. We adopt a latent variable formulation like that of Fawcett and Thomas and use it to estimate several quantities of clinical relevance to determine the efficacy of a treatment in a clinical trial setting. We use a Markov chain Monte Carlo algorithm to estimate the model’s parameters. We illustrate the methodology with an analysis of data from a clinical trial comparing risperidone with a placebo for the treatment of schizophrenia.
Denoising Radiocommunications Signals by Using Iterative Wavelet Shrinkage
Authors: Baxter Paul D. , Upton Graham J. G.
Published: March 2003

Radiocommunications signals pose particular problems in the context of statistical signal processing. This is because short-term fluctuations (noise) are a consequence of atmospheric effects whose characteristics vary in both the short and the longer term. We contrast traditional time domain and frequency domain filters with wavelet methods. We also propose an iterative wavelet procedure which appears to provide benefits over existing wavelet methods.
Effect Modification in a Randomized Trial under Non-Ignorable Non-Compliance: An Application to the Alpha-Tocopherol Beta-Carotene Study
Authors: Korhonen Pasi , Palmgren Juni
Published: March 2003

We propose an inference procedure that allows an estimation of the effects of treatment modifying factors on a survival end point in a placebo-controlled randomized trial with non-ignorable non-compliance. Against prior expectation the alpha-tocopherol beta-carotene (ATBC) study showed an increase in the incidence of lung cancer and total mortality following supplementation with beta-carotene. Any attempt to assess interactions between beta-carotene supplementation and base-line covariates needs to address the issue of non-compliance. In the ATBC study about 25% of the participants dropped out from the active follow-up prematurely for reasons other than death and they stopped receiving the study supplementation. Survival status was observed regardless of drop-out for all participants. Drop-out is related to base-line covariates, to the treatment arm and presumably also to the health status of the individuals through both measured and unmeasured factors. Non-ignorable non-compliance is induced by unmeasured covariates related both to drop-out from active participation in the study and to health status and death. Instead of comparing survival between the randomized groups we focus on an estimation of the effect of beta-carotene actually received and its interaction with base-line covariates such as age, smoking and alcohol consumption. We use an accelerated failure time model to link a potential treatment-free survival time to the actual observed survival time and to the treatment received and its interactions with base-line covariates. We refer to this as the structural model. The estimation of the parameters in the structural model is based on minimizing a score test statistic obtained from the partial likelihood specified for the treatment-free survival time under the structural model. We compare the method proposed with Robins's g-estimation based on a linear rank test statistic. If the structural model holds, then the method proposed can substantially increase efficiency. We propose diagnostic techniques for evaluating the adequacy of the structural model and discuss how a misspecification of the model may induce bias into the score equations.
Latent Class Models for Longitudinal Studies of the Elderly with Data Missing at Random
Authors: Reboussin Beth A , Miller Michael E , Lohman Kurt K , Ten Have Thomas R
Published: March 2003

The elderly population in the USA is expected to double in size by the year 2025, making longitudinal health studies of this population of increasing importance. The degree of loss to follow-up in studies of the elderly, which is often because elderly people cannot remain in the study, enter a nursing home or die, make longitudinal studies of this population problematic. We propose a latent class model for analysing multiple longitudinal binary health outcomes with multiple-cause non-response when the data are missing at random and a non-likelihood-based analysis is performed. We extend the estimating equations approach of Robins and co-workers to latent class models by reweighting the multiple binary longitudinal outcomes by the inverse probability of being observed. This results in consistent parameter estimates when the probability of non-response depends on observed outcomes and covariates (missing at random) assuming that the model for non-response is correctly specified. We extend the non-response model so that institutionalization, death and missingness due to failure to locate, refusal or incomplete data each have their own set of non-response probabilities. Robust variance estimates are derived which account for the use of a possibly misspecified covariance matrix, estimation of missing data weights and estimation of latent class measurement parameters. This approach is then applied to a study of lower body function among a subsample of the elderly participating in the 6-year Longitudinal Study of Aging.
CONSISTENCY OF THE AVERAGED CROSS‐PERIODOGRAM IN LONG MEMORY SERIES
Authors: Lobato Ignacio N.
Published: March 2003

Several aspects of inference with long memory series in a multivariate framework are examined. The main result of this paper is to prove the consistency of the averaged cross‐ periodogram evaluated in a degenerating neighbourhood of zero frequency. We also illustrate several applications of that result and consider some specification issues.
One‐sided testing for conditional heteroskedasticity in time series models
Authors: Hong Yongmiao
Published: March 2003

Engle’s autoregressive conditional heteroskedasticity (ARCH) model and its various generalizations have been widely used to model the volatility of economic and financial time series. Most existing ARCH tests fail to exploit the one‐sided nature of the alternative hypothesis. Lee and King (A locally most mean powerful based score test for ARCH and GARCH regression disturbances. J. Bus. Econ. Stat. 11 (1993), 17–27) recently proposed a locally most mean powerful score‐based one‐ sided test for ARCH effects. In this paper a new one‐sided test for ARCH effects of the disturbance of a dynamic regression model is proposed. The test is based on a weighted sum of sample autocorrelations of squared regression residuals, with the weighting function typically giving more weight to lower orders of lags and less weight to higher orders of lags. Lee and King’s (1993) test can be viewed as a special case of the present approach with the use of uniform weighting. Many non‐uniform weighting schemes deliver better power than uniform weighting; the efficiency gain is substantial when a relatively long lag is used. A simulation experiment confirms the gains from exploiting the one‐sided nature of the alternative hypothesis and from using non‐uniform weighting.
Detection of Periodic Autocorrelation in Time Series Data via Zero‐Crossings
Authors: Martin Donald E. K.
Published: March 2003

A statistical procedure for detection of periodic autocorrelation in time series data is presented. Intuitively, the probability of a zero‐crossing at time t should be inversely related to the correlation between observations at times t and t− 1. Explicit formulas displaying this inverse relationship are given for mean‐zero periodically correlated time series with certain distributional structures. A test statistic based on this relationship is developed. This testing method provides a robust approach to detection of periodic autocorrelation. Analysis of simulated and actual data sets illustrates the usefulness of the proposed method.