bluuebunny/crossref_metadata_2025 huggingface dataset:
Detecting Possibly Non-Consecutive Outliers in Industrial Time Series
Authors: Luceñ0 Alberto
Published: March 2003

A method for robust estimation and multiple outlier detection in time series generated by autoregressive integrated moving average processes in industrial environments is developed. The procedure is based on reweighted maximum likelihood estimation using Huber or redescending weights and, therefore, generalizes the well-established robust M-estimation procedures used in the regression framework. When the scalar process is non-stationary, the computations required can be performed equally well using either rhe original undifferenced series or auxiliary differenced series. Whereas the latter alternative may be preferred for scalar series, the former might be extended to cope with vector partially non-stationary time series without differencing the series, thus avoiding non-invertibility and parameter identifiability problems caused by overdifferencing. The overall strategy is applied in two real industrial data sets.
Double-blind Deconvolution: The Analysis of Post-synaptic Currents in Nerve Cells
Authors: Poskitt D. S. , Doğançay K. , Chung Shin-Ho
Published: March 2003

This paper is concerned with the analysis of observations made on a system that is being stimulated at fixed time intervals but where the precise nature and effect of any individual stimulus is unknown. The realized values are modelled as a stochastic process consisting of a random signal embedded in noise. The aim of the analysis is to use the data to unravel the unknown structure of the system and to ascertain the probabilistic behaviour of the stimuli. A method of parameter estimation based on quasi-profile likelihood is presented and the statistical properties of the estimates are established while recognizing that there will be a discrepancy between the model and the true data-generating mechanism. A method of model validation and determination is also advanced and kernel smoothing techniques are proposed as a basis for identifying the amplitude distribution of the stimuli. The data processing techniques described have a direct application to the investigation of excitatory post-synaptic currents recorded from nerve cells in the central nervous system and their use in quantal analysis of such data is illustrated.
Prior Elicitation, Variable Selection and Bayesian Computation for Logistic Regression Models
Authors: Chen Ming-Hui , Ibrahim Joseph G. , Yiannoutsos Constantin
Published: March 2003

Bayesian selection of variables is often difficult to carry out because of the challenge in specifying prior distributions for the regression parameters for all possible models, specifying a prior distribution on the model space and computations. We address these three issues for the logistic regression model. For the first, we propose an informative prior distribution for variable selection. Several theoretical and computational properties of the prior are derived and illustrated with several examples. For the second, we propose a method for specifying an informative prior on the model space, and for the third we propose novel methods for computing the marginal distribution of the data. The new computational algorithms only require Gibbs samples from the full model to facilitate the computation of the prior and posterior model probabilities for all possible models. Several properties of the algorithms are also derived. The prior specification for the first challenge focuses on the observables in that the elicitation is based on a prior prediction y  0 for the response vector and a quantity a  0 quantifying the uncertainty in y  0. Then, y  0 and a  0 are used to specify a prior for the regression coefficients semi-automatically. Examples using real data are given to demonstrate the methodology.
The Complex Watson Distribution and Shape Analysis
Authors: Mardia K. V. , Dryden I. L.
Published: March 2003

The complex Watson distribution is an important simple distribution on the complex sphere which is used in statistical shape analysis. We describe the density, obtain the integrating constant and provide large sample approximations. Maximum likelihood estimation and hypothesis testing procedures for one and two samples are described. The particular connection with shape analysis is discussed and we consider an application examining shape differences between normal and schizophrenic brains. We make some observations about Bayesian shape inference and finally we describe a more general rotationally symmetric family of distributions.
A Weighted Bootstrap Approach to Bootstrap Iteration
Authors: Hall Peter , Maesono Y.
Published: March 2003

The operation of resampling from a bootstrap resample, encountered in applications of the double bootstrap, maybe viewed as resampling directly from the sample but using probability weights that are proportional to the numbers of times that sample values appear in the resample. This suggests an approximate approach to double-bootstrap Monte Carlo simulation, where weighted bootstrap methods are used to circumvent much of the labour involved in compounded Monte Carlo approximation. In the case of distribution estimation or, equivalently, confidence interval calibration, the new method may be used to reduce the computational labour. Moreover, the method produces the same order of magnitude of coverage error for confidence intervals, or level error for hypothesis tests, as a full application of the double bootstrap.
Recursive Formulae for the Average Efficiency Factor in Block and Row-Column Designs
Authors: John J. A. , Whitaker D.
Published: March 2003

Interchange algorithms are widely used to construct efficient block and row-column designs. We provide simple recursive formulae for updating this average efficiency factor, so that it is no longer computationally expensive to calculate it after each possible interchange.
Parametric and Semiparametric Models for Recapture and Removal Studies: A Likelihood Approach
Authors: Chen Kani
Published: March 2003

