bluuebunny/crossref_metadata_2025 huggingface dataset:
Building Adaptive Estimating Equations When Inverse of Covariance Estimation is Difficult
Authors: Qu Annie , Lindsay Bruce G.
Published: March 2003

To construct an optimal estimating function by weighting a set of score functions, we must either know or estimate consistently the covariance matrix for the individual scores. In problems with high dimensional correlated data the estimated covariance matrix could be unreliable. The smallest eigenvalues of the covariance matrix will be the most important for weighting the estimating equations, but in high dimensions these will be poorly determined. Generalized estimating equations introduced the idea of a working correlation to minimize such problems. However, it can be difficult to specify the working correlation model correctly. We develop an adaptive estimating equation method which requires no working correlation assumptions. This methodology relies on finding a reliable approximation to the inverse of the variance matrix in the quasi-likelihood equations. We apply a multivariate generalization of the conjugate gradient method to find estimating equations that preserve the information well at fixed low dimensions. This approach is particularly useful when the estimator of the covariance matrix is singular or close to singular, or impossible to invert owing to its large size.
A Skew Extension of theT-Distribution, with Applications
Authors: Jones M. C. , Faddy M. J.
Published: March 2003

A tractable skew t-distribution on the real line is proposed. This includes as a special case the symmetric t-distribution, and otherwise provides skew extensions thereof. The distribution is potentially useful both for modelling data and in robustness studies. Properties of the new distribution are presented. Likelihood inference for the parameters of this skew t-distribution is developed. Application is made to two data modelling examples.
Estimating Variance Components by Using Survey Data
Authors: Korn Edward L. , Graubard Barry I.
Published: March 2003

Inflation-type weighted estimators for variance components can be badly biased. Modified weighted estimators suggested in the literature are also badly biased for certain sampling designs. We propose new estimators for variance components, some of which are approximately unbiased regardless of the sampling design. These estimators require knowledge of the joint inclusion probabilities of the observations. The small sample properties of the estimators are studied via simulation for the simple one-way random-effects model. An application is given by using data from the US Hispanic Health and Nutrition Examination Survey.
Efficiency of Projected Score Methods in Rectangular Array Asymptotics
Authors: Li Haihong , Lindsay Bruce G. , Waterman Richard P.
Published: March 2003

The paper considers a rectangular array asymptotic embedding for multistratum data sets, in which both the number of strata and the number of within-stratum replications increase, and at the same rate. It is shown that under this embedding the maximum likelihood estimator is consistent but not efficient owing to a non-zero mean in its asymptotic normal distribution. By using a projection operator on the score function, an adjusted maximum likelihood estimator can be obtained that is asymptotically unbiased and has a variance that attains the Cramér–Rao lower bound. The adjusted maximum likelihood estimator can be viewed as an approximation to the conditional maximum likelihood estimator.
A Simple Estimator for a Shared Frailty Regression Model
Authors: Fine Jason P. , Glidden David V. , Lee Kristine E.
Published: March 2003

We propose a simple estimation procedure for a proportional hazards frailty regression model for clustered survival data in which the dependence is generated by a positive stable distribution. Inferences for the frailty parameter can be obtained by using output from Cox regression analyses. The computational burden is substantially less than that of the other approaches to estimation. The large sample behaviour of the estimator is studied and simulations show that the approximations are appropriate for use with realistic sample sizes. The methods are motivated by studies of familial associations in the natural history of diseases. Their practical utility is illustrated with sib pair data from Beaver Dam, Wisconsin.
An Adaptive Estimation of Dimension Reduction Space
Authors: Xia Yingcun , Tong Howell , Li W. K. , Zhu Li-Xing
Published: March 2003

