bluuebunny/crossref_metadata_2025_split huggingface dataset:
Theoretical and Practical Issues in Purchasing Power Parities Illustrated with Reference to the 1993 Organization for Economic Co-Operation and Development Data
Authors: Cuthbert James R. 
Published: March 2003

Summary
This paper deals first with some theoretical issues in purchasing power parities, including the definition of a generalized class of additive indices, which includes the standard Geary–Khamis and Iklé indices as special cases, and the definition of indicators of price and quantity structure which are independent of the particular units in which quantities are originally expressed. The paper then provides a comparative analysis of the results of applying these techniques to the 1993 Organization for Economic Co-operation and Development data and relates the results to underlying features of the price and expenditure data.
Overcoming Biases and Misconceptions in Ecological Studies
Authors: Guthrie Katherine A. , Sheppard Lianne 
Published: March 2003

Summary
The aggregate data study design provides an alternative group level analysis to ecological studies in the estimation of individual level health risks. An aggregate model is derived by aggregating a plausible individual level relative rate model within groups, such that population-based disease rates are modelled as functions of individual level covariate data. We apply an aggregate data method to a series of fictitious examples from a review paper by Greenland and Robins which illustrated the problems that can arise when using the results of ecological studies to make inference about individual health risks. We use simulated data based on their examples to demonstrate that the aggregate data approach can address many of the sources of bias that are inherent in typical ecological analyses, even though the limited between-region covariate variation in these examples reduces the efficiency of the aggregate study. The aggregate method has the potential to estimate exposure effects of interest in the presence of non-linearity, confounding at individual and group levels, effect modification, classical measurement error in the exposure and non-differential misclassification in the confounder.
Estimation in Epidemics with Incomplete Observations
Authors: Becker Niels G. , Hasofer Abraham M. 
Published: March 2003

Summary
The construction of estimating equations by martingale methods is generalized to yield estimators with explicit expressions for the parameters of the birth-and-death and the general epidemic processes when only partial observations are available. (For the birth-and-death process the death process is observed but the number of births is observed only at the end and for the general epidemic process only the removal process is observed.) For large populations, the use of the martingale central limit theorem yields asymptotic confidence regions for the parameters. Explicit expressions are derived for estimators of the variances of the large sample distributions. The range of validity and usefulness of the new estimators is determined by simulation.
Limited Information Likelihood Analysis Of Survey Data
Authors: Chambers Raymond L. , Dorfman Alan H. , Wang Suojin 
Published: March 2003

Summary
Analysts of survey data are often interested in modelling the population process, or superpopulation, that gave rise to a ‘target’ set of survey variables. An important tool for this is maximum likelihood estimation. A survey is said to provide limited information for such inference if data used in the design of the survey are unavailable to the analyst. In this circumstance, sample inclusion probabilities, which are typically available, provide information which needs to be incorporated into the analysis. We consider the case where these inclusion probabilities can be modelled in terms of a linear combination of the design and target variables, and only sample values of these are available. Strict maximum likelihood estimation of the underlying superpopulation means of these variables appears to be analytically impossible in this case, but an analysis based on approximations to the inclusion probabilities leads to a simple estimator which is a close approximation to the maximum likelihood estimator. In a simulation study, this estimator outperformed several other estimators that are based on approaches suggested in the sampling literature.
Fractal Analysis of Surface Roughness by Using Spatial Data
Authors: Davies S. , Hall Peter 
Published: March 2003

Summary
We develop fractal methodology for data taking the form of surfaces. An advantage of fractal analysis is that it partitions roughness characteristics of a surface into a scale-free component (fractal dimension) and properties that depend purely on scale. Particular emphasis is given to anisotropy where we show that, for many surfaces, the fractal dimension of line transects across a surface must either be constant in every direction or be constant in each direction except one. This virtual direction invariance of fractal dimension provides another canonical feature of fractal analysis, complementing its scale invariance properties and enhancing its attractiveness as a method for summarizing properties of roughness. The dependence of roughness on direction may be explained in terms of scale rather than dimension and can vary with orientation. Scale may be described by a smooth periodic function and may be estimated nonparametrically. Our results and techniques are applied to analyse data on the surfaces of soil and plastic food wrapping. For the soil data, interest centres on the effect of surface roughness on retention of rain-water, and data are recorded as a series of digital images over time. Our analysis captures the way in which both the fractal dimension and the scale change with rainfall, or equivalently with time. The food wrapping data are on a much finer scale than the soil data and are particularly anisotropic. The analysis allows us to determine the manufacturing process which produces the smoothest wrapping, with least tendency for micro-organisms to adhere.
Polynomial Regression and Estimating Functions in the Presence of Multiplicative Measurement Error
Authors: Iturria Stephen J. , Carroll Raymond J. , Firth David 
Published: March 2003