Capture–recapture processes are biased samplings of recurrent event processes, which can be modelled by the Andersen–Gill intensity model. The intensity function is assumed to be a function of time, covariates and a parameter. We derive the maximum likelihood estimators of both the parameter and the population size and show the consistency and asymptotic normality of the estimators for both recapture and removal studies. The estimators are asymptotically efficient and their theoretical asymptotic relative efficiencies with respect to the existing estimators of Yip and co-workers can be as large as ∞. The variance estimation and a numerical example are also presented.
Improving Coverage Accuracy of Nonparametric Prediction Intervals
Authors: Hall Peter , Rieck Andrew
Published: March 2003

Methods are suggested for improving the coverage accuracy of intervals for predicting future values of a random variable drawn from a sampled distribution. It is shown that properties of solutions to such problems may be quite unexpected. For example, the bootstrap and the jackknife perform very poorly when used to calibrate coverage, although the jackknife estimator of the true coverage is virtually unbiased. A version of the smoothed bootstrap can be employed for successful calibration, however. Interpolation among adjacent order statistics can also be an effective way of calibrating, although even there the results are unexpected. In particular, whereas the coverage error can be reduced from O(n  -1) to orders O(n  -2) and O(n  -3) (where n denotes the sample size) by interpolating among two and three order statistics respectively, the next two orders of reduction require interpolation among five and eight order statistics respectively.
Using Information on Realized Effects to Determine Prospective Causal Effects
Authors: Joffe Marshall M.
Published: March 2003

The potential outcomes approach to causal inference postulates that each individual has a number of possibly latent outcomes, each of which would be observed under a different treatment. For any individual, some of these outcomes will be unobservable or counterfactual. Information about post-treatment characteristics sometimes allows statements about what would have happened if an individual or group with these characteristics had received a different treatment. These are statements about the realized effects of the treatment. Determining the likely effect of an intervention before making a decision involves inference about effects in populations defined only by characteristics observed before decisions about treatment are made. Information on realized effects can tighten bounds on these prospectively defined measures of the intervention effect. We derive formulae for the bounds and their sampling variances and illustrate these points with data from a hypothetical study of the efficacy of screening mammography.
Criteria for Confounders in Epidemiological Studies
Authors: Geng Zhi , Guo Jianhua , Fung Wing-Kam
Published: March 2003

The paper addresses a formal definition of a confounder based on the qualitative definition that is commonly used in standard epidemiology text-books. To derive the criterion for a factor to be a confounder given by Miettinen and Cook and to clarify inconsistency between various criteria for a confounder, we introduce the concepts of an irrelevant factor, an occasional confounder and a uniformly irrelevant factor. We discuss criteria for checking these and show that Miettinen and Cook's criterion can also be applied to occasional confounders. Moreover, we consider situations with multiple potential confounders, and we obtain two necessary conditions that are satisfied by each confounder set. None of the definitions and results presented in this paper require the ignorability and sufficient control confounding assumptions which are commonly employed in observational and epidemiological studies.
Consistency of Bernstein Polynomial Posteriors
Authors: Petrone Sonia , Wasserman Larry
Published: March 2003

A Bernstein prior is a probability measure on the space of all the distribution functions on [0, 1]. Under very general assumptions, it selects absolutely continuous distribution functions, whose densities are mixtures of known beta densities. The Bernstein prior is of interest in Bayesian nonparametric inference with continuous data. We study the consistency of the posterior from a Bernstein prior. We first show that, under mild assumptions, the posterior is weakly consistent for any distribution function P0 on [0, 1] with continuous and bounded Lebesgue density. With slightly stronger assumptions on the prior, the posterior is also Hellinger consistent. This implies that the predictive density from a Bernstein prior, which is a Bayesian density estimate, converges in the Hellinger sense to the true density (assuming that it is continuous and bounded). We also study a sieve maximum likelihood version of the density estimator and show that it is also Hellinger consistent under weak assumptions. When the order of the Bernstein polynomial, i.e. the number of components in the beta distribution mixture, is truncated, we show that under mild restrictions the posterior concentrates on the set of pseudotrue densities. Finally, we study the behaviour of the predictive density numerically and we also study a hybrid Bayes–maximum likelihood density estimator.
Estimates of Regression Coefficients Based on the Sign Covariance Matrix
Authors: Ollila Esa , Oja Hannu , Hettmansperger Thomas P.
Published: March 2003

