bluuebunny/crossref_metadata_2025 huggingface dataset:
Coloured overlays and their benefit for reading
Authors: Wilkins Arnold , Lewis Elizabeth , Smith Fiona , Rowland Elizabeth , Tweedie Wendy
Published: March 2003

Children in mainstream schools compared text on white paper with identical text covered in turn by each of ten differently‐coloured plastic overlays. More than 50% reported improved perception with one or more colours, and were given the best colour to use. About half used the overlays for more than three months and their reading speed increased when the overlays were used. In Study I children were examined twice using different methods and examiners. 47% chose the same colour on both occasions, and showed the greatest improvement in reading fluency, reliable at retest. In Study II children were issued with a random colour, and the greatest improvements in reading speed occurred when the random colour was the same as that previously chosen. Phonological reading strategies were not related to visual symptoms, or the benefit from overlays. Across individuals, reading speed varied by a factor of more than three, for good and poor readers. Study III provided estimates of the prevalence of improvements based on a sample of 426 children from 12 schools: 5% of children read more than 25% faster with a coloured overlay.
Teachers’ expectations about students’ use of reading strategies, knowledge and behaviour in Grades 3, 5 and 7
Authors: Arabsolghar Fatemeh , Elkins John
Published: March 2003

Although extensive basic research has been carried out on children’s metacognition, little is known about teachers’ views of their students’ cognitive and metacognitive skills in reading. The ways in which teachers expected their children to use, or to know how to use, certain reading skills are examined in this study. A questionnaire on reading components (strategies, knowledge and behaviour) was completed by 45 teachers in Grades 3, 5 and 7. In this questionnaire teachers were asked to make judgements about whether or not students of high, average and low ability levels in their classes would be likely to show these skills. An analysis of variance (grade×ability×component) revealed a significant interaction between ability and component. There was much greater variability in the three components for the low and average levels of ability. The main effect for ability was significant. The highest expectations of teachers were for high‐ability students in all the three groups of items, followed by average and low‐ability students. The main effect for component was also significant for knowledge. There was no significant difference between the grades. However, teachers hold equivalent performance expectations for high‐ability students in each of the three components, but for average and low‐ability groups, expectations were higher for knowledge than strategy and behaviour.
The prevalence and associations of reading difficulties in a population of South Indian children
Authors: Bhakta Preetha , Hackett Richard J. , Hackett Latha
Published: March 2003

A random sample of 1192 children aged 8–12 years living in Calicut District, Kerala, South India, underwent testing with the Malayalam Graded Reading Test as part of an epidemiological study of neuropsychiatric disorders. The prevalence of reading difficulty was 8.2% and it was associated with younger age, male sex, poverty, less‐educated parents, psychiatric disturbance, school failure, poor school attendance, physical ill‐health, poor motor co‐ordination and impaired vocabulary and visuo‐spatial reasoning. The findings support a multifactorial causal model of reading difficulties in this child population.
What Works?: Selectivity Models and Meta-Analysis
Authors: Copas John
Published: March 2003

What works seeks to identify rehabilitative treatments which are successful in reducing the likelihood that offenders will reoffend. A large number of small case–control studies have been reported in the literature, but with conflicting results. Meta-analysis has been used to reconcile these findings, but again with conflicting results. We reanalyse one of the published meta-analyses in the corrections literature and argue the importance of specifically modelling heterogeneity and selection bias. A sensitivity approach is advocated, suggesting lower average effects and substantially increased measures of uncertainty. The method is tested on a medical example where independent confirmation from a large controlled trial is also available.
Multivariate Shrinkage Estimation of Small Area Means and Proportions
Authors: Longford Nicholas T.
Published: March 2003

The familiar (univariate) shrinkage estimator of a small area mean or proportion combines information from the small area and a national survey. We define a multivariate shrinkage estimator which combines information also across subpopulations and outcome variables. The superiority of the multivariate shrinkage over univariate shrinkage, and of the univariate shrinkage over the unbiased (sample) means, is illustrated on examples of estimating the local area rates of economic activity in the subpopulations defined by ethnicity, age and sex. The examples use the sample of anonymized records of individuals from the 1991 UK census. The method requires no distributional assumptions but relies on the appropriateness of the quadratic loss function. The implementation of the method involves minimum outlay of computing. Multivariate shrinkage is particularly effective when the area level means are highly correlated and the sample means of one or a few components have small sampling and between-area variances. Estimations for subpopulations based on small samples can be greatly improved by incorporating information from subpopulations with larger sample sizes.
Analysis of Cervical Cancer Mortality and Incidence Data from England and Wales: Evidence of a Beneficial Effect of Screening
Authors: Sasieni Peter D. , Adams Joanna
Published: March 2003

