bluuebunny/crossref_metadata_2025 huggingface dataset:
In Defence of Pure Pluralism: Two Readings of Walzer's Spheres of Justice
Authors: Trappenburg Margo
Published: March 2003

In this article I will argue that there are two theories of distributive justice hidden in Walzer's Spheres of Justice. The first one emphasises the separation of distributive spheres. It tries to formulate distributive criteria by sticking faithfully to sphere‐specificity. I shall refer to this theory as ‘pure pluralism’. The second theory downplays the separation of spheres and emphasises ‘across spheres’ or ‘between spheres’ criteria instead. I shall call this theory ‘mitigated pluralism’. Mitigated pluralism has become popular among Walzer's friendly critics who apparently do not want to charge him with a distributive theory as clear and rigid as pure pluralism. Although I consider myself another friendly critic, I shall argue in favour of pure pluralism.
Press Advertising and the Political Differentiation of Newspapers
Authors: Gabszewicz Jean J. , Laussel Didier , Sonnac Nathalie
Published: March 2003

The press industry depends in a crucial way on the possibility of financing an important fraction of its activities by advertising receipts. We show that this may induce the editors of the newspapers to moderate the political message they display to their readers, compared with the political opinions they would have expressed otherwise.
Transport Costs and Rural Development
Authors: Kilkenny Maureen
Published: March 2003

Innovations that reduce costs of transport from rural locations may also reduce transport costs to rural areas. As transport costs fall, producers can afford to concentrate and achieve economies of scale. This paper explains an initially negative, but ultimately positive, relationship between reductions in transport costs and rural development. A two‐region general equilibrium model with firm and worker spatial mobility highlights the firm and household location implications of costly transport‐service use by both industry and agriculture in the context of scale economies and product differentiation. The computable general equilibrium model is initialized and verified with a bi‐regional social accounting matrix and then used for simulations. Changes in relative transport costs are shown to affect relative regional wage rates, thus also determining the location of “production‐cost‐oriented” firms.
A Note on the Benefits to Current Residents of State Employment Growth: Is There an Industry Mix Effect on Migration?
Authors: Partridge Mark D. , Rickman Dan S.
Published: March 2003

Although there is evidence that regional employment growth benefits current residents, an unexplored aspect of this relationship is the industry composition of the growth. Using 1981–1991 migration data for the 48 contiguous U.S. states, this paper examines whether the industry mix of employment growth matters for migration. We find that state employment growth that results from having a larger share of nationally fast‐growing industries leads to less net in‐migration compared to growth that results from each industry in the state growing faster than its national average. Therefore, state employment growth that is attributable to its mix of industries yields greater benefits for current state residents.
TheConvergence of the Italian Regions and Unemployment: Theory and Evidence
Authors: Carmeci Gaetano , Mauro Luciano
Published: March 2003

We explore the links between the halt of the convergence process of Italian regions at the beginning of the 1970s and the increase in regional unemployment dispersion. We consider a neoclassical exogenous growth model with an imperfect labor market and show that during the transitional dynamics the imperfections of the labor market negatively influence the output growth rate. In particular, the model implies that centralized bargaining is likely to set a national minimum wage that is too high with respect to the labor productivity of the less developed regions, resulting in a negative impact on their per capita output growth. We test the implications of the model on a regional panel data set using the GMM framework. Both our market distortion measure and the unemployment rate are found to significantly lower the growth rate of per capita output.
Unlike a Fool, He Is Not Defiled: Ascetic Purity and Ethics in the Samnyāsa Upanisads
Authors: Vail Lise F.
Published: March 2003

The authors of the Samnyāsa Upanisads, manuals of ascetic lifestyle and practice, recommend that wanderers renounce behavioral standards of their formerly Brahmin householder life, including ritual purity and familial duties. Patrick Olivelle argues that these ascetics are thereafter considered impure and corpse– or ghoul–like, clearly lacking in dharma. However, these Upanisads counsel pursuing mental purity and moral behavior, and modeling oneself after the perfection of the Absolute. This essay investigates ascetic notions of purity and identity, and virtues such as non–violence and kindness cultivated in forest isolation. Is ascetic dharma universal in intent, and is it conceptually opposed to householder dharma? What type of ethics is admired by the authors, what type deprecated? Olivelle’s position is reevaluated, as is Jeffrey Kripal’s notion that monistic mysticism does not support ethics adequately.
Humility and Passion: A Caitanyite Vaishnava Ethics of Devotion
Authors: Schweig Graham M.
Published: March 2003