A new estimator of the regression parameters is introduced in a multivariate multiple-regression model in which both the vector of explanatory variables and the vector of response variables are assumed to be random. The affine equivariant estimate matrix is constructed using the sign covariance matrix (SCM) where the sign concept is based on Oja's criterion function. The influence function and asymptotic theory are developed to consider robustness and limiting efficiencies of the SCM regression estimate. The estimate is shown to be consistent with a limiting multinormal distribution. The influence function, as a function of the length of the contamination vector, is shown to be linear in elliptic cases; for the least squares (LS) estimate it is quadratic. The asymptotic relative efficiencies with respect to the LS estimate are given in the multivariate normal as well as the t-distribution cases. The SCM regression estimate is highly efficient in the multivariate normal case and, for heavy-tailed distributions, it performs better than the LS estimate. Simulations are used to consider finite sample efficiencies with similar results. The theory is illustrated with an example.
Bayesian Measures of Model Complexity and Fit
Authors: Spiegelhalter David J. , Best Nicola G. , Carlin Bradley P. , Van Der Linde Angelika
Published: March 2003

We consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined. Using an information theoretic argument we derive a measure pD for the effective number of parameters in a model as the difference between the posterior mean of the deviance and the deviance at the posterior means of the parameters of interest. In general pD approximately corresponds to the trace of the product of Fisher's information and the posterior covariance, which in normal models is the trace of the ‘hat’ matrix projecting observations onto fitted values. Its properties in exponential families are explored. The posterior mean deviance is suggested as a Bayesian measure of fit or adequacy, and the contributions of individual observations to the fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages. Adding pD to the posterior mean deviance gives a deviance information criterion for comparing models, which is related to other information criteria and has an approximate decision theoretic justification. The procedure is illustrated in some examples, and comparisons are drawn with alternative Bayesian and classical proposals. Throughout it is emphasized that the quantities required are trivial to compute in a Markov chain Monte Carlo analysis.
A Statistical Framework for Expression-Based Molecular Classification in Cancer
Authors: Parmigiani Giovanni , Garrett Elizabeth S. , Anbazhagan Ramaswamy , Gabrielson Edward
Published: March 2003

Genome-wide measurement of gene expression is a promising approach to the identification of subclasses of cancer that are currently not differentiable, but potentially biologically heterogeneous. This type of molecular classification gives hope for highly individualized and more effective prognosis and treatment of cancer. Statistically, the analysis of gene expression data from unclassified tumours is a complex hypothesis-generating activity, involving data exploration, modelling and expert elicitation. We propose a modelling framework that can be used to inform and organize the development of exploratory tools for classification. Our framework uses latent categories to provide both a statistical definition of differential expression and a precise, experiment-independent, definition of a molecular profile. It also generates natural similarity measures for traditional clustering and gives probabilistic statements about the assignment of tumours to molecular profiles.
Sequential Classification on Partially Ordered Sets
Authors: Tatsuoka Curtis , Ferguson Thomas
Published: March 2003

A general theorem on the asymptotically optimal sequential selection of experiments is presented and applied to a Bayesian classification problem when the parameter space is a finite partially ordered set. The main results include establishing conditions under which the posterior probability of the true state converges to 1 almost surely and determining optimal rates of convergence. Properties of a class of experiment selection rules are explored.
Covariate Selection for Estimating the Causal Effect of Control Plans by Using Causal Diagrams
Authors: Kuroki Manabu , Miyakawa Masami
Published: March 2003

Consider a case where cause–effect relationships between variables can be described by a causal path diagram and the corresponding linear structural equation model. The paper proposes a graphical selection criterion for covariates to estimate the causal effect of a control plan. For designing the control plan, it is essential to determine both covariates that are used for control and covariates that are used for identification. The selection of covariates used for control is only constrained by the requirement that the covariates be non-descendants of a treatment variable. However, the selection of covariates used for identification is dependent on the selection of covariates used for control and is not unique. In the paper, the difference between covariates that are used for identification is evaluated on the basis of the asymptotic variance of the estimated causal effect of an effective control plan. Furthermore, the results can be also described in terms of a graph structure.
Locating Genes Involved in Human Diseases
Authors: Curnow R. N. , Morris A. P. , Whittaker J. C.
Published: March 2003

The increasing amount of information that is becoming available about the structure and composition of the DNA constituting the human chromosomes has provided new opportunities to locate genes that affect susceptibilities to a range of diseases. The accurate location of these genes is important in genetic counselling and in understanding the effects of genes that may result in disease. Various methods of analysing the data when DNA information is available at a single marker locus for an affected child and his or her parents are reviewed and applied to data on insulin-dependent diabetes mellitus. The importance of distinguishing between the association of alleles at a marker locus and at a disease locus resulting from chromosomal linkage from that resulting from other causes is emphasized.
Analysis of the Human Sex Ratio by Using Overdispersion Models
Authors: Lindsey J. K. , Altham P. M. E.
Published: March 2003

