bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimation in Large Cross Random-Effect Models by Data Augmentation
Authors: Clayton David , Rasbash J. 
Published: March 2003

Summary
Estimation in mixed linear models is, in general, computationally demanding, since applied problems may involve extensive data sets and large numbers of random effects. Existing computer algorithms are slow and/or require large amounts of memory. These problems are compounded in generalized linear mixed models for categorical data, since even approximate methods involve fitting of a linear mixed model within steps of an iteratively reweighted least squares algorithm. Only in models in which the random effects are hierarchically nested can the computations for fitting these models to large data sets be carried out rapidly. We describe a data augmentation approach to these computational difficulties in which we repeatedly fit an overlapping series of submodels, incorporating the missing terms in each submodel as ‘offsets’. The submodels are chosen so that they have a nested random-effect structure, thus allowing maximum exploitation of the computational efficiency which is available in this case. Examples of the use of the algorithm for both metric and discrete responses are discussed, all calculations being carried out using macros within the MLwiN program.
Selecting Treatments: A Decision Theoretic Approach
Authors: Claxton Karl , Lacey Larry F. , Walker Stephen G. 
Published: March 2003

Summary
The paper looks at the problem of comparing two treatments, for a particular population of patients, where one is the current standard treatment and the other a possible alternative under investigation. With limited (finite) financial resources the decision whether to replace one by the other will not be based on health benefits alone. This motivates an economic evaluation of the two competing treatments where the cost of any gain in health benefit is scrutinized; it is whether this cost is acceptable to the relevant authorities which decides whether the new treatment can become the standard. We adopt a Bayesian decision theoretic framework in which a utility function is introduced describing the consequences of making a particular decision when the true state of nature is expressed via an unknown parameter θ (this parameter denotes cost, effectiveness, etc.). The treatment providing the maximum posterior expected utility summarizes the decision rule, expectations taken over the posterior distribution of the parameter θ.
An Ecological Investigation of the Incidence of Cancer in Welsh Children for the Period 1985–1994 in Relation to Residence near the Coastline
Authors: Steward John , John Gareth 
Published: March 2003

Summary
An alarming report from an environmental pressure group raised concerns about childhood leukaemia and the Irish Sea. In response, this ecological study explores the hypotheses that childhood cancer rates are increased by living near the coast of Wales, especially in the north, and in particular near estuaries and mud-flats. Using Poisson regression to adjust for possible confounding variables, no evidence was found for a coastline proximity effect at the level of census wards (5 km). Moreover the rates were significantly lower near estuaries than for the rest of the coast, but there was a small but non-significant increase near mud-flats. Case–control modelling of postcoded cases living within the coastal wards using Stone’s method also failed to detect any monotonic reduction in relative risk near the coastline.
Methods for Eliciting Aetiological Clues from Geographically Clustered Cases of Disease, with Application to Leukaemia–Lymphoma Data
Authors: Williams Joan R. , Alexander Freda E. , Cartwright Ray A. , McNally Richard J. Q. 
Published: March 2003

Summary
The nearest neighbour analysis method has been developed to determine whether a disease case may be regarded as being unusually close to other neighbouring cases of the same disease. Using this method, each disease case is classified as spatially ‘clustered’ or ‘non-clustered’. The method is also used to provide a test for global clustering. ‘Clusters’ are constructed by amalgamating geographically neighbouring clustered cases into one contiguous ‘cluster area’. This paper describes a method for studying differences between clustered and non-clustered cases, in terms of case ‘attributes’. These attributes may be person related, such as age and sex, or area based, such as geographical isolation. The area-based variables are subject to geographical correlation. The comparison of clustered and non-clustered cases may reveal similarities or differences, which may, in turn, give clues to disease aetiology. A method for studying ‘linkage’ or similarities in attributes between cases that occur in the same clusters is also described. The methods are illustrated by application to incidence data for leukaemias and lymphomas.
A Shared Component Model for Detecting Joint and Selective Clustering of Two Diseases
Authors: Knorr-Held Leonhard , Best Nicola G. 
Published: March 2003

Summary
The study of spatial variations in disease rates is a common epidemiological approach used to describe the geographical clustering of diseases and to generate hypotheses about the possible ‘causes’ which could explain apparent differences in risk. Recent statistical and computational developments have led to the use of realistically complex models to account for overdispersion and spatial correlation. However, these developments have focused almost exclusively on spatial modelling of a single disease. Many diseases share common risk factors (smoking being an obvious example) and, if similar patterns of geographical variation of related diseases can be identified, this may provide more convincing evidence of real clustering in the underlying risk surface. We propose a shared component model for the joint spatial analysis of two diseases. The key idea is to separate the underlying risk surface for each disease into a shared and a disease-specific component. The various components of this formulation are modelled simultaneously by using spatial cluster models implemented via reversible jump Markov chain Monte Carlo methods. We illustrate the methodology through an analysis of oral and oesophageal cancer mortality in the 544 districts of Germany, 1986–1990.
A Two-Phase Sampling Scheme with Applications to Auditing or Sed Quis Custodiet Ipsos Custodes?
Authors: Barnett V. , Haworth J. , Smith T. M. F. 
Published: March 2003