Incidence and mortality rates for invasive cervical cancer in England and Wales have changed considerably since records began in 1950s. It is well known that cervical cancer rates show substantial birth cohort effects and these make the interpretation of secular trends difficult. Here the incidence data (from 1971) and mortality data (from 1950) are analysed by using generalized additive models. The goodness of fit is evaluated by using residual plots and scaled changes in deviance. The incidence of squamous cell and adenocarcinoma of the cervix were analysed separately after making an adjustment for those with unknown histology. Although the age and cohort model provides an adequate description of the data up to the mid-1980s, additional terms are required to model the more recent data. Reasonable fits are obtained by adding functions of calendar year since 1978 in each of three age bands. Estimates of the cohort effects obtained from the incidence of squamous cancer and mortality data are remarkably similar and fit with hypothesized effects of changing sexual norms on cervical cancer rates. Secular trends suggest that cervical screening had little effect on cervical cancer rates before the mid-1980s and that since then the effect on mortality and incidence of squamous cell carcinoma of the cervix has been substantial whereas the effect on the incidence of adenocarcinoma of the cervix has been modest. Secular trends are greatest in younger women. They are minimal in women aged 70 years and over. If the secular trends are attributed to cervical screening, the models estimate that, as a result of screening, approximately 6000 fewer deaths occurred between 1991 and 1997 and that approximately 4000 fewer squamous cancers were diagnosed between 1989 and 1992.
Disease Clusters: Should They Be Investigated, And, If So, When and How?
Authors: Elliott Paul , Wakefield Jon
Published: March 2003

Individual cluster reports are subject to several difficulties in interpretation. Although they rarely lead to new aetiological insights, a public health response to delineate the size and extent of any excess risk may be warranted. Further investigation, where merited, should usually include an examination of data for different areas and/or different time periods. A statistical evaluation of disease clusters is often secondary to a detailed appreciation of issues such as the availability and quality of data, confounding and bias in the selection of areas for study.
Ecological Regression Analysis of Environmental Benzene Exposure and Childhood Leukaemia: Sensitivity to Data Inaccuracies, Geographical Scale and Ecological Bias
Authors: Best Nicky , Cockings Samantha , Bennett James , Wakefield Jon , Elliott Paul
Published: March 2003

Benzene is classified as a group 1 human carcinogen by the International Agency for Research on Cancer, and it is now accepted that occupational exposure is associated with an increased risk of various leukaemias. However, occupational exposure accounts for less than 1% of all benzene exposures, the major sources being cigarette smoking and vehicle exhaust emissions. Whether such low level exposures to environmental benzene are also associated with the risk of leukaemia is currently not known. In this study, we investigate the relationship between benzene emissions arising from outdoor sources (predominantly road traffic and petrol stations) and the incidence of childhood leukaemia in Greater London. An ecological design was used because of the rarity of the disease, the difficulty of obtaining individual level measurements of benzene exposure and the availability of data. However, some methodological difficulties were encountered, including problems of case registration errors, the choice of geographical areas for analysis, exposure measurement errors and ecological bias. We use a Bayesian hierarchical modelling framework to address these issues, and we investigate the sensitivity of our inference to various modelling assumptions.
A Comparison of Suicide Patterns in Australia and Hong Kong
Authors: Lloyd Chris J. , Yip Paul S. F.
Published: March 2003

We analyse the patterns of 6564 suicides in Hong Kong and 23671 suicides in Australia for the period 1981–1993. Within the unifying framework of logistic regression we investigate how suicide rates vary with marital status and age and how these patterns vary over time and between the two cultures. The main significant differences between the two cultures are that rates are higher in Australia, rates for males are much higher than for females in Australia but only slightly higher in Hong Kong, in Hong Kong the oldest age group has the highest suicide rate unlike in Australia and the protective effects of marriage are larger in Australia.
Screening for Prostate Cancer by Using Random-Effects Models
Authors: Brant Larry J. , Sheng Shan L. , Morrell Christopher H. , Verbeke Geert N. , Lesaffre Emmanuel , Carter H. Ballentine
Published: March 2003

Random-effects models are used to screen male participants in a long-term longitudinal study for prostate cancer. By using posterior probabilities, each male can be classified into one of four diagnostic states for prostate disease: normal, benign prostatic hyperplasia, local cancer and metastatic cancer. Repeated measurements of prostate-specific antigen, collected when there was no clinical evidence of prostate disease, are used in the classification process. An individual's screening data are considered one repeated measurement at a time as his data are collected longitudinally over time. Posterior probabilities are calculated on the basis of data from other individuals with confirmed diagnoses of each of the four diagnostic states.
Random-Digit Dialling in the UK: Viability Revisited
Authors: Nicolaas Gerry , Lynn Peter
Published: March 2003