Two axiological elements—humility and passion—I argue, are at the ethical core of Bengal Vaishnavism. These modes of behavior, derived from early theological sources, are dialectically related and form the basis for an ethics of devotion that allows the devotee to accept, while simultaneously transcending social norms and identities. I draw primarily from what is considered the most honored story of the Bhāgavata Purāna, the Rāsallā, involving the cowherd maidens who exhibit the highest devotion to God, and from the Caitanya Caritāmrta, the biography of the sixteenth century revivalist and saint Caitanya, the tradition’s model for devotional life.
“Religion” and Christian Conversionin African History: A New Model
Authors: Landau Paul
Published: March 2003

This article reviews recent scholarship on African religion and argues that, while much has been accomplished, historians have inherited a problematic view of the processes that they have investigated. They have unknowingly adopted evangelical ideas, in the form of written words and concepts that they wrongly assume have maintained consistent meanings down through the decades. Because missionaries’ translations have been taken in this way as accurate guides for understanding what gave rise to them, much of Africa’s intellectual history appears religious. The article focuses on the example of tui‐qua, a term used by Khoikhoi and translated as “God,” and suggests a model for understanding evangelism and conversion that does not rely on the supposed ubiquity of religion. It is argued that missionaries used Christian notions to accommodate the falsity of analogous practices, which although often unrelated in their original setting, together became a single entity (religion) as a result.
Exorcism as Empowerment: A New Idiom
Authors: Marshman Michelle
Published: March 2003

During the years 1610 and 1611, two Ursuline novices underwent an exorcism in southern France. In the spring of 1611, their confessor, Father Louis Gaufridy, was condemned of witchcraft and rape by Dominican inquisitors, and burned at the stake by state ofﬁcials. The Dominican inquisitors found Father Gaufridy guilty of causing the Ursulines to be possessed by demons, and luring the young women to caves where they participated in illicit activities. Rather than accepting the role of victim, the Ursulines, working in collusion, accomplished the miraculous: public exoneration, and reintegration into their religious community accompanied not only by a reclaimed stature as women religious, but by an elevated stature. Through an intricate play of accusation and expiation, carefully worked through the demons that possessed them, the Ursuline novices reconstructed themselves as virtuous women religious.
Constantine and the Bishop: The Roman Church in the Early Fourth Century
Authors: Leadbetter Bill
Published: March 2003

When Constantine first entered Rome after his defeat of Maxentius in October 312, he encountered a rich and complex Christian community and a bishop whose position was precarious. Centuries of growth and a long religious peace had resulted in the development of a large number of locally based communities in Rome with their own centres of worship ‐ the tituli. Constantine needed to convince these communities of his bona fides as a Christian emperor. The bishop of Rome, Miltiades, was the ruler of a relatively newly unified see, recently fractured by persecution and controversy. Miltiades’ relative weakness was to Constantine’s political advantage, especially since it made him eager to receive the vast ocean of generosity which Constantine began to pour into the Roman Church. The particular and immediate beneficiary of this generosity was the bishop, who gained a vast and lavishly appointed cathedral, and a palace to go with it.
Long overdue? Another look at the National Literacy Strategy
Authors: Beard Roger
Published: March 2003

This paper discusses the teaching approaches which English primary schools are encouraged to use as part of the National Literacy Strategy (NLS) and the ways in which the case for their increased use is supported by research and inspection evidence.

