bluuebunny/crossref_metadata_2025 huggingface dataset:
Why use Arbitrary Points Scores?: Ordered Categories in Models of Educational Progress
Authors: Fielding Antony
Published: March 2003

Much statistical modelling of random effects on ordered responses, particularly of grades in educational research, continues to use linear models and to treat the responses through arbitrary scores. Methodological and software developments now facilitate the proper treatment of such situations through more realistic generalized random-effects models. This paper reviews some methodological comparisons of these approaches. It highlights the flexibility offered by the macro facilities of the multilevel random-effects software MLwiN. It considers applications to an analysis of primary school educational progress from reception to England and Wales national curriculum key stage 1 mathematics. By contrasting the results from generalized modelling and scoring approaches it draws some conclusions about the theoretical, methodological and practical options that are available. It also considers that results of generalized random-model estimation may be more intelligible to users of analytical results.
Interpreting Forensic DNA Mixtures: Allowing for Uncertainty in Population Substructure and Dependence
Authors: Fung Wing K. , Hu Yue-Qing
Published: March 2003

We consider the assessment of deoxyribonucleic acid (DNA) profiles from biological samples containing a mixture of DNA from more than one person. The problem has been investigated in the context of likelihood ratios by Weir and co-workers under the assumption of independent alleles in DNA profiles. However, uncertainty about independence may arise from various factors such as population substructure and relatedness. This issue has received considerable attention in recent years. Ignoring this uncertainty may seriously overstate the strength of the evidence and therefore disadvantage innocent suspects. Taking this uncertainty into account, we develop a general formula for calculating the match probabilities of DNA profiles. Thus, we extend the result derived by Weir and co-workers to the dependence situation, which is often more to the benefit of the defendant in comparison with the simple product rule result based on an independence assumption. The effect of dependence of alleles on likelihood ratio estimates can be seen in the analysis of two real data sets.
Combining Evidence on Air Pollution and Daily Mortality from the 20 Largest US Cities: A Hierarchical Modelling Strategy
Authors: Dominici Francesca , Samet Jonathan M. , Zeger Scott L.
Published: March 2003

Reports over the last decade of association between levels of particles in outdoor air and daily mortality counts have raised concern that air pollution shortens life, even at concentrations within current regulatory limits. Criticisms of these reports have focused on the statistical techniques that are used to estimate the pollution–mortality relationship and the inconsistency in findings between cities. We have developed analytical methods that address these concerns and combine evidence from multiple locations to gain a unified analysis of the data. The paper presents log-linear regression analyses of daily time series data from the largest 20 US cities and introduces hierarchical regression models for combining estimates of the pollution–mortality relationship across cities. We illustrate this method by focusing on mortality effects of PM10 (particulate matter less than 10 μm in aerodynamic diameter) and by performing univariate and bivariate analyses with PM10 and ozone (O3) level. In the first stage of the hierarchical model, we estimate the relative mortality rate associated with PM10 for each of the 20 cities by using semiparametric log-linear models. The second stage of the model describes between-city variation in the true relative rates as a function of selected city-specific covariates. We also fit two variations of a spatial model with the goal of exploring the spatial correlation of the pollutant-specific coefficients among cities. Finally, to explore the results of considering the two pollutants jointly, we fit and compare univariate and bivariate models. All posterior distributions from the second stage are estimated by using Markov chain Monte Carlo techniques. In univariate analyses using concurrent day pollution values to predict mortality, we find that an increase of 10 μg m-3 in PM10 on average in the USA is associated with a 0.48% increase in mortality (95% interval: 0.05, 0.92). With adjustment for the O3 level the PM10-coefficient is slightly higher. The results are largely insensitive to the specific choice of vague but proper prior distribution. The models and estimation methods are general and can be used for any number of locations and pollutant measurements and have potential applications to other environmental agents.
Extending the Reach of Randomized Social Experiments: New Directions in Evaluations of American Welfare-to-Work and Employment Initiatives
Authors: Riccio James A. , Bloom Howard S.
Published: March 2003

Random assignment experiments are widely used in the USA to test the effectiveness of new social interventions. This paper discusses several major welfare-to-work experiments, highlighting their evolution from simple ‘black box’ tests of single interventions to multigroup designs used to compare alternative interventions or to isolate the effects of components of an intervention. The paper also discusses new efforts to combine experimental and non-experimental analyses to test underlying programme theories and to maximize the knowledge gained about the effectiveness of social programmes. Researchers and policy makers in other countries may find this variety of approaches useful to consider as they debate an expanded role for social experiments.
Designing for a Response Transformation Parameter
Authors: Atkinson Anthony C. , Cook R. Dennis
Published: March 2003