A main obstacle to using telephone methods for social surveys in the UK has been the inability to select a representative and unbiased probability sample of the general population. However, recent changes in the UK telephone numbering system and advances in telephony software appear to provide the means for using random-digit dialling (RDD). This paper presents the results of a study designed to test the feasibility of RDD. The focus is on the sampling method, but we also assess other aspects of the RDD survey method, in the context of a total survey error framework. Although we conclude that RDD is now a viable survey method in the UK, further research and development is advocated to improve the efficiency of fieldwork and response rates.
Generalized Score Test of Homogeneity Based on Correlated Random Effects Models
Authors: Commenges D. , Jacqmin-Gadda H.
Published: March 2003

We present a family of tests based on correlated random effects models which provides a synthesis and a generalization of recent work on homogeneity testing. In these models each subject has a particular random effect, but the random effects between subjects are correlated. We derive the general form of the score statistic for testing that the random effects have a variance equal to 0. We apply this result to both parametric and semi-parametric models. In both cases we show that under certain conditions the score statistic has an asymptotic normal distribution. We consider several applications of this theory, including overdispersion, heterogeneity between groups, spatial correlations and genetic linkage.
Multistage Sampling Designs and Estimating Equations
Authors: Whittemore Alice S.
Published: March 2003

In some applications it is cost efficient to sample data in two or more stages. In the first stage a simple random sample is drawn and then stratified according to some easily measured attribute. In each subsequent stage a random subset of previously selected units is sampled for more detailed and costly observation, with a unit’s sampling probability determined by its attributes as observed in the previous stages. This paper describes multistage sampling designs and estimating equations based on the resulting data. Maximum likelihood estimates (MLEs) and their asymptotic variances are given for designs using parametric models. Horvitz–Thompson estimates are introduced as alternatives to MLEs, their asymptotic distributions are derived and their strengths and weaknesses are evaluated. The designs and the estimates are illustrated with data on corn production.
Perfect Simulation of Conditionally Specified Models
Authors: Møller Jesper
Published: March 2003

We discuss how the ideas of producing perfect simulations based on coupling from the past for finite state space models naturally extend to multivariate distributions with infinite or uncountable state spaces such as auto-gamma, auto-Poisson and autonegative binomial models, using Gibbs sampling in combination with sandwiching methods originally introduced for perfect simulation of point processes.
Multivariate Bandwidth Selection for Local Linear Regression
Authors: Yang Lijian , Tschernig Rolf
Published: March 2003

The existence and properties of optimal bandwidths for multivariate local linear regression are established, using either a scalar bandwidth for all regressors or a diagonal bandwidth vector that has a different bandwidth for each regressor. Both involve functionals of the derivatives of the unknown multivariate regression function. Estimating these functionals is difficult primarily because they contain multivariate derivatives. In this paper, an estimator of the multivariate second derivative is obtained via local cubic regression with most cross-terms left out. This estimator has the optimal rate of convergence but is simpler and uses much less computing time than the full local estimator. Using this as a pilot estimator, we obtain plug-in formulae for the optimal bandwidth, both scalar and diagonal, for multivariate local linear regression. As a simpler alternative, we also provide rule-of-thumb bandwidth selectors. All these bandwidths have satisfactory performance in our simulation study.
A Class of Weighted Dependence Measures for Bivariate Failure Time Data
Authors: Fan Juanjuan , Prentice Ross L. , Hsu Li
Published: March 2003

This paper considers a class of summary measures of the dependence between a pair of failure time variables over a finite follow-up region. The class consists of measures that are weighted averages of local dependence measures, and includes the cross-ratio-measure and finite region version of Kendall's τ; recently proposed by the authors. Two new special cases are identified that can avoid the need to estimate the bivariate survivor function and that admit explicit variance estimators. Nonparametric estimators of such dependence measures are proposed and are shown to be consistent and asymptotically normal with variances that can be consistently estimated. Properties of selected estimators are evaluated in a simulation study, and the method is illustrated through an analysis of Australian Twin Study data.
Standard Errors for EM Estimation
Authors: Jamshidian Mortaza , Jennrich R. I.
Published: March 2003

The EM algorithm is a popular method for computing maximum likelihood estimates. One of its drawbacks is that it does not produce standard errors as a by-product. We consider obtaining standard errors by numerical differentiation. Two approaches are considered. The first differentiates the Fisher score vector to yield the Hessian of the log-likelihood. The second differentiates the EM operator and uses an identity that relates its derivative to the Hessian of the log-likelihood. The well-known SEM algorithm uses the second approach. We consider three additional algorithms: one that uses the first approach and two that use the second. We evaluate the complexity and precision of these three and the SEM in algorithm seven examples. The first is a single-parameter example used to give insight. The others are three examples in each of two areas of EM application: Poisson mixture models and the estimation of covariance from incomplete data. The examples show that there are algorithms that are much simpler and more accurate than the SEM algorithm. Hopefully their simplicity will increase the availability of standard error estimates in EM applications. It is shown that, as previously conjectured, a symmetry diagnostic can accurately estimate errors arising from numerical differentiation. Some issues related to the speed of the EM algorithm and algorithms that differentiate the EM operator are identified.
A Unified Approach to Regression Analysis Under Double-Sampling Designs
Authors: Chen Yi-Hau , Chen Hung
Published: March 2003