This evidence suggests that, in the years immediately before the NLS was implemented, early reading in English primary schools was largely taught by individualised methods. The skills for dealing with information texts were taught rather patchily. Links between reading and writing were often not directly made. The paper argues that the reasons for this individualised pedagogy can be traced back to the influence of the Plowden Report of 1967. The limitations of individualised teaching with whole classes have subsequently been highlighted by school effectiveness research. The paper outlines the studies which have influenced the NLS in promoting greater direct interactive teaching of literacy and notes some critical issues which are still to be resolved.
The effects of bilingualism on learning to read English: evidence from the contrast between Urdu‐English bilingual and English monolingual children
Authors: Mumtaz Shazia , Humphreys Glyn
Published: March 2003

Learning to read in a shallow alphabetic orthography such as Urdu may depend primarily on phonological processing skills, whilst learning to read in a deeper orthography, such as English, may place more reliance on visual processing skills. This study explores the effects of Urdu on the acquisition of English literacy skills by comparing the reading, memory and phonological processing skills of bilingual Urdu‐English and monolingual English children (7–8 years). The bilingual children had more difficulty in reading irregular English words, but were better at reading regular words and nonwords compared to the monolinguals. The poor performance of the bilingual children with irregular English words was linked to their poor visual memory skills, whilst their good performance with regular words and nonwords was related to the presence of enhanced phonological skills. The results demonstrate the transfer of first language skills to reading development in a second language. In English, first language skills can facilitate the development of either lexical or non‐lexical routes to reading.
Cohabitation in Great Britain: Not for Long, but Here to Stay
Authors: Ermisch John , Francesconi Marco
Published: March 2003

This paper uses a new source of data to study the dramatic increase in cohabiting unions in Great Britain. It analyses, in turn, entry into first partnership, the stability of cohabiting unions and repartnering after dissolution of cohabitation. In excess of 70% of first partnerships are now cohabitations, and these last a relatively short time before being either turned into marriage or dissolved. The shift to cohabitation as the dominant mode of first partnership plays an important role in the delay of first marriage and motherhood. The paper also investigates the factors that are associated with the outcome of cohabitations.
Correcting for Regression Dilution Bias: Comparison of Methods for a Single Predictor Variable
Authors: Frost Chris , Thompson Simon G.
Published: March 2003

In an epidemiological study the regression slope between a response and predictor variable is underestimated when the predictor variable is measured imprecisely. Repeat measurements of the predictor in individuals in a subset of the study or in a separate study can be used to estimate a multiplicative factor to correct for this ‘regression dilution bias’. In applied statistics publications various methods have been used to estimate this correction factor. Here we compare six different estimation methods and explain how they fall into two categories, namely regression and correlation-based methods. We provide new asymptotic variance formulae for the optimal correction factors in each category, when these are estimated from the repeat measurements subset alone, and show analytically and by simulation that the correlation method of choice gives uniformly lower variance. The simulations also show that, when the correction factor is not much greater than 1, this correlation method gives a correction factor which is closer to the true value than that from the best regression method on up to 80% of occasions. We also provide a variance formula for a modified correlation method which uses the standard deviation of the predictor variable in the main study; this shows further improved performance provided that the correction factor is not too extreme. A confidence interval for a corrected regression slope in an epidemiological study should reflect the imprecision of both the uncorrected slope and the estimated correction factor. We provide formulae for this and show that, particularly when the correction factor is large and the size of the subset of repeat measures is small, the effect of allowing for imprecision in the estimated correction factor can be substantial.
Proposals for 2001 Samples of Anonymized Records: An Assessment of Disclosure Risk
Authors: Dale Angela , Elliot Mark
Published: March 2003

In 1991 Marsh and co-workers made the case for a sample of anonymized records (SAR) from the 1991 census of population. The case was accepted by the Office for National Statistics (then the Office of Population Censuses and Surveys) and a request was made by the Economic and Social Research Council to purchase the SARs. Two files were released for Great Britain—a 2% sample of individuals and a 1% sample of households. Subsequently similar samples were released for Northern Ireland. Since their release, the files have been heavily used for research and there has been no known breach of confidentiality. There is a considerable demand for similar files from the 2001 census, with specific requests for a larger sample size and lower population threshold for the individual SAR. This paper reassesses the analysis of Marsh and co-workers of the risk of identification of an individual or household in a sample of microdata from the 1991 census and also uses alternative ways of assessing risks with the 1991 SARs. The results of both the reassessment and the new analyses are reassuring and allow us to take the 1991 SARs as a base-line against which to assess proposals for changes to the size and structure of samples from the 2001 census.
The Role of Tobacco Taxes in Starting and Quitting Smoking: Duration Analysis of British Data
Authors: Forster Martin , Jones Andrew M.
Published: March 2003