We investigate D-optimum designs for experiments in which a linear model holds after an unknown power transformation of the univariate response variable. This is a departure from standard D-optimal design in which an appropriate scale for the response is assumed known before data collection. The design problem that we formulate is intrinsically non-linear, requiring characterization of likely parameter values. Several applications are presented to illustrate the importance of recognizing the role of response transformations at the design stage.
Iterated Partial Sum Sequences of Regression Residuals and Tests for Changepoints with Continuity Constraints
Authors: Jandhyala V. K. , MacNeill I. B.
Published: March 2003

Iterated partial sum sequences of regression least squares residuals are defined and large sample properties of sequences of stochastic processes defined by these iterated partial sums are discussed. Also, finite sample properties of the iterated partial sum sequences are obtained. These include a property of least squares residuals of polynomial fits to equispaced data, namely the iterated partial sums sum to 0 provided that the order of iteration is not greater than the order of the polynomial, thus extending the well-known result that residuals sum to 0. Iterated partial sums are shown to play an important role in testing regression parameters for changes at unknown times under the constraint of continuity.
Random Effect Threshold Models for Dose–Response Relationships with Repeated Measurements
Authors: Brockhoff Per M. , Müller Hans-Georg
Published: March 2003

A random threshold model for dose--response designs with repeated measurements is introduced. Inference procedures for this model are discussed. The framework of generalized linear models is used to propose a quasi-likelihood inference procedure. The feasibility of this approach is illustrated with the analysis of food science data concerning a forced choice sensory experiment.
Kernel Smoothing to Improve Bootstrap Confidence Intervals
Authors: Polansky Alan M. , Schucany William. R.
Published: March 2003

Some studies of the bootstrap have assessed the effect of smoothing the estimated distribution that is resampled, a process usually known as the smoothed bootstrap. Generally, the smoothed distribution for resampling is a kernel estimate and is often rescaled to retain certain characteristics of the empirical distribution. Typically the effect of such smoothing has been measured in terms of the mean-squared error of bootstrap point estimates. The reports of these previous investigations have not been encouraging about the efficacy of smoothing. In this paper the effect of resampling a kernel-smoothed distribution is evaluated through expansions for the coverage of bootstrap percentile confidence intervals. It is shown that, under the smooth function model, proper bandwidth selection can accomplish a first-order correction for the one-sided percentile method. With the objective of reducing the coverage error the appropriate bandwidth for one-sided intervals converges at a rate of n  −1/4, rather than the familiar n  −1/5 for kernel density estimation. Applications of this same approach to bootstrap t and two-sided intervals yield optimal bandwidths of order n  −1/2. These bandwidths depend on moments of the smooth function model and not on derivatives of the underlying density of the data. The relationship of this smoothing method to both the accelerated bias correction and the bootstrap t methods provides some insight into the connections between three quite distinct approximate confidence intervals.
Mixed Effects Smoothing Spline Analysis of Variance
Authors: Wang Yuedong
Published: March 2003

We propose a general family of nonparametric mixed effects models. Smoothing splines are used to model the fixed effects and are estimated by maximizing the penalized likelihood function. The random effects are generic and are modelled parametrically by assuming that the covariance function depends on a parsimonious set of parameters. These parameters and the smoothing parameter are estimated simultaneously by the generalized maximum likelihood method. We derive a connection between a nonparametric mixed effects model and a linear mixed effects model. This connection suggests a way of fitting a nonparametric mixed effects model by using existing programs. The classical two-way mixed models and growth curve models are used as examples to demonstrate how to use smoothing spline analysis-of-variance decompositions to build nonparametric mixed effects models. Similarly to the classical analysis of variance, components of these nonparametric mixed effects models can be interpreted as main effects and interactions. The penalized likelihood estimates of the fixed effects in a two-way mixed model are extensions of James–Stein shrinkage estimates to correlated observations. In an example three nested nonparametric mixed effects models are fitted to a longitudinal data set.
Boundary Detection Through Dynamic Polygons
Authors: Pievatolo A. , Green Peter J.
Published: March 2003

A method for the Bayesian restoration of noisy binary images portraying an object with constant grey level on a background is presented. The restoration, performed by fitting a polygon with any number of sides to the object's outline, is driven by a new probabilistic model for the generation of polygons in a compact subset of R2, which is used as a prior distribution for the polygon. Some measurability issues raised by the correct specification of the model are addressed. The simulation from the prior and the calculation of the a posteriori mean of grey levels are carried out through reversible jump Markov chain Monte Carlo computation, whose implementation and convergence properties are also discussed. One example of restoration of a synthetic image is presented and compared with existing pixel-based methods.
On the Estimation of an Instantaneous Transformation for Time Series
Authors: Xia Yingcun , Tong Howell , Li W. K. , Zhu Li-Xing
Published: March 2003