We propose a unified approach to the estimation of regression parameters under double-sampling designs, in which a primary sample consisting of data on the rough or proxy measures for the response and/or explanatory variables as well as a validation subsample consisting of data on the exact measurements are available. We assume that the validation sample is a simple random subsample from the primary sample. Our proposal utilizes a specific parametric model to extract the partial information contained in the primary sample. The resulting estimator is consistent even if such a model is misspecified, and it achieves higher asymptotic efficiency than the estimator based only on the validation data. Specific cases are discussed to illustrate the application of the estimator proposed.
The Peculiar Shrinkage Properties of Partial Least Squares Regression
Authors: Butler Neil A. , Denham Michael C.
Published: March 2003

Partial least squares regression has been widely adopted within some areas as a useful alternative to ordinary least squares regression in the manner of other shrinkage methods such as principal components regression and ridge regression. In this paper we examine the nature of this shrinkage and demonstrate that partial least squares regression exhibits some undesirable properties.
Almost Nonparametric Inference for Repeated Measures in Mixture Models
Authors: Hettmansperger T. P. , Thomas Hoben
Published: March 2003

We consider ways to estimate the mixing proportions in a finite mixture distribution or to estimate the number of components of the mixture distribution without making parametric assumptions about the component distributions. We require a vector of observations on each subject. This vector is mapped into a vector of 0s and 1s and summed. The resulting distribution of sums can be modelled as a mixture of binomials. We then work with the binomial mixture. The efficiency and robustness of this method are compared with the strategy of assuming multivariate normal mixtures when, typically, the true underlying mixture distribution is different. It is shown that in many cases the approach based on simple binomial mixtures is superior.
A Diagnostic for Selecting the Threshold in Extreme Value Analysis
Authors: Guillou Armelle , Hall Peter
Published: March 2003

A new approach is suggested for choosing the threshold when fitting the Hill estimator of a tail exponent to extreme value data. Our method is based on an easily computed diagnostic, which in turn is founded directly on the Hill estimator itself, ‘symmetrized’ to remove the effect of the tail exponent but designed to emphasize biases in estimates of that exponent. The attractions of the method are its accuracy, its simplicity and the generality with which it applies. This generality implies that the technique has somewhat different goals from more conventional approaches, which are designed to accommodate the minor component of a postulated two-component Pareto mixture. Our approach does not rely on the second component being Pareto distributed. Nevertheless, in the conventional setting it performs competitively with recently proposed methods, and in more general cases it achieves optimal rates of convergence. A by-product of our development is a very simple and practicable exponential approximation to the distribution of the Hill estimator under departures from the Pareto distribution.
Time Series Forecasting with Neural Networks: A Comparative Study Using the Air Line Data
Authors: Faraway Julian , Chatfield Chris
Published: March 2003

This case-study fits a variety of neural network (NN) models to the well-known air line data and compares the resulting forecasts with those obtained from the Box–Jenkins and Holt–Winters methods. Many potential problems in fitting NN models were revealed such as the possibility that the fitting routine may not converge or may converge to a local minimum. Moreover it was found that an NN model which fits well may give poor out-of-sample forecasts. Thus we think it is unwise to apply NN models blindly in ‘black box’ mode as has sometimes been suggested. Rather, the wise analyst needs to use traditional modelling skills to select a good NN model, e.g. to select appropriate lagged variables as the ‘inputs’. The Bayesian information criterion is preferred to Akaike’s information criterion for comparing different models. Methods of examining the response surface implied by an NN model are examined and compared with the results of alternative nonparametric procedures using generalized additive models and projection pursuit regression. The latter imposes less structure on the model and is arguably easier to understand.
Information on Sexual Behaviour when some Data are Missing
Authors: Raab G. M. , Donnelly C. A.
Published: March 2003

When tables contain missing values, statistical models that allow the non-response probability to be a function of the intended response have been proposed by several researchers. We investigate the properties of these methods in the context of a survey of the sexual behaviour of university students. Profile likelihoods can be computed, even when models are not identified and saturated profile likelihoods (making no assumptions about the non-response mechanism) are derived. Bayesian approaches are investigated and it is shown that their results may be highly sensitive to the prior specification. The proportion of responders answering ‘yes’ to the question ‘have you ever had sexual intercourse?’ was 73%. However, different assumptions about the non-responders gave proportions as low as 46% or as high as 83%. Our preferred estimate, derived from the response-saturated profile likelihood, is 67% with a 95% confidence interval of 58–74%. This is in line with other studies on response bias in the reports of young people’s sexual behaviour that suggest that the respondents overrepresent the sexually active.
Bayesian Sample Size Determination for Estimating Binomial Parameters from Data Subject to Misclassification
Authors: Rahme Elham , Joseph Lawrence , Gyorkos Theresa W.
Published: March 2003

