bluuebunny/crossref_metadata_2025_split huggingface dataset:
Weak Pronouns as LF Clitics: Clustering and Adjacency Effects in the Pronominal Systems of German and Hebrew
Authors: Laenzlinger Christopher , Shlonsky Ur 
Published: March 2003

This paper is a comparative study of weak pronouns in German and Hebrew. Weak pronouns share properties of both Romance‐like clitics (adjacency, clustering effects) and full pronouns (non‐reduced, free‐standing forms). Weak pronouns are analyzed as maximal projections prior to Spellout and as clitics, X0 elements, at LF. In the overt syntax, they move as XPs to their Case‐checking position. They move beyond the Case position if that position is not close enough to their LF‐host. At LF they move as X0s, i.e. they cliticize and incorporate to a V‐related host.In Hebrew, the clitic host is the participle (Agr participle0) in complex tenses and the verb in AgrS0 or in C0 (inversion) in simplex tenses. In German, the pronouns’ LF‐host is unique: AgrS0 raised to C0. Weak pronouns can form clusters, which are derived by multiple adjunction of the pronouns. Weak pronouns are also subject to an adjacency constraint, as a consequence of the Head Movement Constraint, operative on LF Incorporation.
Ebonics and the Politics of English
Authors: Baron Dennis 
Published: March 2003

In 1996 the Oakland Unified School District passed a resolution declaring Ebonics to be the primary language of the African‐American students in its schools. The resolution further declared Ebonics to be a language in its own right, not a dialect of English. In the face of massive national opposition to the Oakland Ebonics resolution, this radical, separatist move shifted to a conservative, assimilationist one: Oakland retracted its declaration of linguistic independence and reaffirmed the traditional pedagogical goal of teaching students standard English. But the Oakland Ebonics controversy reminds us that, although the English of former British colonies has come into its own in the literary, cultural, and political scene, to the point where we speak of world Englishes, the English varieties of what may be regarded as internal colonies – inner cities and the socially disenfranchised – continues to be stigmatized by speakers of more esteemed varieties.Anyway what was the use of my having come from Oakland. It was not natural to have come from there … there is no there there.Gertrude Stein, Everybody's Autobiography, 1937
Outliers in Multilevel Data
Authors: Langford I. H. , Lewis T. 
Published: March 2003

Summary
This paper offers the data analyst a range of practical procedures for dealing with outliers in multilevel data. It first develops several techniques for data exploration for outliers and outlier analysis and then applies these to the detailed analysis of outliers in two large scale multilevel data sets from educational contexts. The techniques include the use of deviance reduction, measures based on residuals, leverage values, hierarchical cluster analysis and a measure called DFITS. Outlier analysis is more complex in a multilevel data set than in, say, a univariate sample or a set of regression data, where the concept of an outlying value is straightforward. In the multilevel situation one has to consider, for example, at what level or levels a particular response is outlying, and in respect of which explanatory variables; furthermore, the treatment of a particular response at one level may affect its status or the status of other units at other levels in the model.
The Determinants of Infant Mortality in Malaysia: A Graphical Chain Modelling Approach
Authors: Mohamed W. N. , Diamond I. , Smith P. W. F. 
Published: March 2003

Summary
A graphical chain modelling approach is used to study the determinants of neonatal and post-neonatal mortality in Malaysia. This approach provides an easily interpretable empirical description and illustrates explicitly the conditional independence structure between each pair of variables. The interpretation can be read directly from a mathematical graph. Besides examining the direct association of each determinant on mortality, we also examine the effects of socioeconomic determinants on intermediate determinants to understand the pathways through which the socioeconomic determinants affect the chance of mortality. The data analysed come from the second Malaysian Family Life Survey, fielded between August 1988 and January 1989.
Influence of Light Level on the Incidence of Road Casualties and the Predicted Effect of Changing ‘Summertime’
Authors: Broughton J. , Hazelton M. , Stone M. 
Published: March 2003

Summary
Previous studies of the apparent influence of daylight level and hour changes on the incidence of road casualties are reviewed and refined, by analysis of official databases for Great Britain (1969–1973 and 1985–1994) and the USA (1991–1995). New statistical methods, based on precisely computed altitudes of the sun for each accident location, are used to model casualty frequencies aggregated by week and hour of day, and locally evaluated associations between individual casualty incidence and solar altitude. Estimates of the altitude factor are interpreted causally to give counterfactual estimates of the effect of different clock time schedules on countrywide casualty numbers.
Women’s Work in UK Official Statistics and the 1980 Reclassification of Occupations
Authors: Blackwell Louisa 
Published: March 2003