The annual 5% increase in tobacco taxes in real terms proposed in the recent White Paper on smoking has reaffirmed the commitment of successive UK Governments to above-inflation increases in tobacco taxation to encourage people to stop smoking. This paper presents evidence on the determinants of starting and quitting smoking by using data from the British Health and Lifestyle Survey and is the first to identify tax elasticities for starting and quitting smoking using British data. Self-reported individual smoking histories are coupled with a long time series for the tax rate on cigarettes to construct a longitudinal data set. Estimates are obtained for the effect of above-inflation tax rises on the age of starting smoking and the number of years of smoking. The estimates of the tax elasticity of the age of starting smoking are 0.16 for men and 0.08 for women. The estimates of the tax elasticity of quitting are −0.60 for men and −0.46 for women. These are robust to different specifications.
Modelling the Data Measurement Process for the Index of Production
Authors: Patterson K. D.
Published: March 2003

Many statistical series that are available from official agencies, such as the Office for National Statistics in the UK and the Bureau of Economic Analysis in the USA, are subject to an extensive process of revision and refinement. This feature of the data is often not explicitly recognized by users even though it may be important to their use of the data. The starting-point of this study is to conceptualize and model the data measurement process as it is relevant to the index of production (IOP). The IOP attracts considerable attention because of its timely publication and importance as an indicator of the UK's industrial base. This study shows that there is one common stochastic trend (and one common factor in terms of observable variables) ‘driving’ 13 vintages of data on the IOP. Necessary and sufficient conditions are derived for the ‘final’ vintage of data on the IOP to be the permanent component of the series in the Gonzalo–Granger sense, and the revisions to be the transitory components. These conditions are not satisfied for the IOP; hence, the per-manent component is a function of all the published vintages.
Political Generations and Partisanship in the UK, 1964–1997
Authors: Tilley James
Published: March 2003

Political partisanship is often claimed to be influenced by generational and life-cycle processes, with both being cited as the factor that is responsible for higher levels of Conservative identifications among older voters. Given the existence of over-time change it is difficult to assess the validity of these claims as even with repeated survey data any model is underidentified. This paper uses smoothed additive models to isolate and examine the non-linear component of the generational effect. Some identifying assumptions are presented to try to assess the extent to which linear aging or generational processes are responsible for the increased Conservatism of the elderly. The advantage of the smoothed additive models is their ability to highlight non-linear effects, however, and this paper shows that regardless of linear trends people who entered the electorate during Conservative Parliaments are more likely to be Conservative partisan identifiers many years later. The introduction of a multiplicative term linking age to period effects supports this hypothesis by showing that younger people are more susceptible to the influence of period effects.
Testing for Programme Effects in a Regression Discontinuity Design with Imperfect Compliance
Authors: Battistin Erich , Rettore Enrico
Published: March 2003

The administrators of an office automation training programme in Italy enrolled applicants on the basis of their score in an attitudinal test, with low scoring subjects mandated out of the programme. Some of the applicants who were mandated out resorted to an alternative programme. To identify the effect of the programme by comparing participants with non-participants we need to account properly for both the selection by the score and the contamination of the comparison group by a number of non-complying subjects. The estimand resulting from using the mandated status as an instrumental variable for the actual status identifies the effect of the programme on complying subjects exhibiting a score in the attitudinal test in the neighbourhood of the threshold for selection. Simple nonparametric instrumental variable estimators based on the work of Robinson and Hahn and co-workers reveal that the programme had no effect on the probability of being in work several months after its completion. Simulation results show that in spite of the small sample size the test for the no-impact hypothesis has non-negligible power even at small departures from the null hypothesis. As a side-result Robinson's test turns out to be appreciably more powerful than the other test.
Consistency of Procrustes Estimators
Authors: Kent John T. , Mardia Kanti V.
Published: March 2003