We investigate the sample size problem when a binomial parameter is to be estimated, but some degree of misclassification is possible. The problem is especially challenging when the degree to which misclassification occurs is not exactly known. Motivated by a Canadian survey of the prevalence of toxoplasmosis infection in pregnant women, we examine the situation where it is desired that a marginal posterior credible interval for the prevalence of width w has coverage 1−α, using a Bayesian sample size criterion. The degree to which the misclassification probabilities are known a priori can have a very large effect on sample size requirements, and in some cases achieving a coverage of 1−α is impossible, even with an infinite sample size. Therefore, investigators must carefully evaluate the degree to which misclassification can occur when estimating sample size requirements.
Bayesian Analysis of Multivariate Mortality Data with Large Families
Authors: Chen Ming-Hui , Dey Dipak K. , Sinha Debajyoti
Published: March 2003

This paper presents a Bayesian method for the analysis of toxicological multivariate mortality data when the discrete mortality rate for each family of subjects at a given time depends on familial random effects and the toxicity level experienced by the family. Our aim is to model and analyse one set of such multivariate mortality data with large family sizes: the potassium thiocyanate (KSCN) tainted fish tank data of O’Hara Hines. The model used is based on a discretized hazard with additional time-varying familial random effects. A similar previous study (using sodium thiocyanate (NaSCN)) is used to construct a prior for the parameters in the current study. A simulation-based approach is used to compute posterior estimates of the model parameters and mortality rates and several other quantities of interest. Recent tools in Bayesian model diagnostics and variable subset selection have been incorporated to verify important modelling assumptions regarding the effects of time and heterogeneity among the families on the mortality rate. Further, Bayesian methods using predictive distributions are used for comparing several plausible models.
Regression Analysis of Health Care Charges with Heteroscedasticity
Authors: Zhou Xiao-Hua , Stroupe Kevin T. , Tierney William M.
Published: March 2003

We examine the effects of a prospective drug utilization review and patients’ characteristics on total in-patient and out-patient health care charges. Our analysis of charges is complicated by the fact that the total health care charges are skewed. A log-transformation of these charges can normalize their distribution but may not stabilize their variance. To handle these problems, we propose a linear regression model with a non-constant variance (heteroscedasticity). Using results from a fitted linear regression model for log-transformed charges, we also discuss interpreting the regression coefficients in the original scale and estimating the total health care charges to individual patients. Employing these methods, we analyse total health care charges for drug utilization review patients with hypertension and identify patients’ factors that are related to their total health care charges.
Bayesian Analysis of Competing Risks with Partially Masked Cause of Failure
Authors: Basu Sanjib , Sen Ananda , Banerjee Mousumi
Published: March 2003

Bayesian analysis of system failure data from engineering applications under a competing risks framework is considered when the cause of failure may not have been exactly identified but has only been narrowed down to a subset of all potential risks. In statistical literature, such data are termed masked failure data. In addition to masking, failure times could be right censored owing to the removal of prototypes at a prespecified time or could be interval censored in the case of periodically acquired readings. In this setting, a general Bayesian formulation is investigated that includes most commonly used parametric lifetime distributions and that is sufficiently flexible to handle complex forms of censoring. The methodology is illustrated in two engineering applications with a special focus on model comparison issues.
Asymptotic theory for certain regression models with long memory errors
Authors: Deo R. S.
Published: March 2003

The asymptotic distribution of a weighted linear combination of a linear long memory series is shown to be normal for certain weights. This result can be used to derive the limiting distribution of the least squares estimators for polynomial trends and of the periodogram at fixed Fourier frequencies. A closed form expression for the asymptotic relative bias of the tapered periodogram at fixed Fourier frequencies is also obtained. A weighted least squares estimator, which is asymptotically efficient for polynomial trend regressors, is shown to be asymptotically normal.
Time‐correlation analysis of nonstationary time series
Authors: Li Ta‐Hsin
Published: March 2003

This paper introduces a new graphical method of displaying the evolutionary correlation properties of nonstationary time series. The method, called time‐correlation analysis (TCA), summarizes the local correlation properties with a smooth and monotone characterization function interpretable as the first‐order autocorrelation coefficient. The TCA plot is constructed by plotting the discretized characterization function as time series. Issues concerning the range of the TCA plot are investigated in detail. Both simulated and real‐data examples are given to demonstrate the application and interpretation of the method.
Book Review
Authors: Turkman K. F.
Published: March 2003

Joe, H., Multivariate Models and Dependence Concepts
An Estimate of the Fractal Index Using Multiscale Aggregates
Authors: Poggi John‐Michel , Viano Marie‐Claude
Published: March 2003

We study a family of estimators of the fractal index of a Gaussian process based on the quadratic deviations at different aggregation scales. The estimators are convergent and asymptotically Gaussian when suitably normalized. Confidence intervals are provided. These asymptotic results hold for a large family of stationary‐increment models including fractional Brownian motions with square‐integrable spectral density. The estimates are applied to the analysis of an electrical signal
Tests for Deterministic Versus Indeterministic Cycles
Authors: Harvey Andrew , Streibel Mariane
Published: March 2003