We modify Ramsay's algorithm for estimating monotonic transformations in regression and extend it to autoregression, where strict monotonicity is an essential requirement. Compared with other methods, our method can capture some characteristics that are pertinent to the time series and is much easier to implement. An order selection method is introduced and developed. Some real data sets are analysed.
Closure of the Class of Binary Generalized Linear Models in Some Non-standard Settings
Authors: Neuhaus John M.
Published: March 2003

This paper considers fitting generalized linear models to binary data in nonstandard settings such as case–control samples, studies with misclassified responses and misspecified models. We develop simple methods for fitting models to case–control data and show that a closure property holds for generalized linear models in the nonstandard settings, i.e. if the responses follow a generalized linear model in the population of interest, then so will the observed response in the non-standard setting, but with a modified link function. These results imply that we can analyse data and study problems in the non-standard settings by using classical generalized linear model methods such as the iteratively reweighted least squares algorithm. Example data illustrate the results.
Non-conjugate Prior Distribution Assessment for Multivariate Normal Sampling
Authors: Garthwaite Paul H. , Al-Awadhi Shafeeqah A.
Published: March 2003

Elicitation methods are proposed for quantifying expert opinion about a multivariate normal sampling model. The natural conjugate prior family imposes a relationship between the mean vector and the covariance matrix that can portray an expert's opinion poorly. Instead we assume that opinions about the mean and the covariance are independent and suggest innovative forms of question which enable the expert to quantify separately his or her opinion about each of these parameters. Prior opinion about the mean vector is modelled by a multivariate normal distribution and about the covariance matrix by both an inverse Wishart distribution and a generalized inverse-Wishart (GIW) distribution. To construct the latter, results are developed that give insight into the GIW parameters and their interrelationships. Certain of the elicitation methods exploit unconditional assessments as fully as possible, since these can reflect an expert's beliefs more accurately than conditional assessments. Methods are illustrated through an example.
Smoothing for Discrete-Valued Time Series
Authors: Cai Zongwu , Yao Qiwei , Zhang Wenyang
Published: March 2003

We deal with smoothed estimators for conditional probability functions of discrete-valued time series {Yt} under two different settings. When the conditional distribution of Yt given its lagged values falls in a parametric family and depends on exogenous random variables, a smoothed maximum (partial) likelihood estimator for the unknown parameter is proposed. While there is no prior information on the distribution, various nonparametric estimation methods have been compared and the adjusted Nadaraya–Watson estimator stands out as it shares the advantages of both Nadaraya–Watson and local linear regression estimators. The asymptotic normality of the estimators proposed has been established in the manner of sparse asymptotics, which shows that the smoothed methods proposed outperform their conventional, unsmoothed, parametric counterparts under very mild conditions. Simulation results lend further support to this assertion. Finally, the new method is illustrated via a real data set concerning the relationship between the number of daily hospital admissions and the levels of pollutants in Hong Kong in 1994–1995. An ad hoc model selection procedure based on a local Akaike information criterion is proposed to select the significant pollutant indices.
Particle Filtering for Partially Observed Gaussian State Space Models
Authors: Andrieu Christophe , Doucet Arnaud
Published: March 2003

Solving Bayesian estimation problems where the posterior distribution evolves over time through the accumulation of data has many applications for dynamic models. A large number of algorithms based on particle filtering methods, also known as sequential Monte Carlo algorithms, have recently been proposed to solve these problems. We propose a special particle filtering method which uses random mixtures of normal distributions to represent the posterior distributions of partially observed Gaussian state space models. This algorithm is based on a marginalization idea for improving efficiency and can lead to substantial gains over standard algorithms. It differs from previous algorithms which were only applicable to conditionally linear Gaussian state space models. Computer simulations are carried out to evaluate the performance of the proposed algorithm for dynamic tobit and probit models.
Parametric Multiplicative Intensities Models Fitted to Bus Motor Failure Data
Authors: Lindsey J. K.
Published: March 2003

Classes of two- and three-parameter multiplicative intensities models are presented. These have the advantage that they can be simply fitted by standard software. The two-parameter models include the Weibull and extreme value models, whereas the three-parameter models can have bathtub-shaped forms. The bus motor failure data are reanalysed to show that all five failures of the 191 buses can be adequately described by the same two-parameter multiplicative intensity model based on the squared logarithm of distance travelled in service since previous repair. This is a new member of the multiplicative intensity family, based on the squared logarithm of distance, or of time for survival data.
Charpy Impact Energy Data: A Markov Chain Monte Carlo Analysis
Authors: Stephens D. A. , Smith A. F. M.
Published: March 2003