Summary
We consider the polynomial regression model in the presence of multiplicative measurement error in the predictor. Two general methods are considered, with the methods differing in their assumptions about the distributions of the predictor and the measurement errors. Consistent parameter estimates and asymptotic standard errors are derived by using estimating equation theory. Diagnostics are presented for distinguishing additive and multiplicative measurement error. Data from a nutrition study are analysed by using the methods. The results from a simulation study are presented and the performances of the methods are compared.
Saddlepoint Approximations for the Difference of Order Statistics and Studentized Sample Quantiles
Authors: Ma Chunsheng , Robinson John 
Published: March 2003

Summary
For a sample from a given distribution the difference of two order statistics and the Studentized quantile are statistics whose distribution is needed to obtain tests and confidence intervals for quantiles and quantile differences. This paper gives saddlepoint approximations for densities and saddlepoint approximations of the Lugannani–Rice form for tail probabilities of these statistics. The relative errors of the approximations are n −1 uniformly in a neighbourhood of the parameters and this uniformity is global if the densities are log-concave.
A Goodness-of-fit Test for Exponentiality Based on the Memoryless Property
Authors: Ahmad Ibrahim A. , Alwasel Ibrahim A. 
Published: March 2003

Summary
In this investigation a test of goodness of fit for exponentiality is proposed. This procedure applies equally whether the scale and/or the location parameters of the distribution are known or not. The limiting null and non-null distributions of the test statistic are normal under minimal conditions. Monte Carlo critical values for small sample sizes are given and the power of the test is calculated for various alternatives showing that it compares favourably relatively to other more complicated published procedures.
Extensions of Fill's Algorithm for Perfect Simulation
Authors: Møller Jesper , Schladitz Katja 
Published: March 2003

Summary
Fill's algorithm for perfect simulation for attractive finite state space models, unbiased for user impatience, is presented in terms of stochastic recursive sequences and extended in two ways. Repulsive discrete Markov random fields with two coding sets like the auto-Poisson distribution on a lattice with 4-neighbourhood can be treated as monotone systems if a particular partial ordering and quasi-maximal and quasi-minimal states are used. Fill's algorithm then applies directly. Combining Fill's rejection sampling with sandwiching leads to a version of the algorithm which works for general discrete conditionally specified repulsive models. Extensions to other types of models are briefly discussed.
The Exact Bootstrap Mean and Variance of an L-estimator
Authors: Hutson Alan D. , Ernst Michael D. 
Published: March 2003

Summary
Exact analytic expressions for the bootstrap mean and variance of any L-estimator are obtained, thus eliminating the error due to bootstrap resampling. The expressions follow from the direct calculation of the bootstrap mean vector and covariance matrix of the whole set of order statistics. By using these expressions, recommendations can be made about the appropriateness of bootstrap estimation under given conditions.
Maximum Entropy Sampling and Optimal Bayesian Experimental Design
Authors: Sebastiani Paola , Wynn Henry P. 
Published: March 2003

Summary
When Shannon entropy is used as a criterion in the optimal design of experiments, advantage can be taken of the classical identity representing the joint entropy of parameters and observations as the sum of the marginal entropy of the observations and the preposterior conditional entropy of the parameters. Following previous work in which this idea was used in spatial sampling, the method is applied to standard parameterized Bayesian optimal experimental design. Under suitable conditions, which include non-linear as well as linear regression models, it is shown in a few steps that maximizing the marginal entropy of the sample is equivalent to minimizing the preposterior entropy, the usual Bayesian criterion, thus avoiding the use of conditional distributions. It is shown using this marginal formulation that under normality assumptions every standard model which has a two-point prior distribution on the parameters gives an optimal design supported on a single point. Other results include a new asymptotic formula which applies as the error variance is large and bounds on support size.
Mixture Kalman Filters
Authors: Chen Rong , Liu Jun S. 
Published: March 2003

Summary
In treating dynamic systems, sequential Monte Carlo methods use discrete samples to represent a complicated probability distribution and use rejection sampling, importance sampling and weighted resampling to complete the on-line ‘filtering’ task. We propose a special sequential Monte Carlo method, the mixture Kalman filter, which uses a random mixture of the Gaussian distributions to approximate a target distribution. It is designed for on-line estimation and prediction of conditional and partial conditional dynamic linear models, which are themselves a class of widely used non-linear systems and also serve to approximate many others. Compared with a few available filtering methods including Monte Carlo methods, the gain in efficiency that is provided by the mixture Kalman filter can be very substantial. Another contribution of the paper is the formulation of many non-linear systems into conditional or partial conditional linear form, to which the mixture Kalman filter can be applied. Examples in target tracking and digital communications are given to demonstrate the procedures proposed.
On Expected Volumes of Multidimensional Confidence Sets Associated With the Usual and Adjusted Likelihoods
Authors: Datta Gauri S. , DiCiccio Thomas J. 
Published: March 2003