An indeterministic cycle can be represented as an infinite moving‐average process and has a smooth peak in its spectrum. A deterministic cycle can be formulated in such a way that it is still stationary but its cyclical behaviour is characterized by a sudden jump in the spectral distribution function. This paper shows how both processes can be nested within the same model and how, when the cycle is buried in noise, the null hypothesis of a deterministic cycle can be tested. The preferred tests are based on the residuals from regressing the observations on sine and cosine functions of the frequency of interest. They are derived as point optimal and locally best invariant tests and can be extended to regression models which include other explanatory variables. The asymptotic distribution of the locally best invariant test, which is a one‐sided score test, is shown to be Cramer–von Mises with two degrees of freedom. Modifications for dealing with serially correlated noise are discussed and the methods are illustrated with real data.
Diagnostics for Time Series Analysis
Authors: Gerlach Richard , Carter Chris , Kohn Robert
Published: March 2003

Test statistics are proposed to determine the goodness of fit of a time series model. The test statistics are based on a sequence of random variables that are independent and standard normal if the model is correct. The paper shows how to compute this sequence of random variables efficiently using a combination of Markov chain Monte Carlo and importance sampling. The power of the statistics to detect outliers and level shifts is studied for an autoregressive model. The methodology is illustrated using both simulated and real data.
Valid Edgeworth Expansions of Some Estimators and Bootstrap Confidence Intervals in First‐order Autoregression
Authors: Kakizawa Yoshihide
Published: March 2003

We derive the third‐order valid Edgeworth expansions for the standardized and the Studentized versions of some estimators in first‐order autoregression without Gaussianity. As a special case of a Gaussian process, the validity of the expansion obtained by Ochi (Asymptotic expansions for the distribution of an estimator in the first‐order autoregressive process. Journal of Time Ser. Anal. 4 (1983), 57–67) is demonstrated. By applying the second‐order Edgeworth expansion to the bootstrap procedure, we construct the confidence intervals for the autoregressive coefficient.
A Note on Bootstrapping M‐Estimators in ARMA Models
Authors: Allen Michael , Datta Somnath
Published: March 2003

Kreiss and Franke (Bootstrapping stationary autoregressive moving‐average models. J. Time Ser. Anal.13 (1992), 297–317) proposed bootstrapping a linear approximation to the M‐estimator in autoregressive moving‐average (ARMA) models. In this paper, it is argued that it may be better to apply the bootstrap principle directly to the M‐estimator itself. A number of simulation results are presented to compare the two procedures for estimating the sampling distribution of an M‐estimator. The theoretical asymptotic validity of the standard bootstrap applied to the M‐estimator is established.
Polyvariograms and their Asymptotes
Authors: Chen Z. G. , Anderson O. D.
Published: March 2003

A definition of a polyvariogram (PV) γb(h)(h = 1, 2, ...) of order b (b≥ max(0, d− 1) is suggested for time series {Z(t})} satisfying {∇d(Z(t) = W(t) (where d is a non‐negative integer and {W(t)} is a second‐order stationary time series and is not over‐differenced). When b = 0, 1 and 2, this definition corresponds to Cressie's (J. Am. Stat. Assoc. 83 (1988), 1108–16; 85 (1990), 272) semivariogram linvariogram and quadvariogram respectively and is simpler. Under very general conditions about {W(t)}, we obtain the relationship between γb(h) and the autocovariance function of {W(t)} and show that the asymptote of γb(h) is a straight line having a positive slope when b = d− 1 and levelling out when b≥d.

A definition of a sample polyvariogram (SPV) of order b is given and is shown to be an unbiased and consistent estimate of the PV; and further, some uniformly (in h) almost sure convergence rates are obtained. These properties provide theoretical support for using the SPV to replace the practically unknown PV and generalize the guidelines for identifying d given by Cressie, where {W(t)} was restricted to a white noise and b≤ 2. Some further asymptotic theorems and avenues for using them for statistically testing d and parameters of models for {W(t)} are briefly introduced.
Data Driven Order Selection for Projection Estimator of the Spectral Density of Time Series with Long Range Dependence
Authors: Moulines Eric , Soulier Philippe
Published: March 2003

Fractional exponential (FEXP) models have been introduced by Robinson (1991) and Beran (1993) to model the spectral density of a covariance stationary long‐range dependent process. In this class of models, the spectral density f(x) of the process is decomposed as f(x) = |1 − exp(ix)|−2df*(x), where f*(x) accounts for the short‐memory component. In this contribution, FEXP models are used to construct semi‐parametric estimates of the fractional differencing coefficient and of the spectral density, by considering an infinite Fourier series expansion of log f*(x). A data‐driven order selection procedure, adapted from the Mallows' Cp procedure, is proposed to determine the order of truncation. The optimality of the data‐driven procedure is established, under mild assumptions on the short‐memory component f*(x). A limited Monte‐Carlo experiment is presented to support our claims.
Time Scale Estimation by Tracking Parameter Variation
Authors: Belcher John , Tunnicliffe Wilson Granville
Published: March 2003

