bluuebunny/crossref_metadata_2025_split huggingface dataset:
Building Multivariable Prognostic and Diagnostic Models: Transformation of the Predictors by Using Fractional Polynomials
Authors: Sauerbrei W. , Royston P. 
Published: March 2003

Summary
To be useful to clinicians, prognostic and diagnostic indices must be derived from accurate models developed by using appropriate data sets. We show that fractional polynomials, which extend ordinary polynomials by including non-positive and fractional powers, may be used as the basis of such models. We describe how to fit fractional polynomials in several continuous covariates simultaneously, and we propose ways of ensuring that the resulting models are parsimonious and consistent with basic medical knowledge. The methods are applied to two breast cancer data sets, one from a prognostic factors study in patients with positive lymph nodes and the other from a study to diagnose malignant or benign tumours by using colour Doppler blood flow mapping. We investigate the problems of biased parameter estimates in the final model and overfitting using cross-validation calibration to estimate shrinkage factors. We adopt bootstrap resampling to assess model stability. We compare our new approach with conventional modelling methods which apply stepwise variables selection to categorized covariates. We conclude that fractional polynomial methodology can be very successful in generating simple and appropriate models.
Fatal Train Accidents on Britain’s Mainline Railways
Authors: Evans Andrew W. 
Published: March 2003

Summary
This paper investigates trends in the frequencies and numbers of fatalities in fatal train collisions, derailments and buffer overruns on the national railway system of Great Britain. The main part of the paper is based on the 31-year period 1967–1997. After the paper had been completed, but before it was due to be published, the most serious train accident for more than a decade occurred on October 5th, 1999, at Ladbroke Grove in London, in which 30 people lost their lives. That accident was by far the most severe ever to have occurred in Great Britain with modern rolling-stock; it leads to an upward revision in the estimates of current and future fatalities in train accidents. To provide a context for the statistical interpretation of the Ladbroke Grove accident, and to avoid the temptation of being wise after the event, the main part of the paper (Sections 1–5) is presented exactly as it was finalized before the accident; a postscript (Section 6) is then added to update the conclusions in the light of the accident. Railway safety has improved over the long term: even after including the Ladbroke Grove accident in the data, the paper estimates that the current mean frequency of fatal train accidents from all causes is just over 1 per year, and the mean number of fatalities in such accidents is just under 4 per year. It is an indication of the exceptional severity of the Ladbroke Grove accident that the number of people who lost their lives was so much larger than this.
Estimating the Underlying Change in Unemployment in the UK
Authors: Harvey Andrew , Chung Chia-Hui 
Published: March 2003

Summary
By setting up a suitable time series model in state space form, the latest estimate of the underlying current change in a series may be computed by the Kalman filter. This may be done even if the observations are only available in a time-aggregated form subject to survey sampling error. A related series, possibly observed more frequently, may be used to improve the estimate of change further. The paper applies these techniques to the important problem of estimating the underlying monthly change in unemployment in the UK measured according to the definition of the International Labour Organisation by the Labour Force Survey. The fitted models suggest a reduction in root-mean-squared error of around 10% over a simple estimate based on differences if a univariate model is used and a further reduction of 50% if information on claimant counts is taken into account. With seasonally unadjusted data, the bivariate model offers a gain of roughly 40% over the use of annual differences. For both adjusted and unadjusted data, there is a further gain of around 10% if the next month’s figure on claimant counts is used. The method preferred is based on a bivariate model with unadjusted data. If the next month’s claimant count is known, the root-mean-squared error for the estimate of change is just over 10000.
Improved Estimation Procedures for Multilevel Models with Binary Response: A Case-Study
Authors: Rodríguez Germán , Goldman Noreen 
Published: March 2003

Summary
During recent years, analysts have been relying on approximate methods of inference to estimate multilevel models for binary or count data. In an earlier study of random-intercept models for binary outcomes we used simulated data to demonstrate that one such approximation, known as marginal quasi-likelihood, leads to a substantial attenuation bias in the estimates of both fixed and random effects whenever the random effects are non-trivial. In this paper, we fit three-level random-intercept models to actual data for two binary outcomes, to assess whether refined approximation procedures, namely penalized quasi-likelihood and second-order improvements to marginal and penalized quasi-likelihood, also underestimate the underlying parameters. The extent of the bias is assessed by two standards of comparison: exact maximum likelihood estimates, based on a Gauss–Hermite numerical quadrature procedure, and a set of Bayesian estimates, obtained from Gibbs sampling with diffuse priors. We also examine the effectiveness of a parametric bootstrap procedure for reducing the bias. The results indicate that second-order penalized quasi-likelihood estimates provide a considerable improvement over the other approximations, but all the methods of approximate inference result in a substantial underestimation of the fixed and random effects when the random effects are sizable. We also find that the parametric bootstrap method can eliminate the bias but is computationally very intensive.
Procrustes Shape Analysis of Planar Point Subsets
Authors: Dryden Ian L. , Faghihi Mohammad Reza , Taylor Charles C. 
Published: March 2003