Lele has shown that the Procrustes estimator of form is inconsistent and raised the question about the consistency of the Procrustes estimator of shape. In this paper the consistency of estimators of form and shape is studied under various assumptions. In particular, it is shown that the Procrustes estimator of shape is consistent under the assumption of an isotropic error distribution and that consistency breaks down if the assumption of isotropy is relaxed. The relevance of these results for practical shape analysis is discussed. As a by-product, some new results are derived for the offset uniform distribution from directional data.
Wavelet Threshold Estimators for Data with Correlated Noise
Authors: Johnstone Iain M. , Silverman Bernard W.
Published: March 2003

Wavelet threshold estimators for data with stationary correlated noise are constructed by applying a level-dependent soft threshold to the coefficients in the wavelet transform. A variety of threshold choices is proposed, including one based on an unbiased estimate of mean-squared error. The practical performance of the method is demonstrated on examples, including data from a neurophysiological context. The theoretical properties of the estimators are investigated by comparing them with an ideal but unattainable ‘bench-mark', that can be considered in the wavelet context as the risk obtained by ideal spatial adaptivity, and more generally is obtained by the use of an ‘oracle' that provides information that is not actually available in the data. It is shown that the level-dependent threshold estimator performs well relative to the bench-mark risk, and that its minimax behaviour cannot be improved on in order of magnitude by any other estimator. The wavelet domain structure of both short- and long-range dependent noise is considered, and in both cases it is shown that the estimators have near optimal behaviour simultaneously in a wide range of function classes, adapting automatically to the regularity properties of the underlying model. The proofs of the main results are obtained by considering a more general multivariate normal decision theoretic problem.
On Robust Analysis of a Normal Location Parameter
Authors: Choy S. T. Boris , Smith Adrian F. M.
Published: March 2003

Pericchi and Smith considered a normal location parameter problem with double-exponential and Student t prior distributions. These two prior distributions both belong to the class of scale mixtures of normal distributions and are useful in providing a robust analysis of the normal location parameter problem. In this paper we extend the analysis to other scale mixtures of normal distributions, such as the exponential power and the symmetric stable distributions.
Modelling Dependence within Joint Tail Regions
Authors: Ledford Anthony W. , Tawn Jonathan A.
Published: March 2003

Standard approaches for modelling dependence within joint tail regions are based on extreme value methods which assume max-stability, a particular form of joint tail dependence. We develop joint tail models based on a broader class of dependence structure which provides a natural link between max-stable models and weaker forms of dependence including independence and negative association. This approach overcomes many of the problems that are encountered with standard methods and is the basis for a Poisson process representation that generalizes existing bivariate results. We apply the new techniques to simulated and environmental data, and demonstrate the marked advantage that the new approach offers for joint tail extrapolation.
On Bayesian D-optimum Design Criteria and the Equivalence Theorem in Non-linear Models
Authors: Firth D. , Hinde J. P.
Published: March 2003

The concavity of some Bayesian D-optimality criteria is investigated and is found in some cases to depend on the prior distribution. In the case of a non-concave criterion, the standard equivalence theorem may fail, but a local version continues to apply.
Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion
Authors: Hurvich Clifford M. , Simonoff Jeffrey S. , Tsai Chih-Ling
Published: March 2003

Many different methods have been proposed to construct nonparametric estimates of a smooth regression function, including local polynomial, (convolution) kernel and smoothing spline estimators. Each of these estimators uses a smoothing parameter to control the amount of smoothing performed on a given data set. In this paper an improved version of a criterion based on the Akaike information criterion (AIC), termed AICC, is derived and examined as a way to choose the smoothing parameter. Unlike plug-in methods, AICC can be used to choose smoothing parameters for any linear smoother, including local quadratic and smoothing spline estimators. The use of AICC avoids the large variability and tendency to undersmooth (compared with the actual minimizer of average squared error) seen when other ‘classical’ approaches (such as generalized cross-validation (GCV) or the AIC) are used to choose the smoothing parameter. Monte Carlo simulations demonstrate that the AICC-based smoothing parameter is competitive with a plug-in method (assuming that one exists) when the plug-in method works well but also performs well when the plug-in approach fails or is unavailable.
Testing for Pairwise Serial Independence Via the Empirical Distribution Function
Authors: Hong Yongmiao
Published: March 2003