Summary
External auditors such as the National Audit Office (NAO) are the final arbiters on the level of error in accounts presented to them by their clients, and the accuracy or otherwise of individual transactions. In coming to a view on the level of error, they are expected to carry out the audit effectively and efficiently, and therefore need to make the best possible use of all the information at their disposal, even when some of the information may not be totally accurate. We consider the particular situation where the NAO is given access to the results of tests on a relatively large random sample of transactions, typically conducted by the client’s internal auditors. A two-phase sampling scheme arises when the NAO subsequently assesses the quality of the client’s data by retesting a subsample of these transactions. The paper discusses methodologies for combining the two sets of data to produce optimum estimates of the proportion of transactions in error (the error rate) and of the level of monetary error in the account. Although a maximum likelihood approach yields a relatively straightforward solution to the error rate problem, there is no uniformly optimum way to estimate the monetary error. Three possible methods are proposed, and the results of a series of simulation experiments to compare their performance under a variety of audit conditions is described.
Some Practical Issues in the Evaluation of Heterogeneous Labour Market Programmes by Matching Methods
Authors: Lechner Michael 
Published: March 2003

SummaryRecently several studies have analysed active labour market policies by using a recently proposed matching estimator for multiple programmes. Since there is only very limited practical experience with this estimator, this paper checks its sensitivity with respect to issues that are of practical importance in this kind of evaluation study. The estimator turns out to be fairly robust to several features that concern its implementation. Furthermore, the paper demonstrates that the matching approach per se is no panacea for solving all the problems of evaluation studies, but that its success depends critically on the information that is available in the data. Finally, a comparison with a bootstrap distribution provides some justification for using a simplified approximation of the distribution of the estimator that ignores its sequential nature.
Boundary Aware Estimators of Integrated Density Derivative Products
Authors: Cheng Ming-Yen 
Published: March 2003

Summary
Integrated squared density derivatives are important to the plug-in type of bandwidth selector for kernel density estimation. Conventional estimators of these quantities are inefficient when there is a non-smooth boundary in the support of the density. We introduce estimators that utilize density derivative estimators obtained from local polynomial fitting. They retain the rates of convergence in mean-squared error that are familiar from non-boundary cases, and the constant coefficients have similar forms. The estimators and the formula for their asymptotically optimal bandwidths, which depend on integrated products of density derivatives, are applied to automatic bandwidth selection for local linear density estimation. Simulation studies show that the constructed bandwidth rule and the Sheather–Jones bandwidth are competitive in non-boundary cases, but the former overcomes boundary problems whereas the latter does not.
Acceleration of the EM Algorithm by using Quasi-Newton Methods
Authors: Jamshidian Mortaza , Jennrich Robert I. 
Published: March 2003

Summary
The EM algorithm is a popular method for maximum likelihood estimation. Its simplicity in many applications and desirable convergence properties make it very attractive. Its sometimes slow convergence, however, has prompted researchers to propose methods to accelerate it. We review these methods, classifying them into three groups: pure, hybrid and EM-type accelerators. We propose a new pure and a new hybrid accelerator both based on quasi-Newton methods and numerically compare these and two other quasi-Newton accelerators. For this we use examples in each of three areas: Poisson mixtures, the estimation of covariance from incomplete data and multivariate normal mixtures. In these comparisons, the new hybrid accelerator was fastest on most of the examples and often dramatically so. In some cases it accelerated the EM algorithm by factors of over 100. The new pure accelerator is very simple to implement and competed well with the other accelerators. It accelerated the EM algorithm in some cases by factors of over 50. To obtain standard errors, we propose to approximate the inverse of the observed information matrix by using auxiliary output from the new hybrid accelerator. A numerical evaluation of these approximations indicates that they may be useful at least for exploratory purposes.
Fast EM-type Implementations for Mixed Effects Models
Authors: Meng Xiao-Li , Van Dyk D. 
Published: March 2003