For study of the human sex ratio, one of the most important data sets was collected in Saxony in the 19th century by Geissler. The data contain the sizes of families, with the sex of all children, at the time of registration of the birth of a child. These data are reanalysed to determine how the probability for each sex changes with family size. Three models for overdispersion are fitted: the beta–binomial model of Skellam, the ‘multiplicative’ binomial model of Altham and the double-binomial model of Efron. For each distribution, both the probability and the dispersion parameters are allowed to vary simultaneously with family size according to two separate regression equations. A finite mixture model is also fitted. The models are fitted using non-linear Poisson regression. They are compared using direct likelihood methods based on the Akaike information criterion. The multiplicative and beta–binomial models provide similar fits, substantially better than that of the double-binomial model. All models show that both the probability that the child is a boy and the dispersion are greater in larger families. There is also some indication that a point probability mass is needed for families containing children uniquely of one sex.
Model-Based Geostatistics
Authors: Diggle P. J. , Tawn J. A. , Moyeed R. A.
Published: March 2003

Conventional geostatistical methodology solves the problem of predicting the realized value of a linear functional of a Gaussian spatial stochastic process S(x) based on observations Yi = S(xi) + Zi at sampling locations xi, where the Zi are mutually independent, zero-mean Gaussian random variables. We describe two spatial applications for which Gaussian distributional assumptions are clearly inappropriate. The first concerns the assessment of residual contamination from nuclear weapons testing on a South Pacific island, in which the sampling method generates spatially indexed Poisson counts conditional on an unobserved spatially varying intensity of radioactivity; we conclude that a conventional geostatistical analysis oversmooths the data and underestimates the spatial extremes of the intensity. The second application provides a description of spatial variation in the risk of campylobacter infections relative to other enteric infections in part of north Lancashire and south Cumbria. For this application, we treat the data as binomial counts at unit postcode locations, conditionally on an unobserved relative risk surface which we estimate. The theoretical framework for our extension of geostatistical methods is that, conditionally on the unobserved process S(x), observations at sample locations xi form a generalized linear model with the corresponding values of S(xi) appearing as an offset term in the linear predictor. We use a Bayesian inferential framework, implemented via the Markov chain Monte Carlo method, to solve the prediction problem for non-linear functionals of S(x), making a proper allowance for the uncertainty in the estimation of any model parameters.
The Effectiveness of Risk Scores: The Logit Rank Plot
Authors: Copas John
Published: March 2003

A risk score s for event E is a function of covariates with the property that P(E|s) is an increasing function of s. Motivated by applications in medicine and in criminology, we suggest the logit rank plot as a good way of summarizing the effectiveness of such a score. Explicitly, plot logit{P(E|s)} against logit(r), where r is the proportional rank of s in a sample or population. The slope of this plot gives an overall measure of effectiveness, and the logit rank transformation provides a common basis on which different risk scores can be compared. Some practical and theoretical aspects are discussed.
The Effect of Drop-Out on the Efficiency of Longitudinal Experiments
Authors: Verbeke Geert , Lesaffre Emmanuel
Published: March 2003

It is shown that drop-out often reduces the efficiency of longitudinal experiments considerably. In the framework of linear mixed models, a general, computationally simple method is provided, for designing longitudinal studies when drop-out is to be expected, such that there is little risk of large losses of efficiency due to the missing data. All the results are extensively illustrated using data from a randomized experiment with rats.
A Model for Extreme Wind Gusts
Authors: Walshaw David , Anderson Clive W.
Published: March 2003

Estimates of the largest wind gust that will occur at a given location over a specified period are required by civil engineers. Estimation is usually based on models which are derived from the limiting distributions of maxima of stationary time series and which are fitted to data on extreme gusts. In this paper we develop a model for maximum gusts which also incorporates data on hourly mean speeds through a distributional relationship between maxima and means. This joint model is closely linked to the physical processes which generate the most extreme values and thus provides a mechanism by which data on means can augment those on gusts. It is argued that this increases the credibility of extrapolation in estimates of long period return gusts. The model is shown to provide a good fit to data obtained at a location in northern England and is compared with a more traditional modelling approach, which also performs well for this site.
A Comparison of Models for Clustered Binary Outcomes: Analysis of a Designed Immunology Experiment
Authors: Betensky Rebecca A. , Williams Paige L. , Lederman Howard M.
Published: March 2003