Searching for an effective dimension reduction space is an important problem in regression, especially for high dimensional data. We propose an adaptive approach based on semiparametric models, which we call the (conditional) minimum average variance estimation (MAVE) method, within quite a general setting. The MAVE method has the following advantages. Most existing methods must undersmooth the nonparametric link function estimator to achieve a faster rate of consistency for the estimator of the parameters (than for that of the nonparametric function). In contrast, a faster consistency rate can be achieved by the MAVE method even without undersmoothing the nonparametric link function estimator. The MAVE method is applicable to a wide range of models, with fewer restrictions on the distribution of the covariates, to the extent that even time series can be included. Because of the faster rate of consistency for the parameter estimators, it is possible for us to estimate the dimension of the space consistently. The relationship of the MAVE method with other methods is also investigated. In particular, a simple outer product gradient estimator is proposed as an initial estimator. In addition to theoretical results, we demonstrate the efficacy of the MAVE method for high dimensional data sets through simulation. Two real data sets are analysed by using the MAVE approach.
Efficient Construction of Reversible Jump Markov Chain Monte Carlo Proposal Distributions
Authors: Brooks S. P. , Giudici P. , Roberts G. O.
Published: March 2003

The major implementational problem for reversible jump Markov chain Monte Carlo methods is that there is commonly no natural way to choose jump proposals since there is no Euclidean structure in the parameter space to guide our choice. We consider mechanisms for guiding the choice of proposal. The first group of methods is based on an analysis of acceptance probabilities for jumps. Essentially, these methods involve a Taylor series expansion of the acceptance probability around certain canonical jumps and turn out to have close connections to Langevin algorithms. The second group of methods generalizes the reversible jump algorithm by using the so-called saturated space approach. These allow the chain to retain some degree of memory so that, when proposing to move from a smaller to a larger model, information is borrowed from the last time that the reverse move was performed. The main motivation for this paper is that, in complex problems, the probability that the Markov chain moves between such spaces may be prohibitively small, as the probability mass can be very thinly spread across the space. Therefore, finding reasonable jump proposals becomes extremely important. We illustrate the procedure by using several examples of reversible jump Markov chain Monte Carlo applications including the analysis of autoregressive time series, graphical Gaussian modelling and mixture modelling.
Markov Chain Monte Carlo Techniques for Studying Interoccasion and Intersubject Variability: Application to Pharmacokinetic Data
Authors: Lunn David J. , Aarons Leon J.
Published: March 2003

Values of pharmacokinetic parameters may seem to vary randomly between dosing occasions. An accurate explanation of the pharmacokinetic behaviour of a particular drug within a population therefore requires two major sources of variability to be accounted for, namely interoccasion variability and intersubject variability. A hierarchical model that recognizes these two sources of variation has been developed. Standard Bayesian techniques were applied to this statistical model, and a mathematical algorithm based on a Gibbs sampling strategy was derived. The accuracy of this algorithm’s determination of the interoccasion and intersubject variation in pharmacokinetic parameters was evaluated from various population analyses of several sets of simulated data. A comparison of results from these analyses with those obtained from parallel maximum likelihood analyses (NONMEM) showed that, for simple problems, the outputs from the two algorithms agreed well, whereas for more complex situations the NONMEM approach may be less accurate. Statistical analyses of a multioccasion data set of pharmacokinetic measurements on the drug metoprolol (the measurements being of concentrations of drug in blood plasma from human subjects) revealed substantial interoccasion variability for all structural model parameters. For some parameters, interoccasion variability appears to be the primary source of pharmacokinetic variation.
Bayesian Model Averaging in Proportional Hazard Models: Assessing the Risk of a Stroke
Authors: Volinsky Chris T. , Madigan David , Raftery Adrian E. , Kronmal Richard A.
Published: March 2003