Summary
Analyses using either time series or longitudinal data can be complicated by having to disentangle real changes from the artefactual effects imposed by the reclassification of key variables. In this paper the Warwick occupational conversion program is used to create adjustment tables which correct for the artefactual effect of the 1980 reclassification, along with coder errors, on 1971–1981 patterns of occupational sex segregation in longitudinal data from the Office for National Statistics’s Longitudinal Study. The crowding of large numbers of women into a few occupational groups, which is consistent through the reclassification, arises in part because classification schemes fail to recognize the diversity in their work. This reflects past practice and the undervaluation of women’s skills.
Statistical Methods for Clinical Studies in the Presence of Surrogate End Points
Authors: Leung Denis Heng-Yan 
Published: March 2003

Summary
The use of surrogate end points has become increasingly common in medical and biological research. This is primarily because, in many studies, the primary end point of interest is too expensive or too difficult to obtain. There is now a large volume of statistical methods for analysing studies with surrogate end point data. However, to our knowledge, there has not been a comprehensive review of these methods to date. This paper reviews some existing methods and summarizes the strengths and weaknesses of each method. It also discusses the assumptions that are made by each method and critiques how likely these assumptions are met in practice.
On an Adaptive Transformation–Retransformation Estimate of Multivariate Location
Authors: Chakraborty Biman , Chaudhuri Probul 
Published: March 2003

Summary
An affine equivariant estimate of multivariate location based on an adaptive transformation and retransformation approach is studied. The work is primarily motivated by earlier work on different versions of the multivariate median and their properties. We explore an issue related to efficiency and equivariance that was originally raised by Bickel and subsequently investigated by Brown and Hettmansperger. Our estimate has better asymptotic performance than the vector of co-ordinatewise medians when the variables are substantially correlated. The finite sample performance of the estimate is investigated by using Monte Carlo simulations. Some examples are presented to demonstrate the effect of the adaptive transformation–retransformation strategy in the construction of multivariate location estimates for real data.
Nonparametric Validation of Similar Distributions and Assessment of Goodness of Fit
Authors: Munk Axel , Czado Claudia 
Published: March 2003

Summary
In this paper the problem of assessing the similarity of two cumulative distribution functions F and G is considered. An asymptotic test based on an α-trimmed version of Mallows distance Γα(F, G) between F and G is suggested, thus demonstrating the similarity of F and G within a preassigned Γα(F, G) neighbourhood at a controlled type I error rate. The test proposed is applied to the validation of goodness of fit and for the nonparametric assessment of bioequivalence. It is shown that Γα(F, G) can be interpreted as average and population equivalence. Our approach is illustrated by various examples.
Estimating Smooth Monotone Functions
Authors: Ramsay J. O. 
Published: March 2003

Summary
Many situations call for a smooth strictly monotone function f of arbitrary flexibility. The family of functions defined by the differential equation D  2 f =w Df, where w is an unconstrained coefficient function comprises the strictly monotone twice differentiable functions. The solution to this equation is f = C 0 + C 1  D −1{exp(D −1 w)}, where C 0 and C 1 are arbitrary constants and D −1 is the partial integration operator. A basis for expanding w is suggested that permits explicit integration in the expression of f. In fitting data, it is also useful to regularize f by penalizing the integral of w 2 since this is a measure of the relative curvature in f. Applications are discussed to monotone nonparametric regression, to the transformation of the dependent variable in non-linear regression and to density estimation.
Some Algebra and Geometry for Hierarchical Models, Applied to Diagnostics
Authors: Hodges J. S. 
Published: March 2003

Summary
Recent advances in computing make it practical to use complex hierarchical models. However, the complexity makes it difficult to see how features of the data determine the fitted model. This paper describes an approach to diagnostics for hierarchical models, specifically linear hierarchical models with additive normal or t-errors. The key is to express hierarchical models in the form of ordinary linear models by adding artificial ‘cases' to the data set corresponding to the higher levels of the hierarchy. The error term of this linear model is not homoscedastic, but its covariance structure is much simpler than that usually used in variance component or random effects models. The re-expression has several advantages. First, it is extremely general, covering dynamic linear models, random effect and mixed effect models, and pairwise difference models, among others. Second, it makes more explicit the geometry of hierarchical models, by analogy with the geometry of linear models. Third, the analogy with linear models provides a rich source of ideas for diagnostics for all the parts of hierarchical models. This paper gives diagnostics to examine candidate added variables, transformations, collinearity, case influence and residuals.
Effect of Frailty on Marginal Regression Estimates in Survival Analysis
Authors: Henderson R. , Oman P. 
Published: March 2003