Built on Skaug and Tjøstheim's approach, this paper proposes a new test for serial independence by comparing the pairwise empirical distribution functions of a time series with the products of its marginals for various lags, where the number of lags increases with the sample size and different lags are assigned different weights. Typically, the more recent information receives a larger weight. The test has some appealing attributes. It is consistent against all pairwise dependences and is powerful against alternatives whose dependence decays to zero as the lag increases. Although the test statistic is a weighted sum of degenerate Cramér–von Mises statistics, it has a null asymptotic N(0, 1) distribution. The test statistic and its limit distribution are invariant to any order preserving transformation. The test applies to time series whose distributions can be discrete or continuous, with possibly infinite moments. Finally, the test statistic only involves ranking the observations and is computationally simple. It has the advantage of avoiding smoothed nonparametric estimation. A simulation experiment is conducted to study the finite sample performance of the proposed test in comparison with some related tests.
Multivariate Bayesian Variable Selection and Prediction
Authors: Brown P. J. , Vannucci M. , Fearn T.
Published: March 2003

The multivariate regression model is considered with p regressors. A latent vector with p binary entries serves to identify one of two types of regression coefficients: those close to 0 and those not. Specializing our general distributional setting to the linear model with Gaussian errors and using natural conjugate prior distributions, we derive the marginal posterior distribution of the binary latent vector. Fast algorithms aid its direct computation, and in high dimensions these are supplemented by a Markov chain Monte Carlo approach to sampling from the known posterior distribution. Problems with hundreds of regressor variables become quite feasible. We give a simple method of assigning the hyperparameters of the prior distribution. The posterior predictive distribution is derived and the approach illustrated on compositional analysis of data involving three sugars with 160 near infrared absorbances as regressors.
Intentionally Biased Bootstrap Methods
Authors: Hall Peter , Presnell Brett
Published: March 2003

A class of weighted bootstrap techniques, called biased bootstrap or b-bootstrap methods, is introduced. It is motivated by the need to adjust empirical methods, such as the ‘uniform’ bootstrap, in a surgical way to alter some of their features while leaving others unchanged. Depending on the nature of the adjustment, the b-bootstrap can be used to reduce bias, or to reduce variance or to render some characteristic equal to a predetermined quantity. Examples of the last application include a b-bootstrap approach to hypothesis testing in nonparametric contexts, where the b-bootstrap enables simulation ‘under the null hypothesis’, even when the hypothesis is false, and a b-bootstrap competitor to Tibshirani's variance stabilization method. An example of the bias reduction application is adjustment of Nadaraya–Watson kernel estimators to make them competitive with local linear smoothing. Other applications include density estimation under constraints, outlier trimming, sensitivity analysis, skewness or kurtosis reduction and shrinkage.
Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
Authors: Booth James G. , Hobert James P.
Published: March 2003

Two new implementations of the EM algorithm are proposed for maximum likelihood fitting of generalized linear mixed models. Both methods use random (independent and identically distributed) sampling to construct Monte Carlo approximations at the E-step. One approach involves generating random samples from the exact conditional distribution of the random effects (given the data) by rejection sampling, using the marginal distribution as a candidate. The second method uses a multivariate t importance sampling approximation. In many applications the two methods are complementary. Rejection sampling is more efficient when sample sizes are small, whereas importance sampling is better with larger sample sizes. Monte Carlo approximation using random samples allows the Monte Carlo error at each iteration to be assessed by using standard central limit theory combined with Taylor series methods. Specifically, we construct a sandwich variance estimate for the maximizer at each approximate E-step. This suggests a rule for automatically increasing the Monte Carlo sample size after iterations in which the true EM step is swamped by Monte Carlo error. In contrast, techniques for assessing Monte Carlo error have not been developed for use with alternative implementations of Monte Carlo EM algorithms utilizing Markov chain Monte Carlo E-step approximations. Three different data sets, including the infamous salamander data of McCullagh and Nelder, are used to illustrate the techniques and to compare them with the alternatives. The results show that the methods proposed can be considerably more efficient than those based on Markov chain Monte Carlo algorithms. However, the methods proposed may break down when the intractable integrals in the likelihood function are of high dimension.
Inference in Generalized Additive Mixed Models by Using Smoothing Splines
Authors: Lin X. , Zhang D.
Published: March 2003