To assess radiation damage in steel for reactor pressure vessels in the nuclear industry, specimens are subjected to the Charpy test, which measures how much energy a specimen can absorb at a given test temperature before cracking. The resulting Charpy impact energy data are well represented by a three-parameter Burr curve as a function of test temperature, in which the parameters of the Burr curve are themselves dependent on irradiation dose. The resulting non-linear model function, combined with heteroscedastic random errors, gives rise to complicated likelihood surfaces that make conventional statistical techniques difficult to implement. To compute estimates of parameters of practical interest, Markov chain Monte Carlo sampling-based techniques are implemented. The approach is applied to 40 data sets from specimens subjected to no irradiation or one or two doses of irradiation. The influence of irradiation dose on the amount of energy absorbed is investigated.
The Binomial Failure Rate Mixture Model for Common Cause Failure Data From the Nuclear Industry
Authors: Kvam Paul H.
Published: March 2003

A model for the lifetime of a system is considered in which the system is susceptible to simultaneous failures of two or more components, the failures having a common external cause. Three sets of discrete failure data from the US nuclear industry are examined to motivate and illustrate the model derivation: they are for motor-operated valves, cooling fans and emergency diesel generators. To achieve target reliabilities, these components must be placed in systems that have built-in redundancy. Consequently, multiple failures due to a common cause are critical in the risk of core meltdown. Vesely has offered a simple methodology for inference, called the binomial failure rate model: external events are assumed to be governed by a Poisson shock model in which resulting shocks kill X out of m system components, X having a binomial distribution with parameters (m, p), 0<p<1. In many applications the binomial failure rate model fits failure data poorly, and the model has not typically been applied to probabilistic risk assessments in the nuclear industry. We introduce a realistic generalization of the binomial failure rate model by assigning a mixing distribution to the unknown parameter p. The distribution is generally identifiable, and its unique nonparametric maximum likelihood estimator can be obtained by using a simple iterative scheme.
Exact Principal Component Influence Measures Applied to the Analysis of Spectroscopic Data on Rice
Authors: Mertens B. J. A.
Published: March 2003

Exact influence measures are applied in the evaluation of a principal component decomposition for high dimensional data. Some data used for classifying samples of rice from their near infra-red transmission profiles, following a preliminary principal component analysis, are examined in detail. A normalization of eigenvalue influence statistics is proposed which ensures that measures reflect the relative orientations of observations, rather than their overall Euclidean distance from the sample mean. Thus, the analyst obtains more information from an analysis of eigenvalues than from approximate approaches to eigenvalue influence. This is particularly important for high dimensional data where a complete investigation of eigenvector perturbations may be cumbersome. The results are used to suggest a new class of influence measures based on ratios of Euclidean distances in orthogonal spaces.
Application of Markov Chain Monte Carlo Methods to Modelling Birth Prevalence of Down Syndrome
Authors: Bray Isabelle , Wright David E.
Published: March 2003

Data collected before the routine application of prenatal screening are of unique value in estimating the natural live-birth prevalence of Down syndrome. However, much of these data are from births from over 20 years ago and they are of uncertain quality. In particular, they are subject to varying degrees of underascertainment. Published approaches have used ad hoc corrections to deal with this problem or have been restricted to data sets in which ascertainment is assumed to be complete. In this paper we adopt a Bayesian approach to modelling ascertainment and live-birth prevalence. We consider three prior specifications concerning ascertainment and compare predicted maternal-age-specific prevalence under these three different prior specifications. The computations are carried out by using Markov chain Monte Carlo methods in which model parameters and missing data are sampled.
Stochastic Templates for Aquaculture Images and a Parallel Pattern Detector
Authors: De Souza K. M. A. , Kent J. T. , Mardia K. V.
Published: March 2003

A general statistical approach is presented for the identification of objects in digital images, motivated by an application in aquaculture involving underwater images of fish. Using Procrustes analysis, a point distribution model is fitted on a set of training images and used as a prior distribution for the shape of a deformable template. The likelihood of a proposed template is calculated in terms of the response from a feature detector along the boundary of the template. The posterior distribution of template variables is examined by using Markov chain Monte Carlo analysis. A key challenge in the aquaculture application is the variable nature of edges arising from the surface curvature of fish and the low contrast between the foreground and background. Conventional gradient-based edge detection proves inadequate, but a parallel pattern detector copes much better. Results are presented for a fully automated analysis of the database. The strengths and weaknesses of this approach are discussed and future developments are outlined.
Modelling and Analysis of Data that Exhibit Temporal Decay
Authors: Faddy M. F. , Jones M. C.
Published: March 2003

Two recent examples of data from statistical consulting work form the basis of this paper. Both data sets arose from situations where temporal decay was expected, and different models for this are discussed, in particular, variants on simple exponential decay: hyperexponential (or mixed exponential) decay and models based on differential equations. Nonparametric estimation, using local quadratic smoothing of the data, of components of these models is discussed and employed to help to check the appropriateness or otherwise of existing parametric analyses of the data. In one example, the suitability of the parametric analysis is confirmed; in the other, the parametric assumptions made at the time are shown to have some flaws, and an improved parametric analysis is provided.
Factors Influencing Soay Sheep Survival
Authors: Catchpole E. A. , Morgan B. J. T. , Coulson T. N. , Freeman S. N. , Albon S. D.
Published: March 2003