The lymphocyte proliferative assay (LPA) of immune competence was conducted on 52 subjects, with up to 36 processing conditions per subject, to evaluate whether samples could be shipped or stored overnight, rather than being processed on fresh blood as currently required. The LPA study resulted in clustered binary data, with both cluster level and cluster-varying covariates. Two modelling strategies for the analysis of such clustered binary data are through the cluster-specific and population-averaged approaches. Whereas most research in this area has focused on the analysis of matched pairs data, in many situations, such as the LPA study, cluster sizes are naturally larger. Through considerations of interpretation and efficiency of these models when applied to large clusters, the mixed effect cluster-specific model was selected as most appropriate for the analysis of the LPA data. The model confirmed that the LPA response is significantly impaired in individuals infected with the human immunodeficiency virus (HIV). The LPA response was found to be significantly lower for shipped and overnight samples than for fresh samples, and this effect was significantly stronger among HIV-infected individuals. Surprisingly, an anticoagulant effect was not detected.
The Average Area under Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach Based on Generalized Two-Sample Wilcoxon Statistics
Authors: Lee Mei-Ling Ting , Rosner Bernard A.
Published: March 2003

It is well known that, when sample observations are independent, the area under the receiver operating characteristic (ROC) curve corresponds to the Wilcoxon statistics if the area is calculated by the trapezoidal rule. Correlated ROC curves arise often in medical research and have been studied by various parametric methods. On the basis of the Mann–Whitney U-statistics for clustered data proposed by Rosner and Grove, we construct an average ROC curve and derive nonparametric methods to estimate the area under the average curve for correlated ROC curves obtained from multiple readers. For the more complicated case where, in addition to multiple readers examining results on the same set of individuals, two or more diagnostic tests are involved, we derive analytic methods to compare the areas under correlated average ROC curves for these diagnostic tests. We demonstrate our methods in an example and compare our results with those obtained by other methods. The nonparametric average ROC curve and the analytic methods that we propose are easy to explain and simple to implement.
Biplots of Compositional Data
Authors: Aitchison John , Greenacre Michael
Published: March 2003

The singular value decomposition and its interpretation as a linear biplot have proved to be a powerful tool for analysing many forms of multivariate data. Here we adapt biplot methodology to the specific case of compositional data consisting of positive vectors each of which is constrained to have unit sum. These relative variation biplots have properties relating to the special features of compositional data: the study of ratios, subcompositions and models of compositional relationships. The methodology is applied to a data set consisting of six-part colour compositions in 22 abstract paintings, showing how the singular value decomposition can achieve an accurate biplot of the colour ratios and how possible models interrelating the colours can be diagnosed.
Childhood Malaria in the Gambia: A Case-Study in Model-Based Geostatistics
Authors: Diggle Peter , Moyeed Rana , Rowlingson Barry , Thomson Madeleine
Published: March 2003

The paper develops a spatial generalized linear mixed model to describe the variation in the prevalence of malaria among a sample of village resident children in the Gambia. The response from each child is a binary indicator of the presence of malarial parasites in a blood sample. The model includes terms for the effects of child level covariates (age and bed net use), village level covariates (inclusion or exclusion from the primary health care system and greenness of surrounding vegetation as derived from satellite information) and separate components for residual spatial and non-spatial extrabinomial variation. The results confirm and quantify the progressive increase in prevalence with age, and the protective effects of bed nets. They also show that the extrabinomial variation is spatially structured, suggesting an environmental effect rather than variation in familial susceptibility. Neither inclusion in the primary health care system nor the greenness of the surrounding vegetation appeared to affect the prevalence of malaria. The method of inference was Bayesian using vague priors and a Markov chain Monte Carlo implementation.
Solving for an Optimal Airline Yield Management Policy via Statistical Learning
Authors: Chen Victoria C. P. , Günther Dirk , Johnson Ellis L.
Published: March 2003

The yield management (YM) problem considers the task of maximizing a company's revenue. For the competitive airline industry, profit margins depend on a good YM policy. Research on airline YM is abundant but still limited to heuristics and small cases. We address the YM problem for a major domestic airline carrier's hub-and-spoke network, involving 20 cities and 31 flight legs. This is a problem of realistic size since airline networks are usually separated by hub cities. Our method is a variant of the orthogonal array experimental designs and multivariate adaptive regression splines stochastic dynamic programming method. Our method is demonstrated to outperform state of the art YM methods.
A Generalized Estimating Equation Approach to Modelling Foetal Response in Developmental Toxicity Studies When the Number of Implants is Dose Dependent
Authors: Kuk Anthony Y. C.
Published: March 2003

In developmental toxicity studies with exposure before implantation, the toxin that is used may interfere with the early reproductive process and prevent the implantation of some foetuses that would otherwise have been formed. A manifestation of this effect is a decreasing trend in the number of implants per dam as the dose level increases. Because unformed foetuses are not observable, Dunson proposed a multiple-imputation approach to estimate the number of missing foetuses. We propose instead to build a model for observable quantities only, namely the observed litter sizes and the observed numbers of deaths or malformations within litters. Using the probabilistic concept of thinning, we express the probability that a foetus fails to implant in terms of the parameters of the litter size distribution. Estimation is by means of generalized estimating equations and takes into account underdispersion of the observed litter sizes and overdispersion of the numbers of foetal deaths or malformations. A combined risk measure that takes into account not just post-implantation death or malformation but also the possibility of failure to implant is proposed and used to determine the virtually safe dose VSD. It is demonstrated numerically and graphically that ignoring the possibility of unformed foetuses leads to estimates of VSD that are too liberal. The proposed generalized estimating equation approach to estimating VSD and finding lower confidence limits is found to work well in a simulation study. We apply the proposed method to two data sets and compare the results that are obtained with those of existing studies.
An Experimental Design Criterion for Minimizing Meta-Model Prediction Errors Applied to Die Casting Process Design
Authors: Allen Theodore T. , Yu Liyang , Schmitz John
Published: March 2003