Summary
Unexplained heterogeneity in univariate survival data and association in multivariate survival can both be modelled by the inclusion of frailty effects. This paper investigates the consequences of ignoring frailty in analysis, fitting misspecified Cox proportional hazards models to the marginal distributions. Regression coefficients are biased towards 0 by an amount which depends in magnitude on the variability of the frailty terms and the form of frailty distribution. The bias is reduced when censoring is present. Fitted marginal survival curves can also differ substantially from the true marginals.
Biased Bootstrap Methods for Reducing the Effects of Contamination
Authors: Hall Peter , Presnell Brett 
Published: March 2003

Summary
Contamination of a sampled distribution, for example by a heavy-tailed distribution, can degrade the performance of a statistical estimator. We suggest a general approach to alleviating this problem, using a version of the weighted bootstrap. The idea is to ‘tilt’ away from the contaminated distribution by a given (but arbitrary) amount, in a direction that minimizes a measure of the new distribution's dispersion. This theoretical proposal has a simple empirical version, which results in each data value being assigned a weight according to an assessment of its influence on dispersion. Importantly, distance can be measured directly in terms of the likely level of contamination, without reference to an empirical measure of scale. This makes the procedure particularly attractive for use in multivariate problems. It has several forms, depending on the definitions taken for dispersion and for distance between distributions. Examples of dispersion measures include variance and generalizations based on high order moments. Practicable measures of the distance between distributions may be based on power divergence, which includes Hellinger and Kullback–Leibler distances. The resulting location estimator has a smooth, redescending influence curve and appears to avoid computational difficulties that are typically associated with redescending estimators. Its breakdown point can be located at any desired value ε∈ (0, ½) simply by ‘trimming’ to a known distance (depending only on ε and the choice of distance measure) from the empirical distribution. The estimator has an affine equivariant multivariate form. Further, the general method is applicable to a range of statistical problems, including regression.
The Achievable Region Approach to the Optimal Control of Stochastic Systems
Authors: Dacre Marcus , Glazebrook Kevin , Niño-Mora José 
Published: March 2003

Summary
The achievable region approach seeks solutions to stochastic optimization problems by characterizing the space of all possible performances (the achievable region) of the system of interest and optimizing the overall system-wide performance objective over this space. This is radically different from conventional formulations based on dynamic programming. The approach is explained with reference to a simple two-class queueing system. Powerful new methodologies due to the authors and co-workers are deployed to analyse a general multi-class queuing system with parallel servers and then to develop an approach to optimal load distribution across a network of interconnected stations. Finally, the approach is used for the first time to analyse a class of intensity control problems.
Priors and Component Structures in Autoregressive Time Series Models
Authors: Huerta Gabriel , West Mike 
Published: March 2003

Summary
New approaches to prior specification and structuring in autoregressive time series models are introduced and developed. We focus on defining classes of prior distributions for parameters and latent variables related to latent components of an autoregressive model for an observed time series. These new priors naturally permit the incorporation of both qualitative and quantitative prior information about the number and relative importance of physically meaningful components that represent low frequency trends, quasi-periodic subprocesses and high frequency residual noise components of observed series. The class of priors also naturally incorporates uncertainty about model order and hence leads in posterior analysis to model order assessment and resulting posterior and predictive inferences that incorporate full uncertainties about model order as well as model parameters. Analysis also formally incorporates uncertainty and leads to inferences about unknown initial values of the time series, as it does for predictions of future values. Posterior analysis involves easily implemented iterative simulation methods, developed and described here. One motivating field of application is climatology, where the evaluation of latent structure, especially quasi-periodic structure, is of critical importance in connection with issues of global climatic variability. We explore the analysis of data from the southern oscillation index, one of several series that has been central in recent high profile debates in the atmospheric sciences about recent apparent trends in climatic indicators.
Bootstrap Confidence Regions Computed from Autoregressions of Arbitrary Order
Authors: Choi Edwin , Hall Peter 
Published: March 2003

Summary
Given a linear time series, e.g. an autoregression of infinite order, we may construct a finite order approximation and use that as the basis for confidence regions. The sieve or autoregressive bootstrap, as this method is often called, is generally seen as a competitor with the better-understood block bootstrap approach. However, in the present paper we argue that, for linear time series, the sieve bootstrap has significantly better performance than blocking methods and offers a wider range of opportunities. In particular, since it does not corrupt second-order properties then it may be used in a double-bootstrap form, with the second bootstrap application being employed to calibrate a basic percentile method confidence interval. This approach confers second-order accuracy without the need to estimate variance. That offers substantial benefits, since variances of statistics based on time series can be difficult to estimate reliably, and—partly because of the relatively small amount of information contained in a dependent process—are notorious for causing problems when used to Studentize. Other advantages of the sieve bootstrap include considerably greater robustness against variations in the choice of the tuning parameter, here equal to the autoregressive order, and the fact that, in contradistinction to the case of the block bootstrap, the percentile t version of the sieve bootstrap may be based on the ‘raw'’ estimator of standard error. In the process of establishing these properties we show that the sieve bootstrap is second order correct.
A Small Sample Estimator for a Polynomial Regression with Errors in the Variables
Authors: Cheng Chi-Lun , Schneeweiss Hans , Thamerus Markus 
Published: March 2003