We present a survival analysis of Soay sheep mark recapture and recovery data. Unlike previous conditional analyses, it is not necessary to assume equality of recovery and recapture probabilities; instead these are estimated by maximum likelihood. Male and female sheep are treated separately, with the higher numbers and survival probabilities of the females resulting in a more complex model than that used for the males. In both cases, however, age and time aspects need to be included and there is a strong indication of a reduction in survival for sheep aged 7 years or more. Time variation in survival is related to the size of the population and selected weather variables, by using logistic regression. The size of the population significantly affects the survival probabilities of male and female lambs, and of female sheep aged 7 or more years. March rainfall and a measure of the North Atlantic oscillation are found to influence survival significantly for all age groups considered, for both males and females. Either of these weather variables can be used in a model. Several phenotypic and genotypic individual covariates are also fitted. The only covariate which is found to influence survival significantly is the type of horn of first-year female sheep. There is a substantial variation in the recovery probabilities over time, reflecting in part the increased effort when a population crash was expected. The goodness of fit of the model is checked by using graphical procedures.
Spectral Analysis of Combustion Noise and Flame Pattern Recognition
Authors: Yuan J. , Zhang Y.
Published: March 2003

In many situations, flame patterns in a combustion chamber cannot be observed directly by using an electronic or optical probe. However, an experienced engineer can identify the burning process by listening to the noise that it generates. In this paper, we study acoustic characteristics of turbulent impinging flames by using spectral analysis and statistical pattern recognition. By experimenting with the ignition method, different flame patterns were generated in a laboratory. We find that each flame pattern can be characterized effectively by using the power spectrum of the noise and can be identified by using this information alone.
Flexible Estimates of Tumour Incidence for Intermediately Lethal Tumours in a Typical Long-Term Animal Bioassay
Authors: Parise Helen , Dinse Gregg E. , Ryan Louise M.
Published: March 2003

The estimation of the incidence of tumours in an animal carcinogenicity study is complicated by the occult nature of the tumours involved (i.e. tumours are not observable before an animal’s death). Also, the lethality of tumours is generally unknown, making the tumour incidence function non-identifiable without interim sacrifices, cause-of-death data or modelling assumptions. Although Kaplan–Meier curves for overall survival are typically displayed, obtaining analogous plots for tumour incidence generally requires fairly elaborate model fitting. We present a case-study of tetrafluoroethylene to illustrate a simple method for estimating the incidence of tumours as a function of more easily estimable components. One of the components, tumour prevalence, is modelled by using a generalized additive model, which leads to estimates that are more flexible than those derived under the usual parametric models. A multiplicative assumption for tumour lethality allows for the incorporation of concomitant information, such as the size of tumours. Our approach requires only terminal sacrifice data although additional sacrifice data are easily accommodated. Simulations are used to illustrate the estimator proposed and to evaluate its properties. The method also yields a simple summary measure of tumour lethality, which can be helpful in interpreting the results of a study.
Estimation of the Basic Reproduction Number for Infectious Diseases from Age-Stratified Serological Survey Data
Authors: Farrington C. P. , Kanaan M. N. , Gay N. J.
Published: March 2003

The basic reproduction number of an infection, R0, is the average number of secondary infections generated by a single typical infective individual in a totally susceptible population. It is directly related to the effort required to eliminate infection. We consider statistical methods for estimating R0 from age-stratified serological survey data. The main difficulty is indeterminacy, since the contacts between individuals of different ages are not observed. We show that, given an estimate of the average age-specific hazard of infection, a particular leading left eigenfunction is required to specify R0. We review existing methods of estimation in the light of this indeterminacy. We suggest using data from several infections transmitted via the same route, and we propose that the choice of model be guided by a criterion based on similarity of their contact functions. This approach also allows model uncertainty to be taken into account. If one infection induces no lasting immunity, we show that the only additional assumption required to estimate R0 is that the contact function is symmetric. When matched data on two or more infections transmitted by the same route are available, the methods may be extended to incorporate the effect of individual heterogeneity. The approach can also be applied in partially vaccinated populations and to populations comprising loosely linked communities. The methods are illustrated with data on hepatitis A, mumps, rubella, parvovirus, Haemophilus influenzae type b and measles infection.
Analysis of the Bovine Spongiform Encephalopathy Maternal Cohort Study, Revisited
Authors: Cooper J. D. , Bird S. M.
Published: March 2003