We propose the expected integrated mean-squared error (EIMSE) experimental design criterion and show how we used it to design experiments to meet the needs of researchers in die casting engineering. This criterion expresses in a direct way the researchers’ goal to minimize the expected meta-model prediction errors, taking into account the effects of both random experimental errors and errors deriving from our uncertainty about the true model form. Because we needed to make assumptions about the prior distribution of model coefficients to estimate the EIMSE, we performed a sensitivity analysis to verify that the relative prediction performance of the design generated was largely insensitive to our assumptions. Also, we discuss briefly the general advantages of EIMSE optimal designs, including lower expected bias errors compared with popular response surface designs and substantially lower variance errors than certain Box–Draper all-bias designs.
A Hierarchical Modelling Approach to Analysing Longitudinal Data with Drop-Out and Non-Compliance, with Application to an Equivalence Trial in Paediatric Acquired Immune Deficiency Syndrome
Authors: Hogan Joseph W , Daniels Michael J
Published: March 2003

Longitudinal clinical trials with long follow-up periods almost invariably suffer from a loss to follow-up and non-compliance with the assigned therapy. An example is protocol 128 of the AIDS Clinical Trials Group, a 5-year equivalency trial comparing reduced dose zidovudine with the standard dose for treatment of paediatric acquired immune deficiency syndrome patients. This study compared responses to treatment by using both clinical and cognitive outcomes. The cognitive outcomes are of particular interest because the effects of human immunodeficiency virus infection of the central nervous system can be more acute in children than in adults. We formulate and apply a Bayesian hierarchical model to estimate both the intent-to-treat effect and the average causal effect of reducing the prescribed dose of zidovudine by 50%. The intent-to-treat effect quantifies the causal effect of assigning the lower dose, whereas the average causal effect represents the causal effect of actually taking the lower dose. We adopt a potential outcomes framework where, for each individual, we assume the existence of a different potential outcomes process at each level of time spent on treatment. The joint distribution of the potential outcomes and the time spent on assigned treatment is formulated using a hierarchical model: the potential outcomes distribution is given at the first level, and dependence between the outcomes and time on treatment is specified at the second level by linking the time on treatment to subject-specific effects that characterize the potential outcomes processes. Several distributional and structural assumptions are used to identify the model from observed data, and these are described in detail. A detailed analysis of AIDS Clinical Trials Group protocol 128 is given; inference about both the intent-to-treat effect and average causal effect indicate a high probability of dose equivalence with respect to cognitive functioning.
Sample reuse Simulation in Optimal Design forTmax in Pharmacokinetic Experiments
Authors: Wang Jixian
Published: March 2003

T max and Cmax are important pharmacokinetic parameters in drug development processes. Often a nonparametric procedure is needed to estimate them when model independence is required. This paper proposes a simulation-based optimal design procedure for finding optimal sampling times for nonparametric estimates of Tmax and Cmax for each subject, assuming that the drug concentration follows a non-linear mixed model. The main difficulty of using standard optimal design procedures is that the property of the nonparametric estimate is very complicated. This procedure uses a sample reuse simulation to calculate the design criterion, which is an integral of multiple dimension, so that effective optimization procedures such as Newton-type procedures can be used directly to find optimal designs. This procedure is used to construct optimal designs for an open one-compartment model. An approximation based on the Taylor expansion is also derived and showed results that were consistent with those based on the sample reuse simulation.
TESTING FOR CYCLICAL NON‐STATIONARITY IN AUTOREGRESSIVE PROCESSES
Authors: Kunst Robert M.
Published: March 2003

This paper deals with the distributions evolving from the likelihood‐ratio test for the factor 1 −Bn in the lag polynomial Φ(B) under the basic assumption that the data series is generated by the autoregressive model Φ(B)Xt = εt where {εt} denotes Gaussian white noise. A characterization of the statistic and its asymptotic properties is given. Asymptotic and finite‐sample significance points are tabulated. The test procedure is illustrated by an economics example.
PROJECTION MODULUS: A NEW DIRECTION FOR SELECTING SUBSET AUTOREGRESSIVE MODELS
Authors: Zhang Xichuan , Terrell R. Deane
Published: March 2003