Summary
Consider a set of points in the plane randomly perturbed about a mean configuration by Gaussian errors. In this paper a Procrustes statistic based on the shapes of subsets of the points is studied, and its approximate distribution is found for small variations. We derive various properties of the distribution including the first two moments, a central limit result and a scaled χ2–-approximation. We concentrate on the independent isotropic Gaussian error case, although the results are valid for general covariance structures. We investigate triangle subsets in detail and in particular the situation where the population mean is regular (i.e. a Delaunay triangulation of the mean of the process is comprised of equilateral triangles of the same size). We examine the variance of the statistic for differently shaped regions and provide an asymptotic result for general shaped regions. The results are applied to an investigation of regularity in human muscle fibre cross-sections.
Fractal Function Estimation via Wavelet Shrinkage
Authors: Wang Y. 
Published: March 2003

Summary
In scientific studies objects are often very rough. Mathematically these rough objects are modelled by fractal functions, and the fractal dimension is usually used to measure their roughness. This paper investigates fractal function estimation by wavelet shrinkage. It is shown that wavelet shrinkage can estimate fractal functions with their fractal dimensions virtually preserved.
Semiparametric Likelihood-Based Inference for Biased and Truncated Data When the Total Sample Size is Known
Authors: Li Gang , Qin Jing 
Published: March 2003

Summary
Biased and truncated data arise in many practical areas. Many efficient statistical methods have been studied in the literature. This paper discusses likelihood-based inferences for the two types of data in the presence of auxiliary information of known total sample size. It is shown that this information improves inference about the underlying distribution and its parameters in which we are interested. A semiparametric likelihood ratio confidence interval technique is employed. Also some simulation results are reported.
On the Time to Extinction in Recurrent Epidemics
Authors: Nåsell I. 
Published: March 2003

Summary
An approximation is derived for the expected time to extinction in a stochastic model for recurrent epidemics. Numerical illustrations indicate that the approximation is crude but that it has the correct order of magnitude. The quasi-stationary distribution plays an important role in the derivation. Approximations for the critical community size and of the persistence threshold are derived. Comments are made on the classical study by Bartlell (1956–1960).
Bayesian Nonparametric Inference for Random Distributions and Related Functions
Authors: Walker Stephen G. , Damien Paul , Laud Purushottam W. , Smith Adrian F. M. 
Published: March 2003

Summary
In recent years, Bayesian nonparametric inference, both theoretical and computational, has witnessed considerable advances. However, these advances have not received a full critical and comparative analysis of their scope, impact and limitations in statistical modelling; many aspects of the theory and methods remain a mystery to practitioners and many open questions remain. In this paper, we discuss and illustrate the rich modelling and analytic possibilities that are available to the statistician within the Bayesian nonparametric and/or semiparametric framework.
Upper Probabilities Based Only on the Likelihood Function
Authors: Walley Peter , Moral Serafín 
Published: March 2003

Summary
In the problem of parametric statistical inference with a finite parameter space, we propose some simple rules for defining posterior upper and lower probabilities directly from the observed likelihood function, without using any prior information. The rules satisfy the likelihood principle and a basic consistency principle (‘avoiding sure loss’), they produce vacuous inferences when the likelihood function is constant, and they have other symmetry, monotonicity and continuity properties. One of the rules also satisfies fundamental frequentist principles. The rules can be used to eliminate nuisance parameters, and to interpret the likelihood function and to use it in making decisions. To compare the rules, they are applied to the problem of sampling from a finite population. Our results indicate that there are objective statistical methods which can reconcile three general approaches to statistical inference: likelihood inference, coherent inference and frequentist inference.
Rank Statistics Expressible as Integrals Under P–P-plots and Receiver Operating Characteristic Curves
Authors: Girling A. J. 
Published: March 2003