Generalized additive mixed models are proposed for overdispersed and correlated data, which arise frequently in studies involving clustered, hierarchical and spatial designs. This class of models allows flexible functional dependence of an outcome variable on covariates by using nonparametric regression, while accounting for correlation between observations by using random effects. We estimate nonparametric functions by using smoothing splines and jointly estimate smoothing parameters and variance components by using marginal quasi-likelihood. Because numerical integration is often required by maximizing the objective functions, double penalized quasi-likelihood is proposed to make approximate inference. Frequentist and Bayesian inferences are compared. A key feature of the method proposed is that it allows us to make systematic inference on all model components within a unified parametric mixed model framework and can be easily implemented by fitting a working generalized linear mixed model by using existing statistical software. A bias correction procedure is also proposed to improve the performance of double penalized quasi-likelihood for sparse data. We illustrate the method with an application to infectious disease data and we evaluate its performance through simulation.
Convergence of Slice Sampler Markov Chains
Authors: Roberts Gareth O. , Rosenthal Jeffrey S.
Published: March 2003

We analyse theoretical properties of the slice sampler. We find that the algorithm has extremely robust geometric ergodicity properties. For the case of just one auxiliary variable, we demonstrate that the algorithm is stochastically monotone, and we deduce analytic bounds on the total variation distance from stationarity of the method by using Foster–Lyapunov drift condition methodology.
One-step Local Quasi-likelihood Estimation
Authors: Fan Jianqing , Chen J.
Published: March 2003

Local quasi-likelihood estimation is a useful extension of local least squares methods, but its computational cost and algorithmic convergence problems make the procedure less appealing, particularly when it is iteratively used in methods such as the back-fitting algorithm, cross-validation and bootstrapping. A one-step local quasi-likelihood estimator is introduced to overcome the computational drawbacks of the local quasi-likelihood method. We demonstrate that as long as the initial estimators are reasonably good, the one-step estimator has the same asymptotic behaviour as the local quasi-likelihood method. Our simulation shows that the one-step estimator performs at least as well as the local quasi-likelihood method for a wide range of choices of bandwidths. A data-driven bandwidth selector is proposed for the one-step estimator based on the pre-asymptotic substitution method of Fan and Gijbels. It is then demonstrated that the data-driven one-step local quasi-likelihood estimator performs as well as the maximum local quasi-likelihood estimator by using the ideal optimal bandwidth.
On Confidence Intervals Associated With the Usual and Adjusted Likelihoods
Authors: Mukerjee Rahul , Reid N.
Published: March 2003

In the presence of nuisance parameters, we drive an explicit higher order asymptotic formula to compare the expected lengths of confidence intervals given by likelihood ratio statistics arising from the usual profile likelihood and various adjustments thereof. Highest posterior density regions, with approximate frequentist validity, are also included in the study.
Bayesian Inference in Hidden Markov Models Through the Reversible Jump Markov Chain Monte Carlo Method
Authors: Robert Chiristian P. , Rydén Tobias , Titterington D. M.
Published: March 2003

Hidden Markov models form an extension of mixture models which provides a flexible class of models exhibiting dependence and a possibly large degree of variability. We show how reversible jump Markov chain Monte Carlo techniques can be used to estimate the parameters as well as the number of components of a hidden Markov model in a Bayesian framework. We employ a mixture of zero-mean normal distributions as our main example and apply this model to three sets of data from finance, meteorology and geomagnetism.
On a Mixture Autoregressive Model
Authors: Wong Chun Shan , Li W. K.
Published: March 2003

We generalize the Gaussian mixture transition distribution (GMTD) model introduced by Le and co-workers to the mixture autoregressive (MAR) model for the modelling of non-linear time series. The models consist of a mixture of K stationary or non-stationary AR components. The advantages of the MAR model over the GMTD model include a more full range of shape changing predictive distributions and the ability to handle cycles and conditional heteroscedasticity in the time series. The stationarity conditions and autocorrelation function are derived. The estimation is easily done via a simple EM algorithm and the model selection problem is addressed. The shape changing feature of the conditional distributions makes these models capable of modelling time series with multimodal conditional distributions and with heteroscedasticity. The models are applied to two real data sets and compared with other competing models. The MAR models appear to capture features of the data better than other competing models do.
Inference for Multivariate Normal Hierarchical Models
Authors: Everson Philip J. , Morris Carl N.
Published: March 2003