Summary
The mixed effects model, in its various forms, is a common model in applied statistics. A useful strategy for fitting this model implements EM-type algorithms by treating the random effects as missing data. Such implementations, however, can be painfully slow when the variances of the random effects are small relative to the residual variance. In this paper, we apply the ‘working parameter’ approach to derive alternative EM-type implementations for fitting mixed effects models, which we show empirically can be hundreds of times faster than the common EM-type implementations. In our limited simulations, they also compare well with the routines in S-PLUS® and Stata® in terms of both speed and reliability. The central idea of the working parameter approach is to search for efficient data augmentation schemes for implementing the EM algorithm by minimizing the augmented information over the working parameter, and in the mixed effects setting this leads to a transfer of the mixed effects variances into the regression slope parameters. We also describe a variation for computing the restricted maximum likelihood estimate and an adaptive algorithm that takes advantage of both the standard and the alternative EM-type implementations.
Density and Hazard Rate Estimation for Right-Censored Data by Using Wavelet Methods
Authors: Antoniadis Anestis , Grégoire Gérard , Nason Guy 
Published: March 2003

Summary
This paper describes a wavelet method for the estimation of density and hazard rate functions from randomly right-censored data. We adopt a nonparametric approach in assuming that the density and hazard rate have no specific parametric form. The method is based on dividing the time axis into a dyadic number of intervals and then counting the number of events within each interval. The number of events and the survival function of the observations are then separately smoothed over time via linear wavelet smoothers, and then the hazard rate function estimators are obtained by taking the ratio. We prove that the estimators have pointwise and global mean-square consistency, obtain the best possible asymptotic mean integrated squared error convergence rate and are also asymptotically normally distributed. We also describe simulation experiments that show that these estimators are reasonably reliable in practice. The method is illustrated with two real examples. The first uses survival time data for patients with liver metastases from a colorectal primary tumour without other distant metastases. The second is concerned with times of unemployment for women and the wavelet estimate, through its flexibility, provides a new and interesting interpretation.
Gibbs Sampling for Bayesian Non-Conjugate and Hierarchical Models by Using Auxiliary Variables
Authors: Damlen P. , Wakefield J. , Walker S. 
Published: March 2003

Summary
We demonstrate the use of auxiliary (or latent) variables for sampling non-standard densities which arise in the context of the Bayesian analysis of non-conjugate and hierarchical models by using a Gibbs sampler. Their strategic use can result in a Gibbs sampler having easily sampled full conditionals. We propose such a procedure to simplify or speed up the Markov chain Monte Carlo algorithm. The strength of this approach lies in its generality and its ease of implementation. The aim of the paper, therefore, is to provide an alternative sampling algorithm to rejection-based methods and other sampling approaches such as the Metropolis–Hastings algorithm.
Extremal Analysis of Processes Sampled at Different Frequencies
Authors: Robinson Michael E. , Tawn Jonathan A. 
Published: March 2003

Summary
The observed extremes of a discrete time process depend on the process itself and the sampling frequency. We develop theoretical results which show how to account for the effect of sampling frequency on extreme values, thus enabling us to analyse systematically extremal data from series with different sampling rates. We present statistical methodology based on these results which we illustrate though simulations and by applications to sea-waves and rainfall data.
Bayesian Poisson Models for the Graphical Combination of Dependent Expert Information
Authors: Smith Jim Q. , Faria, Jr Álvaro E. 
Published: March 2003

Summary
A supra-Bayesian (SB) wants to combine the information from a group of k experts to produce her distribution of a probability θ. Each expert gives his counts of what he thinks are the numbers of successes and failures in a sequence of independent trials, each with probability θ of success. These counts, used as a surrogate for each expert's own individual probability assessment (together with his associated level of confidence in his estimate), allow the SB to build various plausible conjugate models. Such models reflect her beliefs about the reliability of different experts and take account of different possible patterns of overlap of information between them. Corresponding combination rules are then obtained and compared with other more established rules and their properties examined.
Testing Generalized Linear and Semiparametric Models Against Smooth Alternatives
Authors: Kauermann Göran , Tutz Gerhard 
Published: March 2003

Summary
We propose goodness-of-fit tests for testing generalized linear models and semiparametric regression models against smooth alternatives. The focus is on models having both continous and factorial covariates. As a smooth extension of a parametric or semiparametric model we use generalized varying-coefficient models as proposed by Hastie and Tibshirani. A likelihood ratio statistic is used for testing. Asymptotic expansions allow us to write the estimates as linear smoothers which in turn guarantees simple and fast bootstrapping of the test statistic. The test is shown to have √n-power, but in contrast with parametric tests it is powerful against smooth alternatives in general.
Generalized Least Squares with Misspecified Serial Correlation Structures
Authors: Koreisha Sergio G. , Fang Yue 
Published: March 2003

Summary
Summary. The regression literature contains hundreds of studies on serially correlated disturbances. Most of these studies assume that the structure of the error covariance matrix Ω is known or can be estimated consistently from data. Surprisingly, few studies investigate the properties of estimated generalized least squares (GLS) procedures when the structure of Ω is incorrectly identified and the parameters are inefficiently estimated. We compare the finite sample efficiencies of ordinary least squares (OLS), GLS and incorrect GLS (IGLS) estimators. We also prove new theorems establishing theoretical efficiency bounds for IGLS relative to GLS and OLS. Results from an exhaustive simulation study are used to evaluate the finite sample performance and to demonstrate the robustness of IGLS estimates vis-à-vis OLS and GLS estimates constructed for models with known and estimated (but correctly identified) Ω. Some of our conclusions for finite samples differ from established asymptotic results.
Cox Regression in Cohort Studies with Validation Sampling
Authors: Chen Yi-Hau 
Published: March 2003