The bovine spongiform encephalopathy (BSE) maternal cohort study provided robust evidence of an enhanced risk of developing BSE for offspring of BSE-affected dams. We present for the first time, but in retrospect, an interim analysis of the BSE maternal cohort study and set it in historical context, some of which has only been revealed through the BSE inquiry. We also consider the implications for design of extending the BSE maternal cohort study once an enhanced risk to exposed calves had been established, to assess the risk to calves born further from the clinical onset of BSE in the dam than those in the original study. We demonstrate that, if a data monitoring committee had been established, conclusions similar to those based on the final results could have been drawn several years before the completion of the BSE maternal cohort study. Further, we conclude that an extension of the cohort study is unlikely to have been commissioned because of the substantial financial investment required, yet low power, and practical difficulties associated with implementation of any worthwhile extension.
Spurious regressions between I(1) processes with long memory errors
Authors: Cappuccio Nunzio , Lubian Diego
Published: March 2003

In this paper we develop the asymptotic distribution theory for spurious regression between I(1) processes with long‐memory stationary errors. Our result departs from the standard results of Phillips (Understanding spurious regression in econometrics. J. Economet. 33 (1986), 311–40) in two respects. First, the limit theory we apply is based on a functional central limit theorem for stationary linear processes whose spectral density at frequency zero may diverge or collapse to zero. Second, different limit distributions may apply depending on the form of long memory exhibited by the error term. We also discuss the extension of our analyis to spurious regression with fitted intercept.
The zero‐crossing rate of pth‐order autoregressive processes
Authors: Cheng Ximing , Wu Yougui , Du Jinguan , Liu Huowang
Published: March 2003

He and Kedem have studied the relationship between the zero‐ crossing rate (ZCR) of a second‐o rder autoregressive process and its characteristic roots and have found that, when the roots are on the unit circle, the ZCR converges in mean square to θ/π very quickly regardless of the noise level. In this paper, the ZCR of a pth‐order autoregressive process ((AR)p) is investigated. The relationships betwe en the ZCR and the one‐step asymptotic correlation function (ACF) and between the one‐step ACF and the characteristic roots of the AR(p) model are discussed, and some links between the convergence rate of the ZCR and the characte ristic roots are considered.
Testing for a Unit Root in Autoregressive Moving‐average Models with Missing Data
Authors: Shin Dong Wan , Sarkar Sahadeb
Published: March 2003

Testing for a single autoregressive unit root in an autoregressive moving‐average (ARMA) model is considered in the case when data contain missing values. The proposed test statistics are based on an ordinary least squares type estimator of the unit root parameter which is a simple approximation of the one‐step Newton–Raphson estimator. The limiting distributions of the test statistics are the same as those of the regression statistics in AR(1) models tabulated by Dickey and Fuller (Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74 (1979), 427–31) for the complete data situation. The tests accommodate models with a fitted intercept and a fitted time trend.
A Note on Modelling Seasonal Processes in Continuous Time
Authors: Chambers Marcus J.
Published: March 2003

It is shown that stochastic differential equations are capable of handling stationary seasonal processes but not processes that are integrated of order 1 at frequency π. Such seasonally integrated processes can be modelled in continuous time but require the use of mixed differential‐‐difference equations.
Aggregation and Disaggregation of Structural Time Series Models
Authors: Hotta Luiz K. , Vasconcellos Klaus L.
Published: March 2003

The aggregation/disaggregation problem has been widely studied in the time series literature. Some main issues related to this problem are modelling, prediction and robustness to outliers. In this paper we look at the modelling problem with particular interest in the local level and local trend structural time series models together with their corresponding ARIMA(0, 1, 1) and ARIMA(0, 2, 2) representations. Given an observed time series that can be expressed by a structural or autoregressive integrated moving‐average (ARIMA) model, we derive the necessary and sufficient conditions under which the aggregate and/or disaggregate series can be expressed by the same class of model. Harvey's cycle and seasonal components models (Harvey, Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge: Cambridge University Press, 1989) are also briefly discussed. Systematic sampling of structural and ARIMA models is also discussed.
The Local Bootstrap for Periodogram Statistics
Authors: Paparoditis Efstathios , Politis Dimitris N.
Published: March 2003

A bootstrap procedure for the periodogram of a weakly dependent stationary sequence is proposed. The method works by locally resampling the periodogram ordinates and does not require estimation of the spectral density and of frequency domain residuals obtained by means of initial smoothing. Asymptotic properties of the proposed bootstrap procedure are studied and consistency is proved for interesting classes of statistics including ratio statistics, kernel estimates of the spectral density and parameter estimates. Some practical aspects concerning the implementation of the method are also discussed.
Projection Pursuit Autoregression in Time Series
Authors: Xia Xingcun , An H. Z.
Published: March 2003

Projection pursuit regression is an efficient method of coping with the ‘curse of dimensionality’ in nonparametric regressions. An extension of the idea of projection pursuit regression to nonparametric autoregressions in time series is made in this paper. Some related theories are constructed.
Recursive Prediction and Likelihood Evaluation for Periodic ARMA Models
Authors: Lund Robert , Basawa I. V.
Published: March 2003

