bluuebunny/crossref_metadata_2025_split huggingface dataset:
Phonological processing and early literacy
Authors: Passenger Terri , Stuart Morag , Terrell Colin 
Published: March 2003

Phonological awareness and phonological memory have been cited separately as two cognitive skills thought to underpin literacy. Few studies, however, have investigated the relationship between these two skills and their relative contribution to early reading and spelling. The aims of this longitudinal study were to evaluate the developmental relationship between these two phonological processing skills and to consider their relative contributions to early literacy.This paper reports results from the first 12 months of the study, which monitored 80 preliterate children during their first year of formal schooling. The findings discussed here suggest that phonological awareness and phonological memory both make significant yet distinctive contributions to early literacy: while early phonological awareness may predict subsequent single‐word reading, early phonological memory appears to play an important part in the development of the decoding strategies needed for later reading. Evidence that a qualitative change in phonological memory takes place during the first year of formal schooling confirms earlier claims that a phonological strategy for spelling may develop before a similar strategy for reading (Frith, 1985; Huxford, 1993).
Producing Monthly Estimates of Unemployment and Employment According to the International Labour Office Definition
Authors: Steel David. 
Published: March 2003

Summary
Monthly unemployment statistics are available in Britain from a monthly count of the number of people claiming unemployment-related benefits. There has been considerable debate on the appropriateness of this measure. Unemployment and employment statistics are available quarterly from the Labour Force Survey (LFS), using International Labour Office (ILO) definitions. In this paper various options for producing monthly unemployment estimates according to the ILO definition are examined. Methods considered are a monthly LFS, calculating rolling averages from the quarterly LFS, and methods which combine LFS and claimant count data. It is proposed that a monthly LFS of 60 000 households be introduced which can produce monthly estimates of total unemployment and more detailed estimates quarterly. Such a survey would also fill an important gap by providing monthly employment statistics which are needed to provide a complete picture of the labour market.
Bayesian Updating in Reference Centile Charts
Authors: Fatti L. P. , Senaoana E. M. , Thompson M. L. 
Published: March 2003

Summary
Reference centile charts, used for monitoring the health of an individual over time (e.g. the weight gain of pregnant women over successive periods in their pregnancy) do not take into account the longitudinal nature of the individual profiles. There is also generally more than one variable which affects the outcome of interest, and information regarding the path of a group of variables over time may prove advantageous in terms of sensitivity. We propose a Bayesian approach to this problem in which the successive deviations of the individual’s observations from the mean of the corresponding reference distribution are used to compute updated reference centiles for future measurements on the individual. The univariate and multivariate situations are discussed, and consideration is given to non-normal cross-sectional distributions. The theory is illustrated on data obtained from the records of weight gain and fundal height of pregnant women visiting a clinic at intervals during pregnancy.
A Methodological Strategy for a One-Number Census in the UK
Authors: Brown J. J. , Diamond I. D. , Chambers R. L. , Buckner L. J. , Teague A. D. 
Published: March 2003

Summary
As a result of lessons learnt from the 1991 census, a research programme was set up to seek improvements in census methodology. Underenumeration has been placed top of the agenda in this programme, and every effort is being made to achieve as high a coverage as possible in the 2001 census. In recognition, however, that 100% coverage will never be achieved, the one-number census (ONC) project was established to measure the degree of underenumeration in the 2001 census and, if possible, to adjust fully the outputs from the census for that undercount. A key component of this adjustment process is a census coverage survey (CCS). This paper presents an overview of the ONC project, focusing on the design and analysis methodology for the CCS. It also presents results that allow the reader to evaluate the robustness of this methodology.
Ethnic Differences in the Labour Market: A Comparison of the Samples of Anonymized Records and Labour Force Survey
Authors: Heath Anthony , McMahon Dorren , Roberts Jane 
Published: March 2003

Summary
The public use sample from the 1991 UK census makes it possible to conduct individual level analyses of ethnic minorities’ educational and occupational attainments. Unfortunately, however, the census asked only about higher level qualifications obtained after reaching 18 years of age. A comparison with the Labour Force Surveys (LFSs) shows that the census gives in some respects a misleading impression of qualifications among the first-generation members of ethnic minorities: the LFS data show that ethnic minorities tend to be more polarized in their qualifications than the British-born whites, with relatively large proportions at the two extremes, either with degrees or with no qualifications at all. It follows that the census’s treatment of qualifications may tend to exaggerate the scale of disadvantage of ethnic minorities in the labour market, and particularly in access to the salariat where qualifications play a particularly large role in recruitment. Regression analyses of sample of anonymized records and LFS data confirm these expectations although they indicate that the results of the census are not seriously misleading as regards the pattern of ethnic disadvantages in the competition to avoid unemployment. The LFS data also confirm earlier findings that the ethnic penalties are in general of similar magnitude among the second generation to those among the first generation, despite the substantial equalization of educational experience that has taken place. There is some evidence that disadvantages in access to the salariat may have been reduced, but this is counterbalanced by the evidence that disadvantages in the avoidance of unemployment may have deteriorated.
Bayesian Analysis of Mortality Data
Authors: Dellaportas Petros , Smith Adrian F. M. , Stavropoulos Photis 
Published: March 2003