A quasi‐periodic time series is sampled at a varying but unknown rate. An autoregressive moving‐average model is fitted to the resulting discrete series and the time variation of its parameters is estimated. The functional dependence of the parameters on the sampling rate is then used to estimate this rate and to reconstruct the true time scale.
Bayesian Unit Root Test in Nonnormal AR(1) Model
Authors: Hasegawa Hikaru , Chaturvedi Anoop , Van Hoa Tran
Published: March 2003

In this paper, we approximate the distribution of disturbances by the Edgeworth series distribution and propose a Bayesian analysis in a nonnormal AR(1) model. We derive the posterior distribution of the autocorrelation and the posterior odds ratio for unit roots hypothesis in the AR(1) model when the first four cumulants of the Edgeworth series distribution are finite and the higher order cumulants are negligible. We also apply the posterior analysis to eight real exchange rates and investigate whether these exchange rates behave like a random walk or not.
Prediction Variance and Information Worth of Observations in Time Series
Authors: Pourahmadi Mohsen , Soofi E. S.
Published: March 2003

The problem of developing measures of worth of observations in time series has not received much attention in the literature. Any meaningful measure of worth should naturally depend on the position of the observation as well as the objectives of the analysis, namely parameter estimation or prediction of future values. We introduce a measure that quantifies worth of a set of observations for the purpose of prediction of outcomes of stationary processes. The worth is measured as the change in the information content of the entire past due to exclusion or inclusion of a set of observations. The information content is quantified by the mutual information, which is the information theoretic measure of dependency. For Gaussian processes, the measure of worth turns out to be the relative change in the prediction error variance due to exclusion or inclusion of a set of observations. We provide formulae for computing predictive worth of a set of observations for Gaussian autoregressive moving‐average processs. For non‐Gaussian processes, however, a simple function of its entropy provides a lower bound for the variance of prediction error in the same manner that Fisher information provides a lower bound for the variance of an unbiased estimator via the Cramer‐Rao inequality. Statistical estimation of this lower bound requires estimation of the entropy of a stationary time series.
Robust Automatic Bandwidth for Long Memory
Authors: Henry Marc
Published: March 2003

The choice of bandwidth, or number of harmonic frequencies, is crucial to semiparametric estimation of long memory in a covariance stationary time series as it determines the rate of convergence of the estimate, and a suitable choice can insure robustness to some non‐standard error specifications, such as (possibly long‐memory) conditional heteroscedasticity. This paper considers mean squared error minimizing bandwidths proposed in the literature for the local Whittle, the averaged periodogram and the log periodogram estimates of long memory. Robustness of these optimal bandwidth formulae to conditional heteroscedasticity of general form in the errors is considered. Feasible approximations to the optimal bandwidths are assessed in an extensive Monte Carlo study that provides a good basis for comparison of the above‐mentioned estimates with automatic bandwidth selection.
On the Distributional Properties of GARCH Processes
Authors: Pawlak M. , Schmid W.
Published: March 2003

In this paper we study the distributional properties of the generalized autoregressive conditional heteroskedasticity (GARCH) model often being applied in economics. For a large class of non‐normal distributions of the noise process various inequalities on the distribution of the GARCH process are established. Moreover, these results are used to derive useful conclusions about the behavior of the average run length of a Shewhart control chart for time series.
A Bias Correction for Cross‐validation Bandwidth Selection when a Kernel Estimate is Based on Dependent Data
Authors: Skold Martin
Published: March 2003

Least‐squares cross‐validation (LSCV) bandwidth selection for kernel density estimation has been shown to underestimate the optimal bandwidth if data are positively correlated. We calculate the asymptotic bias for the LSCV criterion under a continuous‐time model and apply it as a correction term to discrete‐time data that can be modeled as a smooth continuous‐time process sampled at a high rate.
Maximum Likelihood Estimates of a Class of One‐Dimensional Stochastic Differential Equation Models From Discrete Data
Authors: Cleur Eugene M.
Published: March 2003

The problem of computing the maximum likelihood estimate of the parameters of a specific class of stochastic differential equation (SDE) models with linear drift whose sample paths are observed at discrete time points is considered. This estimate is obtained as in Cleur and Manfredi (1999) by discretizing the explicit expressions for the estimates which maximize the likelihood function in continuous time, by discretizing the likelihood function through a quadrature approximation before maximizing it, and by maximizing the likelihood function of the Euler scheme approximation to the underlying continuous process. Simulation results indicate that, for the constellation of parameter values considered, all three approaches lead to very similar results.
recursive Mean Adjustment for Unit Root Tests
Authors: Shin Dong Wan , So Beong Soo
Published: March 2003