Selecting a parsimonious subset autoregressive time series model is a valuable objective particularly where there is or may be evidence that a time series may have some form of periodic or quasi‐periodic behaviour. An efficient model selection procedure is essential because of the large number of possible alternative models involved. The explanation of an increase in residual variance due to excluding a lag is examined in Hilbert space. As a result, a new statistic, the projection modulus, and its derivatives are developed to assess the significance of any lag in a model. The impact of deleting a lag, as measured by these statistics, helps to produce a selection procedure where true lags have less chance of being removed. We then assess an efficient subset autoregressive model selection procedure employing these statistics. The success of the proposed procedure is illustrated by its efficiency in identifying the true model for simulated and real data.
Nonparametric Autoregression with Multiplicative Volatility and Additive mean
Authors: Yang Lijian , Hardle Wolfgang , Nielsen Jens
Published: March 2003

For over a decade, nonparametric modelling has been successfully applied to studying nonlinear structures in financial time series. It is well known that the usual nonparametric models often have less than satisfactory performance when dealing with more than one lag. When the mean has an additive structure, however, better estimation methods are available which fully exploit such a structure. Although in the past such nonparametric applications had been focused more on the estimation of the conditional mean, it is equally if not more important to measure the future risk of the series along with the mean. For the volatility function, i.e. the conditional variance given the past, a multiplicative structure is more appropriate than an additive structure, as the volatility is a positive scale function and a multiplicative model provides a better interpretation of each lagged value's influence on such a function. In this paper we consider the joint estimation of both the additive mean and the multiplicative volatility. The technique used is marginally integrated local polynomial estimation. The procedure is applied to the deutschmark/US dollar daily exchange returns.
Moving Averages of Random Vectors with Regularly Varying Tails
Authors: Meerschaert Mark M. , Scheffler Hans‐Peter
Published: March 2003

Regular variation is an analytic condition on the tails of a probability distribution which is necessary for an extended central limit theorem to hold, when the tails are too heavy to allow attraction to a normal limit. The limiting distributions which can occur are called operator stable. In this paper we show that moving averages of random vectors with regularly varying tails are in the generalized domain of attraction of an operator stable law. We also prove that the sample autocovariance matrix of these moving averages is in the generalized domain of attraction of an operator stable law on the vector space of symmetric matrices.

AMS 1990 subject classification. Primary 62M10, secondary 62E20, 62F12, 60F05.
Local Cross‐validation for Spectrum Bandwidth Choice
Authors: Velasco Carlos
Published: March 2003

We investigate an automatic method of determining a local bandwidth for non‐parametric kernel spectral density estimates at a single frequency. This procedure is a modification of a cross‐validation technique for global bandwidth choices, avoiding the computation of any pilot estimate based on initial bandwidths or on approximate parametric models. Only local conditions on the spectral density around the frequency of interest are assumed. We illustrate with a Monte Carlo study the performance in finite samples of the bandwidth estimates proposed.
Can One Use the Durbin–Levinson Algorithm to Generate Infinite Variance Fractional ARIMA Time Series?
Authors: Kokoszka Piotr S. , Taqqu Murad S.
Published: March 2003

The Durbin–Levinson algorithm is used to generate Gaussian time series with a given covariance structure. This is the most efficient way, for example, to simulate a Gaussian fractional ARIMA (FARIMA) time series, a linear sequence with i.i.d. Gaussian innovations which exhibits long‐range dependence. The paper studies the applicability of the Durbin–Levinson algorithm to the simulation of infinite variance FARIMA sequences including an α‐stable FARIMA.
Predictions in time Series Using Multivariate Regression Models
Authors: Stulajter Frantisek
Published: March 2003

Predictions in time series using multivariate regression models are studied with respect to their mean squared errors. Two new methods of prediction are proposed: the simple one and the method based on the kriging theory. The mean squared errors of these predictions are computed and it is shown that the first one can be regarded as a special case of the kriging approach.
A Direct Test for Cointegration Between a Pair of Time Series
Authors: LEYBOURNE STEPHEN J. , NEWBOLD PAUL , VOUGAS DIMITRIOS , KIM TAE‐HWAN
Published: March 2003

In this paper we introduce a new test of the null hypothesis of no cointegration between a pair of time series. For a very simple generating model, our test compares favourably with the Engle–Granger/Dickey–Fuller test and the Johansen trace test. Indeed, shortcomings of the former motivated the development of our test. The applicability of our test is extended to series generated by low‐order vector autoregressions. Again, we find evidence that this general version of our test is more powerful than the Johansen test. The paper concludes with an empirical example in which the new test finds strong evidence of cointegration, but the Johansen test does not.
Semiparametric robust tests on seasonal or cyclical long memory time series
Authors: ARTECHE JOSU
Published: March 2003