Summary
Congdon argued that the use of parametric modelling of mortality data is necessary in many practical demographical problems. In this paper, we focus on a form of model introduced by Heligman and Pollard in 1980, and we adopt a Bayesian analysis, using Markov chain Monte Carlo simulation, to produce the posterior summaries required. This opens the way to richer, more flexible inference summaries and avoids the numerical problems that are encountered with classical methods. Particular methodologies to cope with incomplete life-tables and a derivation of joint lifetimes, median times to death and related quantities of interest are also presented.
Commissioned Analysis of Surgical Performance Using Routine Data: Lessons from the Bristol Inquiry
Authors: Spiegelhalter David J. , Aylin Paul , Best Nicola G. , Evans Stephen J. W. , Murray Gordon D. 
Published: March 2003

SummaryThe public inquiry into paediatric cardiac surgery at the Bristol Royal Infirmary commissioned the authors to design and conduct analyses of routine data sources to compare surgical outcomes between centres. Such analyses are necessarily complex in this context but were further hampered by the inherent inconsistencies and mediocre quality of the various sources of data. Three levels of analysis of increasing sophistication were carried out. The reasonable consistency of the results arising from different sources of data, together with a number of sensitivity analyses, led us to conclude that there had been excess mortality in Bristol in open heart operations on children under 1 year of age. We consider criticisms of our analysis and discuss the role of statisticians in this inquiry and their contribution to the final report of the inquiry. The potential statistical role in future programmes for monitoring clinical performance is highlighted.
Bayesian Multivariate Spatial Interpolation with Data Missing by Design
Authors: Le Nhu D. , Sun Weimin , Zidek James V. 
Published: March 2003

Summary
In a network of s g sites, responses like levels of airborne pollutant concentrations may be monitored over time. The sites need not all measure the same set of response items and unmeasured items are considered as data missing by design. We propose a hierarchical Bayesian approach to interpolate the levels of, say, k responses at s u other locations called ungauged sites and also the unmeasured levels of the k responses at the gauged sites. Our method involves two steps. First, when all hyperparameters are assumed to be known, a predictive distribution is derived. In turn, an interpolator, its variance and a simultaneous interpolation region are obtained. In step two, we propose the use of an empirical Bayesian approach to estimate the hyperparameters through an EM algorithm. We base our theory on a linear Gaussian model and the relationship between a multivariate normal and matrix T-distribution. Our theory allows us to pool data from several existing networks that measure different subsets of response items for interpolation.
Latent Variable Models for Mixed Discrete and Continuous Outcomes
Authors: Sammel Mary Dupuis , Ryan Louise M. , Legler Julie M. 
Published: March 2003

Summary
We propose a latent variable model for mixed discrete and continuous outcomes. The model accommodates any mixture of outcomes from an exponential family and allows for arbitrary covariate effects, as well as direct modelling of covariates on the latent variable. An EM algorithm is proposed for parameter estimation and estimates of the latent variables are produced as a by-product of the analysis. A generalized likelihood ratio test can be used to test the significance of covariates affecting the latent outcomes. This method is applied to birth defects data, where the outcomes of interest are continuous measures of size and binary indicators of minor physical anomalies. Infants who were exposed in utero to anticonvulsant medications are compared with controls.
Second-derivative Functional Regression with Applications to Near Infra-red Spectroscopy
Authors: Goutis Constantinos 
Published: March 2003

Summary
A linear regression method to predict a scalar from a discretized smooth function is presented. The method takes into account the functional nature of the predictors and the importance of the second derivative in spectroscopic applications. This motivates a functional inner product that can be used as a roughness penalty. Using this inner product, we derive a linear prediction method that is similar to ridge regression but with different shrinkage characteristics. We describe its practical implementation and we address the problem of computing the second derivatives nonparametrically. We apply the method to a calibration example using near infra-red spectra. We conclude with a discussion comparing our approach with other regression algorithms.
Curve Registration
Authors: Ramsay J. O. , Li Xiaochun 
Published: March 2003