This paper explores recursive prediction and likelihood evaluation techniques for periodic autoregressive moving‐average (PARMA) time series models. The innovations algorithm is used to develop a simple recursive scheme for computing one‐step‐ahead predictors and their mean squared errors. The asymptotic form of this recursion is explored. The prediction results are then used to develop an efficient (and exact) PARMA likelihood evaluation algorithm for Gaussian series. We then show how a multivariate autoregressive moving average (ARMA) likelihood can be evaluated by writing the multivariate ARMA model in PARMA form. Explicit calculations for PARMA(1, 1) models and periodic autoregressions are included.
The Changing Informal Labour Market in Brazil: Cyclicality versus Excessive Intervention
Authors: Carneiro Francisco
Published: March 2003

Although following an anti‐cyclical behaviour over the last decade, Brazil's informal sector seems to have grown recently as a response to excessive government intervention in the productive system. Despite high inflation and sluggish economic performance, Brazil continues to live with low unemployment and only sporadic social unrest. A substantial informal sector is thought to be acting as a cushion to help minimize labour market instability. This paper reviews the main concepts and methodologies proposed in the literature, summarizes the results obtained by different authors, and presents some additional information on the recent informalization of the Brazilian labour markets.
Breach Penalties in Labour Contracts: Advance Notice and Severance Pay
Authors: Pita Cristina
Published: March 2003

This paper analyses the determining factors of advance notice and severance pay provisions that appear in collective bargaining agreements. The theoretical model constructed is a simple bargaining model in which advance notice and severance pay play the role of breach penalties that the firm must pay for breaking the employment relationship. The hypotheses are tested on data gathered from collective bargaining agreements from different industries that were signed between 1970 and 1989.
The 1995 Pension Reform: Equity, Sustainability and Indexation
Authors: Gronchi Sandro , Aprile Rocco
Published: March 2003

In financial equilibrium, a pay‐as‐you‐go pension system will offer yields (explicit or implicit) equal to the rate of growth of the incomes subject to the contribution levy or of reasonable proxies such as GDP. One virtue of the contribution‐based award formula is that it makes this yield explicit, permitting immediate verification of the system's sustainability. A second virtue is that it ensures equal treatment of all participants. Without prejudicing the constraint of financial sustainability, a pension scheme may choose any one of an infinite number of pairs (combinations) of initial award and indexation rates; or, in other words, any of an infinite set of curves of the total benefit over time. The 1995 pension reform enacted under Lamberto Dini elected zero real indexation, thus permitting higher initial income replacement rates. Insofar as it will generate pension disparities depending on year of retirement, this choice is the likely prelude to creeping real indexation, or recurrent equalizations that will drive the overall yield on contributions above the GDP growth rate and violate the constraint of financial sustainability. Before insoluble problems arise, the reformed system must be amended to decrease income replacement rates to a level compatible with an indexation procedure (be it automatic or discretionary) for adjusting benefits to society's real living standards, as all main pension systems do. The article also criticizes other choices made in the formulation of the law that will prevent the reform from achieving its stated purposes.
The Politics of Voluntary and Mandatory Pensions in the Nordic Countries
Authors: Overbye Einar
Published: March 2003

The article provides an overview of the voluntary (private) pension sector in the Nordic countries, and relates it to the mandatory (public) pension system. It also provides an account of how the Nordic countries have tried to cope with various weaknesses of voluntary occupational plans. The problem of State involvement in the voluntary pension sector is discussed. Concerning this point four problems are underlined: inclusion of marginal labour; securing the pension promise in the case of bankruptcy; conflicting interests in fund management; tax treatment.
Unemployment and Endogenous Growth
Authors: Van Schaik Anton B. T. M. , De Groot Henri L. F.
Published: March 2003

In this paper we develop a two‐sector endogenous growth model with a dual labour market, based on efficiency wages. Growth is driven by intentional R&D performed in the high‐tech and high‐wage sector. How a change in rivalry among firms affects simultaneously growth and unemployment is examined. On the one hand, an increase of the elasticity of substitution between the product varieties of different high‐tech firms reduces market power and leads to higher growth but reduces job prospects. On the other hand, if barriers to entry exist, an increase of the number of rivals in the market (due to removal of entry barriers) leads to lower growth, whereas the effect on aggregate employment is ambiguous.
Accounting for Strikes: Evidence from UK Manufacturing in the 1980s
Authors: Nicolitsas Daphne
Published: March 2003