In the context of the Cardiovascular Health Study, a comprehensive investigation into the risk factors for strokes, we apply Bayesian model averaging to the selection of variables in Cox proportional hazard models. We use an extension of the leaps-and-bounds algorithm for locating the models that are to be averaged over and make available S-PLUS software to implement the methods. Bayesian model averaging provides a posterior probability that each variable belongs in the model, a more directly interpretable measure of variable importance than a P-value. P-values from models preferred by stepwise methods tend to overstate the evidence for the predictive value of a variable and do not account for model uncertainty. We introduce the partial predictive score to evaluate predictive performance. For the Cardiovascular Health Study, Bayesian model averaging predictively outperforms standard model selection and does a better job of assessing who is at high risk for a stroke.
Venezuelan Rainfall Data Analysed by Using a Bayesian Space–time Model
Authors: Sansó Bruno , Guenni Lelys
Published: March 2003

We consider a set of data from 80 stations in the Venezuelan state of Guárico consisting of accumulated monthly rainfall in a time span of 16 years. The problem of modelling rainfall accumulated over fixed periods of time and recorded at meteorological stations at different sites is studied by using a model based on the assumption that the data follow a truncated and transformed multivariate normal distribution. The spatial correlation is modelled by using an exponentially decreasing correlation function and an interpolating surface for the means. Missing data and dry periods are handled within a Markov chain Monte Carlo framework using latent variables. We estimate the amount of rainfall as well as the probability of a dry period by using the predictive density of the data. We considered a model based on a full second-degree polynomial over the spatial co-ordinates as well as the first two Fourier harmonics to describe the variability during the year. Predictive inferences on the data show very realistic results, capturing the typical rainfall variability in time and space for that region. Important extensions of the model are also discussed.
Modelling Extreme Wind Speeds in Regions Prone to Hurricanes
Authors: Walshaw David
Published: March 2003

Extreme wind speeds can arise as the result of a simple pressure differential, or a complex dynamic system such as a tropical storm. When sets of record values comprise a mixture of two or more different types of event, the standard models for extremes based on a single limiting distribution are not applicable. We develop a mixture model for extreme winds arising from two distinct processes. Working with sequences of annual maximum speeds obtained at hurricane prone locations in the USA, we take a Bayesian approach to inference, which allows the incorporation of prior information obtained from other sites. We model the extremal behaviour for the contrasting wind climates of Boston and Key West, and show that the standard models can give misleading results at such locations.
Simultaneous Optimization of Mechanical Properties of Steel by Maximizing Exponential Desirability Functions
Authors: Kim Kwang-Jae , Lin Dennis K. J.
Published: March 2003

A modelling approach to optimize a multiresponse system is presented. The approach aims to identify the setting of the input variables to maximize the degree of overall satisfaction with respect to all the responses. An exponential desirability functional form is suggested to simplify the desirability function assessment process. The approach proposed does not require any assumptions regarding the form or degree of the estimated response models and is robust to the potential dependences between response variables. It also takes into consideration the difference in the predictive ability as well as relative priority among the response variables. Properties of the approach are revealed via two real examples—one classical example taken from the literature and another that the authors have encountered in the steel industry.
Non-Linear State Space Modelling of Fisheries Biomass Dynamics by Using Metropolis-Hastings within-Gibbs Sampling
Authors: Millar Russell B. , Meyer Renate
Published: March 2003

State space modelling and Bayesian analysis are both active areas of applied research in fisheries stock assessment. Combining these two methodologies facilitates the fitting of state space models that may be non-linear and have non-normal errors, and hence it is particularly useful for modelling fisheries dynamics. Here, this approach is demonstrated by fitting a non-linear surplus production model to data on South Atlantic albacore tuna (Thunnus alalunga). The state space approach allows for random variability in both the data (the measurement of relative biomass) and in annual biomass dynamics of the tuna stock. Sampling from the joint posterior distribution of the unobservables was achieved by using Metropolis-Hastings within-Gibbs sampling.
Non-Homogeneous Markov Models in the Analysis of Survival After Breast Cancer
Authors: Pérez-Ocón Rafael , Ruiz-Castro Juan Eloy , Gámiz-Pérez M. Luz
Published: March 2003