Summary
Functional data analysis involves the extension of familiar statistical procedures such as principal components analysis, linear modelling, and canonical correlation analysis to data where the raw observation xi is a function. An essential preliminary to a functional data analysis is often the registration or alignment of salient curve features by suitable monotone transformations hi of the argument t, so that the actual analyses are carried out on the values xi{hi(t)}. This is referred to as dynamic time warping in the engineering literature. In effect, this conceptualizes variation among functions as being composed of two aspects: horizontal and vertical, or domain and range. A nonparametric function estimation technique is described for identifying the smooth monotone transformations hi, and is illustrated by data analyses. A second-order linear stochastic differential equation is proposed to model these components of variation.
Triple-goal Estimates in Two-stage Hierarchical Models
Authors: Shen Wei , Louis Thomas A. 
Published: March 2003

Summary
The beauty of the Bayesian approach is its ability to structure complicated models, inferential goals and analyses. To take full advantage of it, methods should be linked to an inferential goal via a loss function. For example, in the two-stage, compound sampling model the posterior means are optimal under squared error loss. However, they can perform poorly in estimating the histogram of the parameters or in ranking them. ‘Triple-goal’ estimates are motivated by the desire to have a set of estimates that produce good ranks, a good parameter histogram and good co-ordinate-specific estimates. No set of estimates can simultaneously optimize these three goals and we seek a set that strikes an effective trade-off. We evaluate and compare three candidate approaches: the posterior means, the constrained Bayes estimates of Louis and Ghosh, and a new approach that optimizes estimation of the histogram and the ranks. Mathematical and simulation-based analyses support the superiority of the new approach and document its excellent performance for the three inferential goals.
Local Maximum Likelihood Estimation and Inference
Authors: Fan Jianqing , Farmen Mark , Gijbels Irene 
Published: March 2003

Summary
Local maximum likelihood estimation is a nonparametric counterpart of the widely used parametric maximum likelihood technique. It extends the scope of the parametric maximum likelihood method to a much wider class of parametric spaces. Associated with this nonparametric estimation scheme is the issue of bandwidth selection and bias and variance assessment. This paper provides a unified approach to selecting a bandwidth and constructing confidence intervals in local maximum likelihood estimation. The approach is then applied to least squares nonparametric regression and to nonparametric logistic regression. Our experiences in these two settings show that the general idea outlined here is powerful and encouraging.
On the Effect of Overdispersion On Exact Conditional Tests
Authors: Severini Thomas A. 
Published: March 2003

Summary
Let Y 1, . . ., Yn denote independent random variables such that Yj has a one-parameter exponential family distribution with canonical parameter θj=λ+ψXj; here X 1, . . ., Xn are known constants. Consider a test of the null hypothesis ψ=0. Under the null hypothesis, A=ΣYj is sufficient for λ and, hence, a test of ψ=0 may be based on the conditional distribution of T=ΣXj Yj given A, which is independent of λ. In this paper, the effects of overdispersion due to a mixture model on the conditional distribution of T given A are considered.
Inference in Molecular Population Genetics
Authors: Stephens Matthew , Donnelly Peter 
Published: March 2003

Summary
Full likelihood-based inference for modern population genetics data presents methodological and computational challenges. The problem is of considerable practical importance and has attracted recent attention, with the development of algorithms based on importance sampling (IS) and Markov chain Monte Carlo (MCMC) sampling. Here we introduce a new IS algorithm. The optimal proposal distribution for these problems can be characterized, and we exploit a detailed analysis of genealogical processes to develop a practicable approximation to it. We compare the new method with existing algorithms on a variety of genetic examples. Our approach substantially outperforms existing IS algorithms, with efficiency typically improved by several orders of magnitude. The new method also compares favourably with existing MCMC methods in some problems, and less favourably in others, suggesting that both IS and MCMC methods have a continuing role to play in this area. We offer insights into the relative advantages of each approach, and we discuss diagnostics in the IS framework.
Improved Small Sample Inference in the Mixed Linear Model: Bartlett Correction and Adjusted Likelihood
Authors: Zucker David M. , Lieberman Offer , Manor Orly 
Published: March 2003

Summary
The mixed linear model is a popular method for analysing unbalanced repeated measurement data. The classical statistical tests for parameters in this model are based on asymptotic theory that is unreliable in the small samples that are often encountered in practice. For testing a given fixed effect parameter with a small sample, we develop and investigate refined likelihood ratio (LR) tests. The refinements considered are the Bartlett correction and use of the Cox–Reid adjusted likelihood; these are examined separately and in combination. We illustrate the various LR tests on an actual data set and compare them in two simulation studies. The conventional LR test yields type I error rates that are higher than nominal. The adjusted LR test yields rates that are lower than nominal, with absolute accuracy similar to that of the conventional LR test in the first simulation study and better in the second. The Bartlett correction substantially improves the accuracy of the type I error rates with either the conventional or the adjusted LR test. In many cases, error rates that are very close to nominal are achieved with the refined methods.
Epidemics in Heterogeneous Communities: Estimation of R0 and Secure Vaccination Coverage
Authors: Britton Tom 
Published: March 2003