Summary
Given a probability measure on the unit square, the measure of the region under an empirical P–P-plot defines a two-sample rank statistic. Instances include trimmed and censored versions of the Mann–Whitney–Wilcoxon statistic and a class of statistics with applications in the analysis of receiver operating characteristic (ROC) curves. A large sample distribution for such a statistic is obtained, which is valid under sampling from general populations. Explicit results are presented for comparing arbitrary quantile segments of two populations. The results are not restricted to continuous data and incorporate adjustments for tied values in the discrete case. A multivariate version of the large sample distribution extends the class of tractable statistics in ROC analysis and facilitates the use of methods based on partial areas when the data are discrete.
Flexible Empirical Bayes Estimation for Wavelets
Authors: Clyde Merlise , George Edward I. 
Published: March 2003

Summary
Wavelet shrinkage estimation is an increasingly popular method for signal denoising and compression. Although Bayes estimators can provide excellent mean-squared error (MSE) properties, the selection of an effective prior is a difficult task. To address this problem, we propose empirical Bayes (EB) prior selection methods for various error distributions including the normal and the heavier-tailed Student t-distributions. Under such EB prior distributions, we obtain threshold shrinkage estimators based on model selection, and multiple-shrinkage estimators based on model averaging. These EB estimators are seen to be computationally competitive with standard classical thresholding methods, and to be robust to outliers in both the data and wavelet domains. Simulated and real examples are used to illustrate the flexibility and improved MSE performance of these methods in a wide variety of settings.
Count Data and Treatment Heterogeneity in 2×2 Crossover Trials
Authors: Longford N. T. 
Published: March 2003

SUMMARY
Count data are routinely assumed to have a Poisson distribution, especially when there are no straightforward diagnostic procedures for checking this assumption. We reanalyse two data sets from crossover trials of treatments for angina pectoris, in which the outcomes are counts of anginal attacks. Standard analyses focus on treatment effects, averaged over subjects; we are also interested in the dispersion of these effects (treatment heterogeneity). We set up a log-Poisson model with random coefficients to estimate the distribution of the treatment effects and show that the analysis is very sensitive to the distributional assumption; the population variance of the treatment effects is confounded with the (variance) function that relates the conditional variance of the outcomes, given the subject’s rate of attacks, to the conditional mean. Diagnostic model checks based on resampling from the fitted distribution indicate that the default choice of the Poisson distribution for the analysed data sets is poorly supported. We propose to augment the data sets with observations of the counts, made possibly outside the clinical setting, so that the conditional distribution of the counts could be established.
Exact Group Comparisons Using Irregular Longitudinal Data
Authors: Maritz J. S. , Lombard C. J. , Morrell C. H. 
Published: March 2003

SUMMARY
Longitudinal data on an experimental subject arise when observations of a dependent variable are made at several successive values of an indexing variable x; often the indexing variable is time. When such data are recorded for a number of subjects, typically animal or human, the x-configuration may vary from subject to subject, producing irregular longitudinal data. Exact permutation methods are considered for comparing two or more groups of subjects. This approach entails choosing appropriate descriptive and test statistics and generating their exact distributions. The method is applied to two data sets, one relating to pigs and one to baboons. The second of these data sets involves a co-variate and the results are compared with those yielded by fitting a parametric mixed effects model. In the first example differences between groups were clearly established, and the graphical representations proposed were useful for describing the differences. In the second example the agreement between the results of the two methods of analysis was found to be very close.
Bayesian Projection of the Acquired Immune Deficiency Syndrome Epidemic
Authors: De Angelis D. , Gilks W. R. , Day N. E. 
Published: March 2003

SUMMARY
Short-term projections of the acquired immune deficiency syndrome (AIDS) epidemic in England and Wales have been regularly updated since the publication of the Cox report in 1988. The key approach for those updates has been the back-calculation method, which has been informally adapted to acknowledge various sources of uncertainty as well as to incorporate increasingly available information on the spread of the human immunodeficiency virus (HIV) in the population. We propose a Bayesian formulation of the back-calculation method which allows a formal treatment of uncertainty and the inclusion of extra information, within a single coherent composite model. Estimation of the variably dimensioned model is carried out by using reversible-jump Markov chain Monte Carlo methods. Application of the model to data for homosexual and bisexual males in England and Wales is presented, and the role of the various sources of information and model assumptions is appraised. Our results show a massive peak in HIV infections around 1983 and suggest that the incidence of AIDS has now reached a plateau, although there is still substantial uncertainty about the future.
A Non-Homogeneous Hidden Markov Model for Precipitation Occurrence
Authors: Hughes James P. , Guttorp Peter , Charles Stephen P. 
Published: March 2003