SummaryAn estimation procedure is proposed for the Cox model in cohort studies with validation sampling, where crude covariate information is observed for the full cohort and true covariate information is collected on a validation set sampled randomly from the full cohort. The method proposed makes use of the partial information from data that are available on the entire cohort by fitting a working Cox model relating crude covariates to the failure time. The resulting estimator is consistent regardless of the specification of the working model and is asymptotically more efficient than the validation-set-only estimator. Approximate asymptotic relative efficiencies with respect to some alternative methods are derived under a simple scenario and further studied numerically. The finite sample performance is investigated and compared with alternative methods via simulation studies. A similar procedure also works for the case where the validation set is a stratified random sample from the cohort.
Familywise Robustness Criteria for Multiple-Comparison Procedures
Authors: Holland Burt , Cheung Siu Hung 
Published: March 2003

SummaryA criticism of multiple-comparison procedures is that the family of inferences over which an error rate is controlled is often arbitrarily selected, yet the conclusion may depend heavily on the choice of the family. Such ambiguity is most likely in large exploratory studies requiring numerous simultaneous inferences. In ambiguous situations it is desirable that results of multiple-comparison procedures depend little on the chosen family. To assess this, we propose several familywise robustness criteria to evaluate such procedures, and we find some of their properties theoretically and by simulation. Procedures that control the false discovery rate seem to be familywise robust.
Robust Estimation in Generalized Linear Mixed Models
Authors: Yau Kelvin K. W. , Kuk Anthony Y. C. 
Published: March 2003

SummaryGeneralized linear mixed models (GLMMs) are widely used to analyse non-normal response data with extra-variation, but non-robust estimators are still routinely used. We propose robust methods for maximum quasi-likelihood and residual maximum quasi-likelihood estimation to limit the influence of outlying observations in GLMMs. The estimation procedure parallels the development of robust estimation methods in linear mixed models, but with adjustments in the dependent variable and the variance component. The methods proposed are applied to three data sets and a comparison is made with the nonparametric maximum likelihood approach. When applied to a set of epileptic seizure data, the methods proposed have the desired effect of limiting the influence of outlying observations on the parameter estimates. Simulation shows that one of the residual maximum quasi-likelihood proposals has a smaller bias than those of the other estimation methods. We further discuss the equivalence of two GLMM formulations when the response variable follows an exponential family. Their extensions to robust GLMMs and their comparative advantages in modelling are described. Some possible modifications of the robust GLMM estimation methods are given to provide further flexibility for applying the method.
The Statistical Analysis of Preference Data
Authors: Taplin Ross H. 
Published: March 2003

SUMMARY
A new statistic and a new method of analysis are proposed for data where a sample of respondents provides a preference ordering of some treatments. The new preference statistic is compared with the Friedman statistic, particularly for an example where 12 home owners each ranked four grasses. The new analysis provides a more natural and less misleading assessment of where the differences occur than an analysis based on the rank sums of the Friedman statistic. The new analysis is also more robust to deviations from the classical location problem, is not related to election methods known to have undesirable characteristics and adheres to the Condorcet criterion for election methods.
Designing and Integrating Composite Networks for Monitoring Multivariate Gaussian Pollution Fields
Authors: Zidek James V. , Sun Weimin , Le Nhu D. 
Published: March 2003

SUMMARY
Networks of ambient monitoring stations are used to monitor environmental pollution fields such as those for acid rain and air pollution. Such stations provide regular measurements of pollutant concentrations. The networks are established for a variety of purposes at various times so often several stations measuring different subsets of pollutant concentrations can be found in compact geographical regions. The problem of statistically combining these disparate information sources into a single ‘network’ then arises. Capitalizing on the efficiencies so achieved can then lead to the secondary problem of extending this network. The subject of this paper is a set of 31 air pollution monitoring stations in southern Ontario. Each of these regularly measures a particular subset of ionic sulphate, sulphite, nitrite and ozone. However, this subset varies from station to station. For example only two stations measure all four. Some measure just one. We describe a Bayesian framework for integrating the measurements of these stations to yield a spatial predictive distribution for unmonitored sites and unmeasured concentrations at existing stations. Furthermore we show how this network can be extended by using an entropy maximization criterion. The methods assume that the multivariate response field being measured has a joint Gaussian distribution conditional on its mean and covariance function. A conjugate prior is used for these parameters, some of its hyperparameters being fitted empirically.