The decrease in the number of strikes in the UK during the 1980s has revived the discussion on the explanatory factors of strike frequency. This paper investigates explanations for the time variation of strike frequency in different industries of British manufacturing. The framework used is that of the joint cost model of strikes; strike frequency is inversely related to strike costs. The results from a panel of 90 manufacturing industries for the period 1983–88 show some support for the hypothesis that strikes decreased because they became more expensive. In the main, we find that factors that affect both employers and employees (such as revenue, inventories) are significant in explaining variations in strike frequency. Factors that affect only employees, however, such as the unemployment rate, are not.
Technology Effects on Wages in Finnish Manufacturing
Authors: Laaksonen Seppo , Vainiomäki Jari
Published: March 2003

In this paper we study the effect of technology on establishment‐level wages using a classification of manufacturing industries based on four technology levels. The technology wage premiums are estimated separately for non‐manual and manual workers using wage equations with available control variables for plant and workforce characteristics (human capital) over the time period 1974–93. The results do not show a straightforward increasing relationship between a plant’s average wages and its technology at higher technology levels. However, establishments with the lowest technology level paid the lowest wages during the whole period. We also find that the relative non‐manual to manual wage ratio increased over the time period in the highest technology levels. These findings are consistent with technology wage premiums and skill‐biased technological change found in studies for other countries
A New Look at Gender Effects in Participation and Occupation Choice
Authors: Soopramanien Didier , Johnes Geraint
Published: March 2003

In this paper we evaluate the extent to which changes over time in women’s labour market destinations are due to characteristics, on the one hand, and prices, on the other. Multinomial and nested logit methods are used to analyse US data for 1970 and 1990, and the results are compared. The latter method, which has not previously been employed in the present context, alleviates problems due to the strong assumption in simpler models of the independence of irrelevant alternatives, and provides much additional useful information.
The Distribution of the German Public–Private Wage Gap
Authors: Jürges Hendrik
Published: March 2003

This paper provides a distributional analysis of the public–private sector wage gap in Germany from 1984 to 1996. The public sector wage distribution is generally less dispersed than the private sector wage distribution. The raw wage differential is positive for males who are at the lower tail of the male wage distribution and negative at the upper tail. In contrast, females enjoy positive wage gaps along most part of the wage distribution. A decomposition analysis reveals that the male wage premium, i.e. the part of the wage gap not accounted for by differences in observable characteristics, is uniformly negative, whereas the female wage premium is positive.
Occupational Upgrading and the Business Cycle
Authors: Devereux Paul J.
Published: March 2003

Many economics theories suggest that the assignment of workers to occupations changes over the business cycle: expansions allow workers to upgrade to occupations that pay higher wages and require more skill. This paper provides some empirical evidence from the USA that such upgrading does occur and that, as predicted, it has greater effects on less‐skilled individuals. Furthermore, the skill composition of new hires changes over the business cycle, even within occupations. Consistent with a job competition model, the education levels of new hires within occupations are higher when the unemployment rate is high and this effect is more pronounced in lower‐paying occupations. The changes in assignment imply that low‐skilled individuals suffer most from recessions in terms of occupation quality and unemployment. The results are consistent with employers responding to a greater supply of educated workers by increasing hiring standards, and so imply that the social return to education may be lower than the private return. However, the results are also consistent with more neo‐classical models of the labor market.
Orientations and Motivation in the Acquisition of English as a Second Language Among High School Students in Quebec City
Authors: Belmechri Faïza , Hummel Kirsten
Published: March 2003

We investigated the emergence of orientations and their relation to motivation in a predominantly monolingual context. In this context (Quebec City), a previous study (Kruidenier & Clément, 1986) revealed that students' orientations toward learning English as a second language (ESL) were: friendship, travel, prestige, and knowledge/respect. The present study' s participants consisted of 93 francophone Grade 11 high school students learning ESL. We used an adapted form of Kruidenier and Clément's Likert‐type scale questionnaire. We ran factor analyses and a multiple regression analysis on the data. Results indicated that students' orientations were: travel, understanding/ school (instrumental), friendship, understanding, and career (instrumental); they also demonstrated that orientations were predictors of their motivation. However, the absence of an integrative orientation here does not justify its exclusion in the assessment of motivation, because it could emerge in other contexts.
The Role of Diminutives in the Acquisition of Russian Gender: Can Elements of Child‐Directed Speech Aid in Learning Morphology?
Authors: Kempe Vera , Brooks Patricia J.
Published: March 2003

Diminutives are a pervasive feature of child‐directed speech (CDS) in Russian. Their frequent use might be beneficial for gender acquisition because it eliminates nontransparent morphophonological marking. To examine the effect of diminutives on gender learning, adult native speakers of English were taught Russian nouns, with half of the participants trained on diminutive nouns and half on the nondiminutive base forms. Over four sessions, participants learned to use adjectives that had to agree in gender with nouns. Learners were then tested on various types of novel nouns. The diminutive training group demonstrated better learning of noun gender, and better generalization to novel forms, indicating that regularization of gender marking through diminutives promotes the extraction of morphophonological regularities.