The concept of SCLM (seasonal or cyclical long memory) implies the existence of one or more spectral poles or zeros. The processes traditionally used to model such a behaviour assume the same persistence across different frequencies. In this paper, we propose semiparametric Wald and Lagrange multiplier (LM) tests of the equality of memory parameters at different frequencies (extendable to other linear restrictions) which are standard in the sense that they have well known χ2 distributions under the null hypothesis – although Gaussianity is nowhere assumed – and are consistent against constant and local alternatives. They have also the advantage of being robust against misspecification at frequencies distant from those of interest. Their finite sample performance is compared with the asymptotically locally efficient Robinson's tests (1994). An empirical application to a UK inflation series is also included.
Human Capital, Wages and Family Interactions
Authors: Rossetti Stefania , Tanda Paola
Published: March 2003

The majority of studies of the returns to human capital investment have generally considered the relationship among wages and individuals’ investment in education. However, among factors which increase individuals’ stocks of human capital and affect their labour market performances there are variables linked to the relevant social environment, such as the family. This paper takes into consideration intra‐generational relations which form between spouses through marriage, as well as inter‐generational relationships between parents and their sons/daughters. The empirical work investigates the effects of some family background variables on individuals’ economic performances, using data drawn from the 1995 survey of household income and wealth conducted by the Bank of Italy. The results of the analysis show that individuals’ wages do not depend only on their own human capital endowment, but also on that of other family members (parents and partners).
Employer Wage Differentials in Germany: A Comparative Note
Authors: Stephan Gesine
Published: March 2003

The paper contributes to the growing empirical literature on employer wage differentials, presenting first estimates for West Germany and comparing them with recent findings from other studies for the USA, France and Denmark. The empirical results show that the variation of global employer wage differentials is comparatively low in West Germany and has remained stable during the first half of the 1990s. This low dispersion results from wage setting for blue‐collar workers, while cross‐country differences are negligible for white‐collar workers. Employer wage differentials have, however, become more important for the remuneration of West German blue‐collar workers during the period investigated.
Determination of Average Working Time in Finland
Authors: Böckerman Petri , Kiander Jaakko
Published: March 2003

This study explores the determination of average working time in the context of a Nordic welfare state. The study is focused on the Finnish case. The issue is explored by using data from six industries from 1960 to 1996. The main empirical result is that both an increase in labour productivity and a widening of the tax wedge have contributed to a decline in average working time in Finland. These observations are consistent with the predictions of a theoretical model that is based on the notion of equilibrium working hours.
Advantage through Training in Poland? A Microeconometric Evaluation of the Employment Effects of Training and Job Subsidy Programmes
Authors: Puhani Patrick A.
Published: March 2003

I estimate the employment effects of publicly financed training and intervention works (subsidized employment) programmes in Poland. The analysis is based on the Polish Labour Force Survey (PLFS). Two widely applied approaches to identify causal effects are used. The results are qualitatively invariant with respect to the applied methodology. I find some evidence that training improves the employment opportunities of both men and women, whereas intervention works do not. Previous findings using PLFS data thus seem to be robust. The contrary view expressed by other authors on intervention works (subsidized employment) is likely to stem from the different data source (on only some Polish regions and with fewer control variables) used by them and does not seem to be related to the estimation strategy.
Coping with On‐the‐Job Writing in ESL: A Constructivist‐Semiotic Perspective
Authors: Parks Susan , Maguire Mary H.
Published: March 2003

Despite a long‐standing interest within applied linguistics in the analysis of written genres, few studies have attempted to show how such genres are appropriated by new members in academic or workplace settings. Based on a 22‐month qualitative study, this article reports on how francophone nurses, who were newly hired in an English‐medium hospital in Montreal, Canada, developed skill in writing nursing notes (which differed from the way they were done in French) in English. Central to the analysis is the construct of mediation, explored in terms of how collaborative processes, both overt and covert, shape text production as well as other less visible, taken‐for‐granted aspects of the social context.
The LS Graph: A Methodology for Visualizing Writing Revision
Authors: Lindgren Eva , Sullivan Kirk P. H.
Published: March 2003

The writing process has long been a subject for investigation. Until recently researchers have been restricted to written protocols for the analysis of writing sessions. These provide vast amounts of information from which it is impossible to create detailed mental representations of the writer’s movements around the text, revision activity, or pause behavior. Computer keystroke –logging programs, which record all keystrokes and mouse actions, facilitate the collection of quantitative data about text creation. This article presents the LS graph, a novel way of graphically representing and summarizing the quantitative data collected when keystroke logging. Further, the graph can be combined with a detailed manual analysis of the individual revisions that can be undertaken by playing back the logged writing session.