Summary
A stochastic multitype model for the spread of an infectious disease in a community of heterogeneous individuals is analysed. In particular, estimates of R 0 (the basic reproduction number) and the critical vaccination coverage are derived, where estimation is based on final size data of an outbreak in the community. It is shown that these key parameters cannot be estimated consistently from data; only upper and lower bounds can be estimated. Confidence regions for the upper bounds are derived, thus giving conservative estimates of R 0 and the fractions necessary to vaccinate.
Graphical Techniques for Selecting Explanatory Variables for Time Series Data
Authors: Marriott J. M. , Pettitt A. N. 
Published: March 2003

SUMMARY
Bayesian model building techniques are developed for data with a strong time series structure and possibly exogenous explanatory variables that have strong explanatory and predictive power. The emphasis is on finding whether there are any explanatory variables that might be used for modelling if the data have a strong time series structure that should also be included. We use a time series model that is linear in past observations and that can capture both stochastic and deterministic trend, seasonality and serial correlation. We propose the plotting of absolute predictive error against predictive standard deviation. A series of such plots is utilized to determine which of several nested and non-nested models is optimal in terms of minimizing the dispersion of the predictive distribution and restricting predictive outliers. We apply the techniques to modelling monthly counts of fatal road crashes in Australia where economic, consumption and weather variables are available and we find that three such variables should be included in addition to the time series filter. The approach leads to graphical techniques to determine strengths of relationships between the dependent variable and covariates and to detect model inadequacy as well as determining useful numerical summaries.
Bayesian Analysis of Two-Component Mixture Distributions Applied to Estimating Malaria Attributable Fractions
Authors: Vounatsou P. , Smith T. , Smith A. F. M. 
Published: March 2003

SUMMARY
Malaria illness can be diagnosed by the presence of fever and parasitaemia. However, in highly endemic areas the diagnosis of clinical malaria can be difficult since children may tolerate parasites without fever and may have fever due to other causes. We propose a novel, simulation-based Bayesian approach for obtaining precise estimates of the probabilities of children with different levels of parasitaemia having fever due to malaria, by formulating the problem as a mixture of distributions. The methodology suggested is a general methodology for decomposing any two-component mixture distribution nonparametrically, when an independent training sample is available from one of the components. It is based on the assumption that one of the component distributions lies on the left of the other but there is some overlap between the distributions.
Analysis of Distance for Structured Multivariate Data and Extensions to Multivariate Analysis of Variance
Authors: Gower J. C. , Krzanowski W. J. 
Published: March 2003

SUMMARY
Many data sets in practice fit a multivariate analysis of variance (MANOVA) structure but are not consonant with MANOVA assumptions. One particular such data set from economics is described. This set has a 24 factorial design with eight variables measured on each individual, but the application of MANOVA seems inadvisable given the highly skewed nature of the data. To establish a basis for analysis, we examine the structure of distance matrices in the presence of a priori grouping of units and show how the total squared distance among the units of a multivariate data set can be partitioned according to the factors of an external classification. The partitioning is exactly analogous to that in the univariate analysis of variance. It therefore provides a framework for the analysis of any data set whose structure conforms to that of MANOVA, but which for various reasons cannot be analysed by this technique. Descriptive aspects of the technique are considered in detail, and inferential questions are tackled via randomization tests. This approach provides a satisfactory analysis of the economics data.
Multichannel Electroencephalographic Analyses via Dynamic Regression Models with Time-Varying Lag–Lead Structure
Authors: Prado Raquel , West Mike , Krystal Andrew D. 
Published: March 2003

SUMMARY
Multiple time series of scalp electrical potential activity are generated routinely in electroencephalographic (EEG) studies. Such recordings provide important non-invasive data about brain function in human neuropsychiatric disorders. Analyses of EEG traces aim to isolate characteristics of their spatiotemporal dynamics that may be useful in diagnosis, or may improve the understanding of the underlying neurophysiology or may improve treatment through identifying predictors and indicators of clinical outcomes. We discuss the development and application of non-stationary time series models for multiple EEG series generated from individual subjects in a clinical neuropsychiatric setting. The subjects are depressed patients experiencing generalized tonic–clonic seizures elicited by electroconvulsive therapy (ECT) as antidepressant treatment. Two varieties of models—dynamic latent factor models and dynamic regression models—are introduced and studied. We discuss model motivation and form, and aspects of statistical analysis including parameter identifiability, posterior inference and implementation of these models via Markov chain Monte Carlo techniques. In an application to the analysis of a typical set of 19 EEG series recorded during an ECT seizure at different locations over a patient’s scalp, these models reveal time-varying features across the series that are strongly related to the placement of the electrodes. We illustrate various model outputs, the exploration of such time-varying spatial structure and its relevance in the ECT study, and in basic EEG research in general.