bluuebunny/crossref_metadata_2025_split huggingface dataset:
On Repeated Measures Analysis with Misspecified Covariance Structure
Authors: Crowder Martin 
Published: March 2003

Summary
In recent years various sophisticated methods have been developed for the analysis of repeated measures, or longitudinal data. The more traditional approach, based on a normal likelihood function, has been shown to be unsatisfactory, in the sense of yielding asymptotically biased estimates when the covariance structure is misspecified. More recent methodology, based on generalized linear models and quasi-likelihood estimation, has gained widespread acceptance as ‘generalized estimating equations’. However, this also has theoretical problems. In this paper a suggestion is made for improving the asymptotic behaviour of estimators by using the older approach, implemented via Gaussian estimation. The resulting estimating equations include the quasi-score function as one component, so the methodology proposed can be viewed as a combination of Gaussian estimation and generalized estimating equations which has a firmer asymptotic basis than either alone has.
Robust Projection Indices
Authors: Nason Guy P. 
Published: March 2003

Summary
Loosely speaking a robust projection index is one that prefers projections involving true clusters over projections consisting of a cluster and an outlier. We introduce a mathematical definition of one-dimensional index robustness and describe a numerical experiment to measure it. We design five new indices based on measuring divergence from Student's t-distribution which are intended to be especially robust: the experiment shows that they are more robust than several established indices. The experiment also reveals more generally that the robustness of moment indices depends on the number of approximation terms, providing additional practical guidance for existing projection pursuit implementations. We investigate the theoretical properties of one new Student t-index and Hall's index and show that the new index automatically adapts its robustness to the degree of outlier contamination. We conclude by outlining the possibilities for extending our experiments to both higher dimensions and other new indices.
Generalized Linear Models with Functional Predictors
Authors: James Gareth M. 
Published: March 2003

SummaryWe present a technique for extending generalized linear models to the situation where some of the predictor variables are observations from a curve or function. The technique is particularly useful when only fragments of each curve have been observed. We demonstrate, on both simulated and real data sets, how this approach can be used to perform linear, logistic and censored regression with functional predictors. In addition, we show how functional principal components can be used to gain insight into the relationship between the response and functional predictors. Finally, we extend the methodology to apply generalized linear models and principal components to standard missing data problems.
Thin Plate Regression Splines
Authors: Wood Simon N. 
Published: March 2003

SummaryI discuss the production of low rank smoothers for d ≥ 1 dimensional data, which can be fitted by regression or penalized regression methods. The smoothers are constructed by a simple transformation and truncation of the basis that arises from the solution of the thin plate spline smoothing problem and are optimal in the sense that the truncation is designed to result in the minimum possible perturbation of the thin plate spline smoothing problem given the dimension of the basis used to construct the smoother. By making use of Lanczos iteration the basis change and truncation are computationally efficient. The smoothers allow the use of approximate thin plate spline models with large data sets, avoid the problems that are associated with ‘knot placement’ that usually complicate modelling with regression splines or penalized regression splines, provide a sensible way of modelling interaction terms in generalized additive models, provide low rank approximations to generalized smoothing spline models, appropriate for use with large data sets, provide a means for incorporating smooth functions of more than one variable into non-linear models and improve the computational efficiency of penalized likelihood models incorporating thin plate splines. Given that the approach produces spline-like models with a sparse basis, it also provides a natural way of incorporating unpenalized spline-like terms in linear and generalized linear models, and these can be treated just like any other model terms from the point of view of model selection, inference and diagnostics.
The Effect of Treatment Compliance in a Placebo-Controlled Trial: Regression with Unpaired Data
Authors: Goetghebeur Els , Lapp Krista 
Published: March 2003

SUMMARY
The causal effect of a treatment is estimated at different levels of treatment compliance, in a placebo-controlled trial on the reduction of blood pressure. The structural nested mean model makes no direct assumptions on selected treatment compliance levels and placebo prognosis but relies on the randomization assumption and a parametric form for causal effects. It can be seen as a regression model for unpaired data, where pre- and post-randomization covariables are treated differently. The causal parameters are found as solutions to estimating equations involving estimated placebo response and treatment compliance based on base-line covariates for all subjects. Our example considers a linear effect of the percentage of prescribed dose taken on achieved diastolic blood pressure reduction. We propose an exploration of structural model checks. In the example, this reveals an interaction between the causal effect of active dose taken and the base-line body weight of the patient.
Nonparametric Density Estimation and Discrimination from Images of Shapes
Authors: Wright David , Stander Julian , Nicolaides Kypros 
Published: March 2003