A cohort of 300 women with breast cancer who were submitted for surgery is analysed by using a non-homogeneous Markov process. Three states are onsidered: no relapse, relapse and death. As relapse times change over time, we have extended previous approaches for a time homogeneous model to a non omogeneous multistate process. The trends of the hazard rate functions of transitions between states increase and then decrease, showing that a changepoint can be considered. Piecewise Weibull distributions are introduced as transition intensity functions. Covariates corresponding to treatments are incorporated in the model multiplicatively via these functions. The likelihood function is built for a general model with k changepoints and applied to the data set, the parameters are estimated and life-table and transition probabilities for treatments in different periods of time are given. The survival probability functions for different treatments are plotted and compared with the corresponding function for the homogeneous model. The survival functions for the various cohorts submitted for treatment are fitted to the mpirical survival functions.
Non-Linear Autoregressive Time Series with Multivariate Gaussian Mixtures as Marginal Distributions
Authors: Glasbey C. A.
Published: March 2003

A new form of non-linear autoregressive time series is proposed to model solar radiation data, by specifying joint marginal distributions at low lags to be multivariate Gaussian mixtures. The model is also a type of multiprocess dynamic linear model, but with the advantage that the likelihood has a closed form.
Bayesian Cure Rate Models for Malignant Melanoma: A Case-Study of Eastern Cooperative Oncology Group Trial E1690
Authors: Chen Ming-Hui , Harrington David P. , Ibrahim Joseph G.
Published: March 2003

We propose several Bayesian models for modelling time-to-event data. We consider a piecewise exponential model, a fully parametric cure rate model and a semiparametric cure rate model. For each model, we derive the likelihood function and examine some of its properties for carrying out Bayesian inference with non-informative priors. We also examine model identifiability issues and give conditions which guarantee identifiability. Also, for each model, we construct a class of informative prior distributions based on historical data, i.e. data from similar previous studies. These priors, called power priors, prove to be quite useful in this context. We examine the properties of the power priors for Bayesian inference and, in particular, we study their effect on the current analysis. Tools for model comparison and model assessment are also proposed. A detailed case-study of a recently completed melanoma clinical trial conducted by the Eastern Cooperative Oncology Group is presented and the methodology proposed is demonstrated in detail.
Maximum Likelihood Estimation of Bivariate Logistic Models for Incomplete Responses with Indicators of Ignorable and Non-Ignorable Missingness
Authors: Horton Nicholas J. , Fitzmaurice Garrett M.
Published: March 2003

Missing observations are a common problem that complicate the analysis of clustered data. In the Connecticut child surveys of childhood psychopathology, it was possible to identify reasons why outcomes were not observed. Of note, some of these causes of missingness may be assumed to be ignorable, whereas others may be non-ignorable. We consider logistic regression models for incomplete bivariate binary outcomes and propose mixture models that permit estimation assuming that there are two distinct types of missingness mechanisms: one that is ignorable; the other non-ignorable. A feature of the mixture modelling approach is that additional analyses to assess the sensitivity to assumptions about the missingness are relatively straightforward to incorporate. The methods were developed for analysing data from the Connecticut child surveys, where there are missing informant reports of child psychopathology and different reasons for missingness can be distinguished.
Modelling Daily Multivariate Pollutant Data at Multiple Sites
Authors: Shaddick Gavin , Wakefield Jon
Published: March 2003

This paper considers the spatiotemporal modelling of four pollutants measured daily at eight monitoring sites in London over a 4-year period. Such multiple-pollutant data sets measured over time at multiple sites within a region of interest are typical. Here, the modelling was carried out to provide the exposure for a study investigating the health effects of air pollution. Alternative objectives include the design problem of the positioning of a new monitoring site, or for regulatory purposes to determine whether environmental standards are being met. In general, analyses are hampered by missing data due, for example, to a particular pollutant not being measured at a site, a monitor being inactive by design (e.g. a 6-day monitoring schedule) or because of an unreliable or faulty monitor. Data of this type are modelled here within a dynamic linear modelling framework, in which the dependences across time, space and pollutants are exploited. Throughout the approach is Bayesian, with implementation via Markov chain Monte Carlo sampling.
Dynamic Generalized Linear Models with Application to Environmental Epidemiology
Authors: Chiogna Monica , Gaetan Carlo , Gaetan Carlo
Published: March 2003