For unit root tests, we propose a new mean adjustment scheme, called recursive mean adjustment. For adjusting the mean of an observation at a time t, instead of the global sample mean, we use the recursive sample mean which is the sample mean of the observations up to the time t. The approach is simple and can be applied to a wide class of unit root tests. The recursive mean adjustment gives us tests with substantially higher powers compared with the tests based on the ordinary mean adjustment.
A Note on Mean‐squared Prediction Errors of the Least Squares Predictors in Random Walk Models
Authors: Ing C. K.
Published: March 2003

An asymptotic expression for the mean‐squared prediction error (MSPE) of the least squares predictor is obtained in the random walk model. It is shown that the term of order 1/n in this error, where n is the sample size, is twice as large as the one obtained from the first‐order autoregressive (AR(1)) model satisfying the stationary assumption. Moreover, while the correlation between the squares of the (normalized) regressor variable and normalized least squares estimator is asymptotically negligible in the stationary AR(1) model, we have found that the correlation has significantly negative value in the random walk model. To obtain these results, a new methodology, which is found to be useful in dealing with the moment properties of a strongly dependent process, is introduced.
Robust estimates for arch processes
Authors: MULER NORA , YOHAI VICTOR J.
Published: March 2003

In this paper, we present two robust estimates for ARCH(p) models: τ ‐ and filtered τ‐estimates. These are defined by the minimization of conveniently robustified likelihood functions. The robustification is achieved by replacing the mean square error of the standardized observations with the square of a robust τ‐scale estimate in the reduced form of the Gaussian likelihood function. The robust filtering procedure avoids the propagation of the effect of one outlier on subsequent conditional variances. A Monte‐Carlo study shows that the maximum likelihood estimate practically collapses when there is only a small percentage of outlier contamination, while both robust estimates perform much better.
The Determinants of Blue CollarWage Rates: An Analysis of a DataSet Incorporating Observed Job Characteristics
Authors: Smith Michael , Masi Anthony , Lemay Paula
Published: March 2003

There is wide agreement that one of the major problems in the estimation of wage equations, and in testing theories of wages, is the quality of the data available. In particular, the data used in almost all studies involve indirect or unsatisfactory measures of a variety of relevant factors, including working conditions. In this paper we exploit an unusual data set that includes measures of characteristics of jobs generated by observation (rather than self report, or through inference from the Dictionary of Occupational Titles). With these data we estimate a wage equation in which compensating differentials play a significant role in wage determination and, once working conditions and other characteristics are effectively controlled, all but one measure of previous labour market experience had no effect on the wage rate.
Remitting Behaviour of “Temporary” and “Permanent” Migrants: The Case of Greeks in Germany and Australia
Authors: Glytsos Nicholas P.
Published: March 2003

This paper makes the point that migrants have different motives for sending remittances and remittance receivers have different claims on migrants' income, depending on whether people move to accumulate capital to improve their living at home after they return — temporary migration — or to start a new life in a foreign country — permanent migration. This hypothesis is empirically tested with data from Greek–German and Greek–Australian migration. The findings attest to the fact that German remittances constitute obligatory income streams to close family at home, while Australian remittances are gifts. Some quantitative estimates of the relative impact of individual factors on remittances are also obtained. Some hints are also given for a changing remitting behaviour of Greek migrants in Germany, along with the changing character of migration in that country.
Labour Force Transitions among Married Women in the USA
Authors: Francesconi Marco
Published: March 2003

This paper describes the patterns of labour market transitions for a cohort of married women in the USA drawn from the National Longitudinal Survey and observed between 1968 and 1991. The empirical analysis of labour market movements is motivated by human capital theory augmented by demographic and life‐cycle considerations. These movements are investigated by estimating competing risk models of labour force spell duration. The results show that the determinants of spell lengths and the determinants of the reasons for spell terminations vary across labour market states. More importantly, both the labour market state in which an individual is observed over her work cycle and the labour market state to which she moves are relevant in shaping her spell length and her tradeable characteristics in the labour market.
Employment Decisions of Married Women: Evidence and Explanations
Authors: Del Boca Daniela , Locatelli Marilena , Pasqua Silvia
Published: March 2003

Aggregate evidence has revealed a significant increase in women’s labour market participation (especially among married women) and a decline in male participation, both in Italy and in all the other OECD countries. This paper empirically tests the relationship between the education and employment status of husbands and wives using the Bank of Italy Survey (1995). The results of our analysis show that employed women are likely to be married to employed men with a higher level of education and higher income. The estimates of the labour supply decisions of wives show that the effect of the unemployment status of husbands is mediated by other factors associated with the family’s view of wives working outside home. The response to a husband’s unemployment depends significantly on the employment decisions of parents (mothers and mothers‐in‐law), a proxy for the couple’s attitude towards women’s work.