SUMMARY
A non-homogeneous hidden Markov model is proposed for relating precipitation occurrences at multiple rain-gauge stations to broad scale atmospheric circulation patterns (the so-called ‘downscaling problem’). We model a 15-year sequence of winter data from 30 rain stations in south-western Australia. The first 10 years of data are used for model development and the remaining 5 years are used for model evaluation. The fitted model accurately reproduces the observed rainfall statistics in the reserved data despite a shift in atmospheric circulation (and, consequently, rainfall) between the two periods. The fitted model also provides some useful insights into the processes driving rainfall in this region.
A Discrete Variable Chain Graph for Applicants for Credit
Authors: Stanghellini E. , McConway K. J. , Hand D. J. 
Published: March 2003

SUMMARY
A bank offering unsecured personal loans may be interested in several related outcome variables, including defaulting on the repayments, early repayment or failing to take up an offered loan. Current predictive models used by banks typically consider such variables individually. However, the fact that they are related to each other, and to many interrelated potential predictor variables, suggests that graphical models may provide an attractive alternative solution. We developed such a model for a data set of 15 variables measured on a set of 14 000 applications for unsecured personal loans. The resulting global model of behaviour enabled us to identify several previously unsuspected relationships of considerable interest to the bank. For example, we discovered important but obscure relationships between taking out insurance, prior delinquency with a credit card and delinquency with the loan.
The Analysis of Designed Experiments and Longitudinal Data by Using Smoothing Splines
Authors: Verbyla Arũnas P. , Cullis Brian R. , Kenward Michael G. , Welham Sue J. 
Published: March 2003

SUMMARY
In designed experiments and in particular longitudinal studies, the aim may be to assess the effect of a quantitative variable such as time on treatment effects. Modelling treatment effects can be complex in the presence of other sources of variation. Three examples are presented to illustrate an approach to analysis in such cases. The first example is a longitudinal experiment on the growth of cows under a factorial treatment structure where serial correlation and variance heterogeneity complicate the analysis. The second example involves the calibration of optical density and the concentration of a protein DNase in the presence of sampling variation and variance heterogeneity. The final example is a multienvironment agricultural field experiment in which a yield–seeding rate relationship is required for several varieties of lupins. Spatial variation within environments, heterogeneity between environments and variation between varieties all need to be incorporated in the analysis. In this paper, the cubic smoothing spline is used in conjunction with fixed and random effects, random coefficients and variance modelling to provide simultaneous modelling of trends and covariance structure. The key result that allows coherent and flexible empirical model building in complex situations is the linear mixed model representation of the cubic smoothing spline. An extension is proposed in which trend is partitioned into smooth and non-smooth components. Estimation and inference, the analysis of the three examples and a discussion of extensions and unresolved issues are also presented.
Analysing Financial Returns by Using Regression Models Based on Non-Symmetric Stable Distributions
Authors: Lambert Phillipe , Lindsey J. K. 
Published: March 2003

SUMMARY
The daily evolution of the price of Abbey National shares over a 10-week period is analysed by using regression models based on possibly non-symmetric stable distributions. These distributions, which are only known through their characteristic function, can be used in practice for interactive modelling of heavy-tailed processes. A regression model for the location parameter is proposed and shown to induce a similar model for the mode. Finally, regression models for the other three parameters of the stable distribution are introduced. The model found to fit best allows the skewness of the distribution, rather than the location or scale parameters, to vary over time. The most likely share return is thus changing over time although the region where most returns are observed is stationary.
Semiparametric Estimation of Regression Quantiles with Application to Standardizing Weight for Height and Age in US Children
Authors: Heagerty Patrick J. , Pepe Margaret S. 
Published: March 2003

SUMMARY
The appropriate interpretation of measurements often requires standardization for concomitant factors. For example, standardization of weight for both height and age is important in obesity research and in failure-to-thrive research in children. Regression quantiles from a reference population afford one intuitive and popular approach to standardization. Current methods for the estimation of regression quantiles can be classified as nonparametric with respect to distributional assumptions or as fully parametric. We propose a semiparametric method where we model the mean and variance as flexible regression spline functions and allow the unspecified distribution to vary smoothly as a function of covariates. Similarly to Cole and Green, our approach provides separate estimates and summaries for location, scale and distribution. However, similarly to Koenker and Bassett, we do not assume any parametric form for the distribution. Estimation for either cross-sectional or longitudinal samples is obtained by using estimating equations for the location and scale functions and through local kernel smoothing of the empirical distribution function for standardized residuals. Using this technique with data on weight, height and age for females under 3 years of age, we find that there is a close relationship between quantiles of weight for height and age and quantiles of body mass index (BMI=weight/height2) for age in this cohort.