We propose modelling short-term pollutant exposure effects on health by using dynamic generalized linear models. The time series of count data are modelled by a Poisson distribution having mean driven by a latent Markov process; estimation is performed by the extended Kalman filter and smoother. This modelling strategy allows us to take into account possible overdispersion and time-varying effects of the covariates. These ideas are illustrated by reanalysing data on the relationship between daily non-accidental deaths and air pollution in the city of Birmingham, Alabama.
Generalized Local Influence with Applications to Fish Stock Cohort Analysis
Authors: Cadigan N. G. , Farrell P. J.
Published: March 2003

It is important to understand the influence of data and model assumptions on the results of a statistical analysis, and influence diagnostics are valuable tools for this. We consider local influence diagnostics for a statistical model that is fully parametric, and where estimation involves a fit function that is second order differentiable with respect to the parameters. Similarly to Cook, we study the local behaviour of influence graphs formed from perturbations to model components. However, the diagnostics that we develop are more general in that the type of model result that is used to assess influence is fairly arbitrary and must only be first order differentiable with respect to model parameters and the perturbations. This allows us to focus our influence analyses on important results, and to produce diagnostics that are meaningful to practitioners. The procedures that we propose are applied to sequential population analysis, a common method that is used to estimate the size of commercially exploited fish stocks. Our diagnostics reveal interesting patterns of influence that are not revealed by using Cook's likelihood displacement influence diagnostics. Our diagnostics lead to an increased understanding of how the data affect important estimates, and thereby provide information for assessing the potential effect of errors in model inputs. In addition, empirical comparisons illustrate that the local influence diagnostics proposed tend to provide a good description of global influence.
Minimum Distance Estimation of the Distribution Functions of Stochastically Ordered Random Variables
Authors: Gangnon Ronald E. , King William N.
Published: March 2003

Stochastic ordering of distributions can be a natural and minimal restriction in an estimation problem. Such restrictions occur naturally in several settings in medical research. The standard estimator in such settings is the nonparametric maximum likelihood estimator (NPMLE). The NPMLE is known to be biased, and, even when the empirical cumulative distribution functions nearly satisfy the stochastic orderings, the NPMLE and the empirical cumulative distribution functions may differ substantially. In many settings, this can make the NPMLE seem to be an unappealing estimator. As an alternative to the NPMLE, we propose a minimum distance estimator of distribution functions subject to stochastic ordering constraints. Consistency of the minimum distance estimator is proved, and superior performance is demonstrated through a simulation study. We demonstrate the use of the methodology to assess the reproducibility of gradings of nuclear sclerosis from fundus photographs.
Bayesian Inference for Time Series with Stable Innovations
Authors: Qiou Zuqiang , Ravishanker Nalini
Published: March 2003

This paper describes Bayesian inference for a linear time series model with stable innovations. An advantage of the Bayesian approach is that it enables the simultaneous estimation of the parameters characterizing the stable law and the parameters of the linear autoregressive moving‐average model. Our approach uses a Metropolis–Hastings algorithm to generate samples from the joint posterior distribution of all the parameters and subsequent inference is based on these samples. We illustrate our approach using data simulated from three linear processes with stable innovations and a real data set
Existence and Stochastic Structure of a Non‐negative Integer‐valued Autoregressive Process
Authors: Latour Alain
Published: March 2003

A simple criterion is given for the existence of a generalized integer‐valued autoregressive (GINAR(p)) process. We show that the GINAR(p) process is nothing but an AR(p) process. The spectral density gives a good insight into the stochastic structure of a GINAR(p) model. The spectral representation of the process is explicitly given. The estimation of parameters of the process is also discussed and clarifies some results presented by Du and Li (The integer‐valued autoregressive (INAR(p)) model. J. Times Ser. Anal., 12 (1991), 129‐‐42). Finally, we describe the number of seizures of an epileptic patient using a model of this class.
Gaussian Semiparametric Estimation of Non‐stationary Time Series
Authors: Velasco Carlos
Published: March 2003