Summary
We consider a general multiparameter set-up, where both the interest and the nuisance parameters are possibly vector valued. We derive an explicit higher order asymptotic formula to compare the expected volumes of confidence sets given by likelihood ratio statistics arising from the usual profile likelihood and various adjustments thereof. Our general framework also allows us to include highest posterior density regions, with approximate frequentist validity, in the study. The fact that our interest parameter is possibly vector valued complicates the derivation and warrants the development of special tools and techniques.
A Risk Set Calibration Method for Failure Time Regression by Using a Covariate Reliability Sample
Authors: Xie Sharon X. , Wang C. Y. , Prentice Ross L. 
Published: March 2003

Summary
Regression parameter estimation in the Cox failure time model is considered when regression variables are subject to measurement error. Assuming that repeat regression vector measurements adhere to a classical measurement model, we can consider an ordinary regression calibration approach in which the unobserved covariates are replaced by an estimate of their conditional expectation given available covariate measurements. However, since the rate of withdrawal from the risk set across the time axis, due to failure or censoring, will typically depend on covariates, we may improve the regression parameter estimator by recalibrating within each risk set. The asymptotic and small sample properties of such a risk set regression calibration estimator are studied. A simple estimator based on a least squares calibration in each risk set appears able to eliminate much of the bias that attends the ordinary regression calibration estimator under extreme measurement error circumstances. Corresponding asymptotic distribution theory is developed, small sample properties are studied using computer simulations and an illustration is provided.
The Construction of Efficient Two-Replicate Row–Column Designs for Use in Field Trials
Authors: John J. A. , Williams E. R. 
Published: March 2003

SUMMARY
Two-replicate row–column designs are often used for field trials in multisite tree or plant breeding programmes. With only two replicates for each trial, it is important to use designs with optimal or near optimal efficiency factors. This paper presents an algorithm for generating such designs. The method extends the contraction approach of Bailey and Patterson to any set of parameters and uses the factorial design construction algorithm of Williams and John to generate designs. Our experience with the algorithm is that it produces designs that are at least as good as, and often much better and more quickly generated than, those obtained by other recent computer algorithms.
Confocal Fluorescence Microscopy of Leaf Cells: An Application of Bayesian Image Analysis
Authors: Hurn M. 
Published: March 2003

SUMMARY
Confocal fluorescence microscopy is a recent and important imaging tool for visualizing three-dimensional specimens without the need for physical sectioning, so that changes in living cells can be studied over time. The application of interest here is a study of the changes in the stomatal guard cells of plant leaves during their opening and closing response sequences. Quantitative estimates of the size and shape of these cells rely on accurate classification (or segmentation) of the images into areas which are parts of cells and areas which are background. This segmentation is complicated in confocal microscopy because the images appear to be ‘smudged’ or ‘dirty’; this degradation is due largely to diffraction and attenuation of the recorded signal caused by the specimen itself. Correcting for this degradation is difficult without knowing the specimen-dependent parameters involved in the degradation process. A fully Bayesian approach is proposed for tackling this problem of blind deconvolution, i.e. of concurrently estimating the degradation parameters while segmenting two-dimensional sections. The end-products are interval estimates of size and shape which acknowledge some of the uncertainty involved in the segmentation. The results are promising, generating credible intervals which are sufficiently narrow to be useful in practice.
Frequency Domain Log-linear Models; Air Pollution and Mortality
Authors: Kelsall Julia E. , Zeger Scott L. , Samet Jonathan M. 
Published: March 2003

SUMMARY
Motivated by a study of the association between counts of daily mortality and air pollution, we present a frequency domain estimation approach for log-linear models that accounts for both overdispersion and autocorrelation. The methods also allow for the discounting or downweighting of information at particular frequencies at which, for example, confounding variables are likely to have greatest influence. This allows flexible sensitivity analyses to be carried out to assess the possible effect of confounders on the estimated effect. We apply the methods to estimate the association between counts of mortality and the concentration of airborne particles in Philadelphia, USA, for the years 1974–1988. We obtain an estimated effect of particulate air pollution on mortality that is significantly greater than zero but less than that obtained by a standard log-linear analysis.
Analyses of Infectious Disease Data from Household Outbreaks by Markov Chain Monte Carlo Methods
Authors: O’Neill Philip D. , Balding David J. , Becker Niels G. , Eerola Mervi , Mollison Denis 
Published: March 2003