This paper provides a new method and algorithm for making inferences about the parameters of a two-level multivariate normal hierarchical model. One has observed J p-dimensional vector outcomes, distributed at level 1 as multivariate normal with unknown mean vectors and with known covariance matrices. At level 2, the unknown mean vectors also have normal distributions, with common unknown covariance matrix A and with means depending on known covariates and on unknown regression coefficients. The algorithm samples independently from the marginal posterior distribution of A by using rejection procedures. Functions such as posterior means and covariances of the level 1 mean vectors and of the level 2 regression coefficient are estimated by averaging over posterior values calculated conditionally on each value of A drawn. This estimation accounts for the uncertainty in A, unlike standard restricted maximum likelihood empirical Bayes procedures. It is based on independent draws from the exact posterior distributions, unlike Gibbs sampling. The procedure is demonstrated for profiling hospitals based on patients' responses concerning p=2 types of problems (non-surgical and surgical). The frequency operating characteristics of the rule corresponding to a particular vague multivariate prior distribution are shown via simulation to achieve their nominal values in that setting.
Modelling and Smoothing Parameter Estimation With Multiple Quadratic Penalties
Authors: Wood S. N.
Published: March 2003

Penalized likelihood methods provide a range of practical modelling tools, including spline smoothing, generalized additive models and variants of ridge regression. Selecting the correct weights for penalties is a critical part of using these methods and in the single-penalty case the analyst has several well-founded techniques to choose from. However, many modelling problems suggest a formulation employing multiple penalties, and here general methodology is lacking. A wide family of models with multiple penalties can be fitted to data by iterative solution of the generalized ridge regression problem minimize ||W  1/2 (Xp−y) ||2ρ+Σi=1  m θi  p′S  i  p (p is a parameter vector, X a design matrix, S  i a non-negative definite coefficient matrix defining the ith penalty with associated smoothing parameter θi, W a diagonal weight matrix, y a vector of data or pseudodata and ρ an ‘overall’ smoothing parameter included for computational efficiency). This paper shows how smoothing parameter selection can be performed efficiently by applying generalized cross-validation to this problem and how this allows non-linear, generalized linear and linear models to be fitted using multiple penalties, substantially increasing the scope of penalized modelling methods. Examples of non-linear modelling, generalized additive modelling and anisotropic smoothing are given.
Semiparametric Regression for the Mean and Rate Functions of Recurrent Events
Authors: Lin D. Y. , Wei L. J. , Yang I. , Ying Z.
Published: March 2003

The counting process with the Cox-type intensity function has been commonly used to analyse recurrent event data. This model essentially assumes that the underlying counting process is a time-transformed Poisson process and that the covariates have multiplicative effects on the mean and rate function of the counting process. Recently, Pepe and Cai, and Lawless and co-workers have proposed semiparametric procedures for making inferences about the mean and rate function of the counting process without the Poisson-type assumption. In this paper, we provide a rigorous justification of such robust procedures through modern empirical process theory. Furthermore, we present an approach to constructing simultaneous confidence bands for the mean function and describe a class of graphical and numerical techniques for checking the adequacy of the fitted mean–rate model. The advantages of the robust procedures are demonstrated through simulation studies. An illustration with multiple-infection data taken from a clinical study on chronic granulomatous disease is also provided.
Cochran's Rule for Simple Random Sampling
Authors: Sugden R. A. , Smith T. M. F. , Jones R. P.
Published: March 2003

Cochran's rule for the minimum sample size to ensure adequate coverage of nominal 95% confidence intervals is derived by using the Edgeworth expansion for the distribution function of the standardized sample mean. The rule is extended for confidence intervals based on the Studentized sample mean. The performance of the rule and Edgeworth approximations for smaller sample sizes are examined by simulation.
On Repeated Measures Analysis with Misspecified Covariance Structure
Authors: Crowder Martin
Published: March 2003

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

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

We 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

I 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

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

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.