Generalizing the definition of the memory parameter d in terms of the differentiated series, we showed in Velasco (Non‐stationary log‐periodogram regression, Forthcoming J. Economet., 1997) that it is possible to estimate consistently the memory of non‐stationary processes using methods designed for stationary long‐range‐dependent time series. In this paper we consider the Gaussian semiparametric estimate analysed by Robinson (Gaussian semiparametric estimation of long range dependence. Ann. Stat. 23 (1995), 1630–61) for stationary processes. Without a priori knowledge about the possible non‐stationarity of the observed process, we obtain that this estimate is consistent for d∈ (−½, 1) and asymptotically normal for d∈ (−½,¾) under a similar set of assumptions to those in Robinson's paper. Tapering the observations, we can estimate any degree of non‐stationarity, even in the presence of deterministic polynomial trends of time. The semiparametric efficiency of this estimate for stationary sequences also extends to the non‐stationary framework.
Maximum likelihood estimation in space time bilinear models
Authors: DAI YUQING , BILLARD L.
Published: March 2003

The space time bilinear (STBL) model is a special form of a multiple bilinear time series that can be used to model time series which exhibit bilinear behaviour on a spatial neighbourhood structure. The STBL model and its identification have been proposed and discussed byDai and Billard (1998). The present work considers the problem of parameter estimation for the STBL model. A conditional maximum likelihood estimation procedure is provided through the use of a Newton–Raphson numerical optimization algorithm. The gradient vector and Hessian matrix are derived together with recursive equations for computation implementation. The methodology is illustrated with two simulated data sets, and one real‐life data set.
Testing for serial dependence in time series models of counts
Authors: Jung Robert C. , Tremayne A. R.
Published: March 2003

Abstract. In analysing time series of counts, the need to test for the presence of a dependence structure routinely arises. Suitable tests for this purpose are considered in this paper. Their size and power properties are evaluated under various alternatives taken from the class of INARMA processes. We find that all the tests considered except one are robust against extra binomial variation in the data and that tests based on the sample autocorrelations and the sample partial autocorrelations can help to distinguish between integer‐valued first‐order and second‐order autoregressive as well as first‐order moving average processes.
Technological Innovation and Employment Reallocation
Authors: Greenan Nathalie , Guellec Dominique
Published: March 2003

The paper describes the dynamics of employment at a firm and sector level in French industry and examines how far technological innovation can give account of it. We use a sample of 15,186 firms, over the period 1986–90. The two facts we want to explain at a firm and sector level are the net change in employment and the micro turmoil (transfers between competing firms). Innovating firms and sectors create jobs more than others over the medium run (5 years). Process innovation is more about job creation than product innovation at the firm level, but the converse is true at the sector level. This paradox is probably due to substitution effects (creative destruction).
The Gender Earnings Gap for US Physicians: Has Equality been Achieved?
Authors: Bashaw David J. , Heywood John S.
Published: March 2003

Recent work examining a constructed hourly wage measure indicates gender earnings equality among US physicians. Yet, physicians are not typically paid by the hour and the constructed measure fails to capture the true influence of hours worked and presents a fallacy of composition. When the earnings specification replaces the constructed wage measure with annual earnings or is modified to properly include hours worked, earnings equality vanishes and large gender differentials reappear.
Innovation and Sectoral Employment: A Trade–off between Compensation Mechanisms
Authors: Ebersberger Bernd , Pyka Andreas
Published: March 2003