SUMMARY
Nonparametric density estimation is the basis for a new methodology for discrimination using shape data in the form of pixel images. Our work is driven by an application based on screening for neural tube defects from ultrasonography data that comprise binary pixel images of head shapes from human fetuses. We discuss the choice of smoothing parameters used for the density estimates, the variation that is inherent in our method and how our approach could be extended to take into account other discriminatory information. We compare our method based on density estimates with alternative approaches such as those based on Fourier descriptors.
A Biomedical Application of Latent Class Models with Random Effects
Authors: Hadgu Alula , Qu Yinsheng 
Published: March 2003

SUMMARY
Traditional latent class modelling has been used in many biomedical settings. Unfortunately, many of these applications assume that the diagnostic tests are independent given the true disease status, an assumption that is often violated in practice. Qu, Tan and Kutner developed general latent class models with random effects to model the conditional dependence among multiple diagnostic tests. In this paper latent class modelling with random effects is used to estimate the sensitivity and specificity of six screening tests for detecting Chlamydia trachomatis in endocervical specimens from women attending family planning clinics.
The Effect of Non-Stationarity on Extreme Sea-Level Estimation
Authors: Dixon Mark J. , Tawn Jonathan A. 
Published: March 2003

SUMMARY
The sea-level is the composition of astronomical tidal and meteorological surge processes. It exhibits temporal non-stationarity due to a combination of long-term trend in the mean level, the deterministic tidal component, surge seasonality and interactions between the tide and surge. We assess the effect of these non-stationarities on the estimation of the distribution of extreme sea-levels. This is important for coastal flood assessment as the traditional method of analysis assumes that, once the trend has been removed, extreme sea-levels are from a stationary sequence. We compare the traditional approach with a recently proposed alternative that incorporates the knowledge of the tidal component and its associated interactions, by applying them to 22 UK data sites and through a simulation study. Our main finding is that if the tidal non-stationarity is ignored then a substantial underestimation of extreme sea-levels results for most sites. In contrast, if surge seasonality and the tide–surge interaction are not modelled the traditional approach produces little additional bias. The alternative method is found to perform well but requires substantially more statistical modelling and better data quality.
Design and Analysis of Two-Phase Studies with Binary Outcome Applied to Wilms Tumour Prognosis
Authors: Breslow N. E. , Chatterjee N. 
Published: March 2003

SUMMARY
Two-phase stratified sampling is used to select subjects for the collection of additional data, e.g. validation data in measurement error problems. Stratification jointly by outcome and covariates, with sampling fractions chosen to achieve approximately equal numbers per stratum at the second phase of sampling, enhances efficiency compared with stratification based on the outcome or covariates alone. Nonparametric maximum likelihood may result in substantially more efficient estimates of logistic regression coefficients than weighted or pseudolikelihood procedures. Software to implement all three procedures is available. We demonstrate the practical importance of these design and analysis principles by an analysis of, and simulations based on, data from the US National Wilms Tumor Study.
Using Orientation Statistics to Investigate Variations in Human Kinematics
Authors: Rancourt Denis , Rivest Louis-Paul , Asselin Jérôm 
Published: March 2003

SUMMARY
This paper applies orientation statistics to investigate variations in upper limb posture of human subjects drilling at six different locations on a vertical panel. Some of the drilling locations are kinematically equivalent in that the same posture could be used for these locations. Upper limb posture is measured by recording the co-ordinates of four markers attached to the subjects hand, forearm, arm and torso. A 3×3 rotation characterizes the relative orientation of one body segment with respect to another. Replicates are available since each subject drilled at the same location five times. Upper limb postures for the six drilling locations are compared by one-way analysis-of-variance tests for rotations. These tests rely on tangent space approximations at the estimated modal rotation of the sample. A parameterization of rotations in terms of unit quaternions simplifies the computations. The analysis detects significant differences in posture between all pairs of drilling locations. The smallest changes, less than 10° at all joints, are obtained for the kinematically equivalent pairs of locations. A short discussion of the biomechanical interpretation of these findings is presented.
Bias in Meta-Analysis Due to Outcome Variable Selection Within Studies
Authors: Hutton J. L. , Williamson Paula R. 
Published: March 2003

SUMMARY
Although bias in meta-analysis arising from selective publication has been studied, within-study selection has received little attention. Chronic diseases often have several possible outcome variables. Methods based on the size of the effect allow results from studies with different outcomes to be combined. However, the possibility of selective reporting of outcomes must be considered. The effect of selective reporting on estimates of the size of the effect and significance levels is presented, and sensitivity analyses are suggested. Substantial publication bias could arise from multiple testing of outcomes in a study, followed by selective reporting. Two meta-analyses, on anthelminth therapy and a treatment for incontinence, are reassessed allowing for within-study selection, as it is clear that more outcomes had been measured than were reported. The sensitivity analyses show that the robustness of the anthelminth results is dependent on what assumption one makes about the reporting strategy for the largest trial. The possible influence of correlation between within-child measurements was such that the conclusions could easily be reversed. The effect of a mild assumption on within-trial selection alone could alter general recommendations about the treatment for incontinence.
Bayesian Volumetric Calibration: An Application of Reversible Jump Markov Chain Monte Carlo Methods
Authors: Henderson Robin , Morton-Jones Tony , McKnespiey Paul 
Published: March 2003