SUMMARY
The analysis of infectious disease data presents challenges arising from the dependence in the data and the fact that only part of the transmission process is observable. These difficulties are usually overcome by making simplifying assumptions. The paper explores the use of Markov chain Monte Carlo (MCMC) methods for the analysis of infectious disease data, with the hope that they will permit analyses to be made under more realistic assumptions. Two important kinds of data sets are considered, containing temporal and non-temporal information, from outbreaks of measles and influenza. Stochastic epidemic models are used to describe the processes that generate the data. MCMC methods are then employed to perform inference in a Bayesian context for the model parameters. The MCMC methods used include standard algorithms, such as the Metropolis–Hastings algorithm and the Gibbs sampler, as well as a new method that involves likelihood approximation. It is found that standard algorithms perform well in some situations but can exhibit serious convergence difficulties in others. The inferences that we obtain are in broad agreement with estimates obtained by other methods where they are available. However, we can also provide inferences for parameters which have not been reported in previous analyses.
Case–Control Studies with Complex Sampling
Authors: Scott Alastair , Wild Chris 
Published: March 2003

SUMMARY
The use of complex sampling designs in population-based case–control studies is becoming more common, particularly for sampling the control population. This is prompted by all the usual cost and logistical benefits that are conferred by multistage sampling. Complex sampling has often been ignored in analysis but, with the advent of packages like SUDAAN, survey-weighted analyses that take account of the sample design can be carried out routinely. This paper explores this approach and more efficient alternatives, which can also be implemented by using readily available software.
Estimation of Origin–Destination Trip Rates in Leicester
Authors: Hazelton Martin L. 
Published: March 2003

SUMMARY
The road system in region RA of Leicester has vehicle detectors embedded in many of the network’s road links. Vehicle counts from these detectors can provide transportation researchers with a rich source of data. However, for many projects it is necessary for researchers to have an estimate of origin-to-destination vehicle flow rates. Obtaining such estimates from data observed on individual road links is a non-trivial statistical problem, made more difficult in the present context by non-negligible measurement errors in the vehicle counts collected. The paper uses road link traffic count data from April 1994 to estimate the origin–destination flow rates for region RA. A model for the error prone traffic counts is developed, but the resulting likelihood is not available in closed form. Nevertheless, it can be smoothly approximated by using Monte Carlo integration. The approximate likelihood is combined with prior information from a May 1991 survey in a Bayesian framework. The posterior is explored using the Hastings–Metropolis algorithm, since its normalizing constant is not available. Preliminary findings suggest that the data are overdispersed according to the original model. Results for a revised model indicate that a degree of overdispersion exists, but that the estimates of origin–destination flow rates are quite insensitive to the change in model specification.
Markov Chain Monte Carlo Inference Applied to a Complex Model for the Longitudinal Analysis of Breast Patterns
Authors: Myles Jonathan P. , Duffy Stephen W. , Prevost Teresa C. , Day Nicholas E. , Hakama Matti , Salminen Tiina 
Published: March 2003

SUMMARY
As part of a recently completed study of the effectiveness of breast cancer screening in Finland, 3975 women were invited to attend screens at 2-yearly intervals and the screens were classified as ‘favourable' or ‘unfavourable' in a simplification of the Wolfe classification of mammographic patterns. There is interest in both the rate of change from favourable to unfavourable states and vice versa, and the error rates involved in classifying patterns, in particular in whether these vary with age. We discuss simplifying assumptions which need to be made, because of the very short time series that is involved (four or five observations), to enable transitions between states to be disentangled from classification errors. A model for the data is proposed, the likelihood for the data given this model is obtained and a Markov chain Monte Carlo method is used to obtain posterior distributions. We show how our estimation procedure was checked in advance of the availability of the data, by means of a simulation.
Correcting for Non-Compliance Bias in Case–Control Studies to Evaluate Cancer Screening Programmes
Authors: Duffy Stephen W. , Cuzick Jack , Tabar Laszlo , Vitak Bedrich , Chen Tony Hsiu-Hsi , Yen Ming-Fang , Smith Robert A. 
Published: March 2003

SUMMARY
In the evaluation of uncontrolled service screening programmes for cancer, the case–control design is sometimes used, in which people who die from the disease in question are compared with live controls with respect to screening histories. Such a design tends to yield estimates of relative mortality in voluntary participants compared with non-participants. This may bias results, since compliers and non-compliers may differ a priori in ways which are not related to screening but which nevertheless affect the risk of death from the disease. We present a simple method, employing external data from previously published randomized controlled trials of screening, of correction for this bias. We illustrate it by using data from a case–control study performed within the invited arm of the Malmö mammographic screening trial, a prospective study from the service screening programme in two counties in Sweden, and a matched case–control study of mammographic screening in Florence, Italy.