The question whether technological progress displaces employment or whether technological advance is beneficial for the level of employment has been at the core of economic debate for over two centuries. The beneficial effect might be achieved by several compensation mechanisms within the economic system. In this paper we categorize these compensation mechanisms into two basic categories that reflect the different nature of the ideas ruling the compensation. We discriminate the mechanisms of employment despite innovation from employment via innovation. In the context of new innovation economics we model an artificial industry implementing both compensation mechanisms. Simulation analysis is used to examine both the short–run and long–run properties of the model. There we focus on the influence of wage restraint policy on the functioning of the compensation mechanism.
Recovering from the Effects of Domestic Violence: Implications for Welfare Reform Policy
Authors: Murphy Patricia A.
Published: March 2003

This article highlights the importance of understanding how the impact of domestic violence renders participation in welfare‐to‐work programs highly problematic since the symptoms of post‐traumatic stress disorder (a not uncommon outcome of domestic violence experience) can undermine the victim’s best efforts to benefit from training and return to work programs. A short history of PTSD is provided with the cautionary note that definitions of PTSD remain in flux. Sub‐categories of PTSD, such as Battered Women’s Syndrome, are also reviewed. Recovery from PTSD is not described in psychotherapeutic terms, but in rehabilitative terms with an emphasis on those recovery processes which facilitate restoration of psychological and functional capacities, leading to participation in welfare‐to‐work or retraining programs with labor market participation as the goal.
Paying Workers or Paying Lawyers: Employee Termination Practices in the United States and Canada
Authors: Nielsen Laura
Published: March 2003

This article explores one multinational corporation’s employee termination practices in the United States and Canada. There are fairly insignificant differences in employees’ legal protections in the two countries and the company claims a uniform corporate employee termination process cross‐nationally. However, there are major structural and procedural differences in the employee termination process. The differences, including the way attorneys are utilized, the use of quasi‐legal personnel to comply with regulatory requirements, and the substance of the severance package are explored. In the United States money is directed toward legal professionals –“paying lawyers” while in Canada expenses associated with employee termination go to severance packages –“paying workers.”
Comparing Japanese and American Industrial Effluent Control: A Case Study of the Consequences of Contrasting Regulatory Styles
Authors: Aoki Kazumasu
Published: March 2003

Based on the experiences of a multinational corporation with contrasting effluent control in the United States and Japan, this article suggests that American regulations are more costly to comply with, but do not necessarily sustain superior effectiveness to the Japanese counterparts. The informal character of Japanese regulations and cooperative interactions between industry and government appear to encourage the firm to implement measures that overcomply with the current laws, and may reduce the marginal cost of pollution control. On the other hand, the greater legalism and contentious process associated with American regulations dampen the firm’s incentives to overcomply with permits requirements and to adopt certain environmental management practices that the firm has employed in its Japanese factories.
Formal and Informal Aspects of Labor Dispute Resolution in Japan
Authors: Koshiro Kazutoshi
Published: March 2003

Labor conflict resolution machineries have concentrated on collective dispute settlement in Japan. Grievance procedures stipulated in labor contracts have not been working effectively. The center of gravity of labor‐management relations has shifted from collective bargaining with labor unions to individual labor contracts with individual workers both in the unorganized sector and the non‐union members within the organized sector. Similarities and differences between Japan and the United States are examined. Vague demarcation between rights and interest disputes as well as between collective and individual disputes characterize the dispute settlement machineries in Japan.
Privately Financed Capital in Public Services
Authors: Heald David
Published: March 2003

Recent changes in U.K. policy, notably the retirement of the 1981 Ryrie rules, presage a substantial increase in the use of private finance for public sector projects. The most important features of the relaxations of 1989 and 1992 relate to successive modifications of the value‐for‐money test, notably in connection with removing the requirement for a systematic comparison with a hypothetical publicly financed project (e.g. when the private sector can be directly remunerated by user tolls); less stringent rules on leasing; and allowing private borrowing on the security of Exchequer‐funded assets. The crucial issues are identified to be the extent of private finance and the implications for macroeconomic indicators; whether the hypothesized operational efficiency gains are sufficient to offset higher financing costs; whether risk is genuinely transferred to the private sector; and whether risk ought to be transferred to the private sector.
The Cambridge Economics Tripos 1903–55 and the Training of Economists
Authors: Tribe Keith
Published: March 2003