SUMMARY
Volumetric calibration is used for high accuracy estimation of the functional volume-to-level relationship for vessels used to store hazardous liquids. An example of calibration of a vessel for reprocessed nuclear material is used to illustrate an analysis of calibration data. We advocate a Bayesian approach, with proper account of genuine prior information, using a reversible jump Markov chain Monte Carlo method for estimation.
Monitoring Processes with Data Censored Owing to Competing Risks by Using Exponentially Weighted Moving Average Control Charts
Authors: Steiner Stefan H. , MacKay R. Jock 
Published: March 2003

SUMMARY
In industry, process monitoring is widely employed to detect process changes rapidly. However, in some industrial applications observations are censored. For example, when testing breaking strengths and failure times often a limited stress test is performed. With censored observations, a direct application of traditional monitoring procedures is not appropriate. When the censoring occurs due to competing risks, we propose a control chart based on conditional expected values to detect changes in the mean strength. To protect against possible confounding caused by changes in the mean of the censoring mechanism we also suggest a similar chart to detect changes in the mean censoring level. We provide an example of monitoring bond strength to illustrate the application of this methodology.
Using Auxiliary Data for Parameter Estimation with Non-Ignorably Missing Outcomes
Authors: Ibrahim Joseph G. , Lipsitz Stuart R. , Horton Nick 
Published: March 2003

SUMMARY
We propose a method for estimating parameters in generalized linear models when the outcome variable is missing for some subjects and the missing data mechanism is non-ignorable. We assume throughout that the covariates are fully observed. One possible method for estimating the parameters is maximum likelihood with a non-ignorable missing data model. However, caution must be used when fitting non-ignorable missing data models because certain parameters may be inestimable for some models. Instead of fitting a non-ignorable model, we propose the use of auxiliary information in a likelihood approach to reduce the bias, without having to specify a non-ignorable model. The method is applied to a mental health study.
Radiocarbon Dating with Temporal Order Constraints
Authors: Nicholls Geoff , Jones Martin 
Published: March 2003

SUMMARY
Bayesian methods are now widely used for analysing radiocarbon dates. We find that the non-informative priors in use in the literature generate a bias towards wider date ranges which does not in general reflect substantial prior knowledge. We recommend using a prior in which the distribution of the difference between the earliest and latest dates has a uniform distribution. We show how such priors are derived from a simple physical model of the deposition and observation process. We illustrate this in a case-study, examining the effect that various priors have on the reconstructed dates. Bayes factors are used to help to decide model choice problems.
Geoadditive Models
Authors: Kammann E. E. , Wand M. P. 
Published: March 2003

SummaryA study into geographical variability of reproductive health outcomes (e.g. birth weight) in Upper Cape Cod, Massachusetts, USA, benefits from geostatistical mapping or kriging. However, also observed are some continuous covariates (e.g. maternal age) that exhibit pronounced non-linear relationships with the response variable. To account for such effects properly we merge kriging with additive models to obtain what we call geoadditive models. The merging becomes effortless by expressing both as linear mixed models. The resulting mixed model representation for the geoadditive model allows for fitting and diagnosis using standard methodology and software.
Bayesian Texture Segmentation of Weed and Crop Images Using Reversible Jump Markov Chain Monte Carlo Methods
Authors: Dryden Ian L. , Scarr Mark R. , Taylor Charles C. 
Published: March 2003

SummaryA Bayesian method for segmenting weed and crop textures is described and implemented. The work forms part of a project to identify weeds and crops in images so that selective crop spraying can be carried out. An image is subdivided into blocks and each block is modelled as a single texture. The number of different textures in the image is assumed unknown. A hierarchical Bayesian procedure is used where the texture labels have a Potts model (colour Ising Markov random field) prior and the pixels within a block are distributed according to a Gaussian Markov random field, with the parameters dependent on the type of texture. We simulate from the posterior distribution by using a reversible jump Metropolis–Hastings algorithm, where the number of different texture components is allowed to vary. The methodology is applied to a simulated image and then we carry out texture segmentation on the weed and crop images that motivated the work.
Extending Life: Scientific Prospects and Political Obstacles
Authors: Miller Richard A. 
Published: March 2003