Summary
An adjusted least squares estimator, introduced by Cheng and Schneeweiss for consistently estimating a polynomial regression of any degree with errors in the variables, is modified such that it shows good results in small samples without losing its asymptotic properties for large samples. Simulation studies corroborate the theoretical findings.
A Model for Markers and Latent Health Status
Authors: Ting Lee Mel-Ling , DeGruttola Victor , Schoenfeld David 
Published: March 2003

Summary
We extend the bivariate Wiener process considered by Whitmore and co-workers and model the joint process of a marker and health status. The health status process is assumed to be latent or unobservable. The time to reach the primary end point or failure (death, onset of disease, etc.) is the time when the latent health status process first crosses a failure threshold level. Inferences for the model are based on two kinds of data: censored survival data and marker measurements. Covariates, such as treatment variables, risk factors and base-line conditions, are related to the model parameters through generalized linear regression functions. The model offers a much richer potential for the study of treatment efficacy than do conventional models. Treatment effects can be assessed in terms of their influence on both the failure threshold and the health status process parameters. We derive an explicit formula for the prediction of residual failure times given the current marker level. Also we discuss model validation. This model does not require the proportional hazards assumption and hence can be widely used. To demonstrate the usefulness of the model, we apply the methods in analysing data from the protocol 116a of the AIDS Clinical Trials Group.
On Measuring Sensitivity to Parametric Model Misspecification
Authors: Gustafson Paul 
Published: March 2003

Summary
In settings where parametric inference is inconsistent under model misspecification, the discrepancy between correct and misspecified inferences is compared with the discrepancy between correct and misspecified models. To make the comparison tractable, large sample and small misspecification approximations are employed. The ratio of the approximate discrepancy between inferences to the approximate discrepancy between models is regarded as a relative measure of sensitivity to model misspecification. The maximum ratio over a family of correct distributions is determined as a measure of worst case sensitivity. As well, the distribution producing this maximum can be examined, to see how a particular combination of a parametric family and estimand is susceptible to model misspecifications.
Statistical Aspects of Chaotic Maps With Negative Dependence in a Communications Setting
Authors: Lawrance A. J. , Balakrishna N. 
Published: March 2003

Summary
It is shown that a class of tailed shift chaotic maps can be designed with substantial negative dependence, both linear and non-linear, and that extended Perron–Frobenius theory gives their dependence structure. Using a simplified chaos-based communication system, it is shown that chaotic spreading sequences with low kurtosis and negative non-linear mean-centred quadratic autocorrelations can improve bit-received accuracy. This quadratic form of non-linear dependence is investigated and shown to be statistically sensible.
Comparing Accuracies of Two Screening Tests in a Two-Phase Study for Dementia
Authors: Zhou Xiao-Hua 
Published: March 2003

SUMMARY
A two-phase design has been widely used in epidemiological studies of dementia. The first phase assesses a large sample with screening tests. The second, based on the screening test results and possibly on other observed patient’s factors, selects a subset of the study sample for a more definitive disease verification assessment. In comparing the accuracies of two screening tests in a two-phase study of dementia, inferences are commonly made from a sample of verified cases. The omission of non-verified cases can seriously bias comparison results. To correct for this bias, we derive the maximum likelihood (ML) estimators for the accuracies of two screening tests and their corresponding correlation. The p-values and confidence intervals are computed using the asymptotic normality of the ML estimators. Our method is used to compare the accuracies of two screening tests in a two-phase epidemiological study of dementia. We found that, although the sensitivities of the new and standard screening tests in detecting a diseased subject are not different, the new screening test performs better in detecting a non-diseased subject.
Parameter Estimation in Large Dynamic Paired Comparison Experiments
Authors: Glickman Mark E. 
Published: March 2003

SUMMARY
Paired comparison data in which the abilities or merits of the objects being compared may be changing over time can be modelled as a non-linear state space model. When the population of objects being compared is large, likelihood-based analyses can be too computationally cumbersome to carry out regularly. This presents a problem for rating populations of chess players and other large groups which often consist of tens of thousands of competitors. This problem is overcome through a computationally simple non-iterative algorithm for fitting a particular dynamic paired comparison model. The algorithm, which improves over the commonly used algorithm of Elo by incorporating the variability in parameter estimates, can be performed regularly even for large populations of competitors. The method is evaluated on simulated data and is applied to ranking the best chess players of all time, and to ranking the top current tennis-players.