In the last two decades, biogerontologists have found that the pace of aging can be decelerated in mammals by dietary or genetic means. Though still largely unappreciated by the lay and scientific public alike, these discoveries belie the common assumption that human aging probably cannot be slowed and prompt the question of whether we can use our growing knowledge about aging to produce 90‐year‐old adults who are as healthy and active as today's 50‐year‐olds. This article explores three related issues: first, why discussions of antiaging medicines are no longer silly; next, why the development of antiaging strategies is making so little headway; and last, whether further work in this area would be worthwhile.By “aging” I mean a process that converts healthy young adults into less healthy older ones with an increasing risk of illness and death. This definition, which seems fairly innocuous, is careful enough to be controversial, because there are many interesting areas of investigation—studies of childhood development; specific late‐life diseases; death or proliferative ennui of cells in culture; leaf abscission; and time‐dependent changes in the chemical composition of wines, cheeses, and bones—that are sometimes proposed in various combinations as worthy of gerontological attention and governmental funding.
Disability as a Public Health Issue: Findings and Reflections from the Massachusetts Survey of Secondary Conditions
Authors: Wilber Nancy , Mitra Monika , Walker Deborah Klein , Allen Deborah 
Published: March 2003

In the last decade, the health status of individuals with disabilities has emerged as an explicit focus of public health attention, with consumers, policymakers, and researchers joining in defining and implementing an agenda in this area. With small federal grants from a few agencies, the field has begun to produce research findings, academic curricula, and model policies and interventions to promote health and prevent adverse outcomes among people with disabilities (Andresen, Lollar, and Meyers 2000; Lollar 1994; Pope 1992; Seekins, White, Ravesloot, et al. 1999; Simeonsson and McDevitt 1999; Tanenhaus, Meyers, and Harbison 2000). The development of the term secondary conditions and the inclusion of the chapter “Disability and Secondary Conditions” in Healthy People 2010 (U.S. Dept. of Health and Human Services 2000) reflect this progress toward recognizing disability as a national public health issue.Public health researchers and practitioners have begun to recognize the dynamic nature of disability, promote the health of people with disabilities, and develop strategies to prevent secondary conditions among them. To understand the epidemiology of secondary conditions, the authors developed the Massachusetts Survey of Secondary Conditions, a longitudinal study of adults with major disabilities in = 656) based on a conceptual framework linking disability, mediating factors, and health outcomes. This paper reports baseline data on the number of secondary conditions experienced by survey respondents. Respondents experienced a mean of 5.3 of 17 secondary conditions. More numerous secondary conditions were associated with fair or poor general health and number of days unable to do routine activities. Factors amenable to public health interventions included difficulty with weight and exercise maintenance, tobacco and marijuana use, and experiencing assault. Disability should be a focus in all public health research, policy, and programs.
Maternal Labour Supply and Child Nutrition in West Africa
Authors: Glick Peter , Sahn David E. 
Published: March 2003

It is widely recognized that women in developing countries have dual roles as generators of household income and as primary caregivers to their children. Many policies directed at reducing poverty or malnutrition involve one or the other of these roles. Programs to reduce child malnutrition, for example, typically target mothers as caregivers. However, because of the time constraints women face, there are potential conflicts between women's different activities about which policy makers are rarely informed. Nutrition interventions have not usually considered the barriers to participation in such programs facing mothers who, either by choice or necessity, have entered the labour force (Leslie, 1988; Engle, 1994). Similarly, policies directed at improving female employment opportunities typically ignore women's important role in household activities related to children's healthy development. In this paper we address a potentially important implication of women's multiple roles and the time constraints they face: that female labour force participation, by reducing the time available for household activities related to child development, may place young children at nutritional risk.
The Pollbooks of Sandwich, Kent, 1831–68
Authors: Andrews F. W. G. 
Published: March 2003

Abstract
Where a run of pollbooks exists it is possible, by transcribing the material into a computer data‐base, to trace the voting history of individual voters over the entire period of their psephological history. Sandwich was eventually disenfranchised in 1881 for gross corruption, which had a long history in the borough, and the analysis indicates—though it can never prove—which groups of voters were most likely to have been influenced by cash considerations. A further consideration of those voters who split their votes suggests which party might have been the principal beneficiary of this relatively unusual voting choice.
Is the MCI a Useful Signal of Monetary Policy Conditions? An Empirical Investigation
Authors: Siklos Pierre L. 
Published: March 2003

This paper explores some of the potential advantages and disadvantages of monetary policy indicators based on a linear combination of a selected interest rate and the trade‐weighted exchange rate. The resulting measure, called a monetary conditions index (MCI), may provide a means to increase the transparency and credibility of monetary policy but it can also increase confusion among financial market participants if they view the central bank as reacting too closely to every ‘wiggle’ in the MCI.I argue that the aggregation of financial asset prices into an index can have salutary effects on the conduct of monetary policy because it can filter out some of the ‘noise’ in high frequency data. The danger comes from a central bank that stipulates following solely the MCI as a guide to the stance of monetary policy.