The performance of students during the first 50 years of the Cambridge Economics Tripos is examined. Students are grouped according to the Parts completed, and an assessment is made of the relative performance. A general comparison of college performance is also made. Students of Part I only are shown to achieve poor results; students taking both Parts do not consistently perform better than those taking only Part II. The results are placed in the context of Marshall’s intention that the Tripos should be a three‐year training in economics, and also in the context of Harry Johnson’s critique of Cambridge economics, which the results generally support.
A Post Deregulation Analysis of the Sources of Productivity Growth in UK Building Societies
Authors: Glass J. Colin , McKillop Donal G.
Published: March 2003

In this study we employ a distance function approach to investigate sources of productivity growth in UK building societies in the post‐deregulation period 1989–93. Productivity growth is decomposed into technical change and change in efficiency, with the latter change also being decomposed into change in pure technical efficiency, change in scale efficiency and change in input congestion. As a scale‐inefficient society may be able to obtain size efficiency gains even when the attainment of scale efficiency is impractical, we also measure the change in size efficiency over the period. The finding of substantial productivity growth was largely due to progressive shifts in technology, with the relatively small improvements in efficiency being largely due to improvements in scale efficiency. A marked increase in the attainment of size efficiency over the period was also found.
Estimates of Scale and Scope Economies in the New Zealand Life Insurance Industry
Authors: Khaled M. , Adams M. B. , Pickford M.
Published: March 2003

Recent multi‐product statistical studies of production in life insurance industries in North America and the UK have produced mixed findings with regard to economies of scale and scope. This study presents results for the life insurance industry in New Zealand, which has a small economy where the industry is comparatively unregulated. A total of 135 pooled observations for the population of non‐bank life insurance companies for the period 1988–92 are used in a two‐input two‐output generalized translog model. Product‐specific economies of scale are measured in a variety of ways including two new ones, using what we have called ‘quasi average incremental cost’ and ‘partial average cost’. We find that while small companies could benefit from economies of scale, the optimum firm size is modest, and that medium‐ and large‐sized companies experience approximately constant returns to scale. In contrast, small‐ and medium‐sized companies experience diseconomies of scope, while the large‐sized companies have neither economies nor diseconomies of scope.
Integration, Foreign Direct Investment and Labour Markets: Microeconomic Perspectives
Authors: Gaston Noel , Nelson Douglas
Published: March 2003

Foreign direct investment (FDI) has grown far more rapidly than trade during the last two decades. As with the other prominent features of globalization, FDI is controversial. The impact of FDI on labour markets has been of growing concern, in particular for source countries. The deterioration of labour market conditions for unskilled workers in many OECD countries during the 1980s and 1990s was a primary catalyst for the concern. With regard to its impact on labour markets, FDI may have effects that, at least in the short and medium run, may well dwarf the effects of trade and immigration. In this paper, we review the economic theories and econometric evidence which purport to explain various aspects of the impact of FDI on labour markets. The emphasis is on two partial equilibrium models which, respectively, focus on the location decisions of multinationals and the impact of global firms on collective bargaining outcomes.
The Information Content of the Gilt–equity Yield Ratio
Authors: Levin Eric J. , Wright Robert E.
Published: March 2003

In principle the gilt–equity yield ratio (GEYR) can be used as a decision criterion for choosing between equity and bonds because it is sensitive to mispricing. However, the GEYR is also influenced by other variables. Consequently, observed movement in the GEYR cannot be confidently attributed to mispricing without controlling for the other variables which might cause the GEYR to vary. This paper demonstrates that the GEYR can be used as a signal to switch profitably between equity and bonds provided that the GEYR threshold value indicating a profitable switch is adjusted to incorporate changes in variables such as expected inflation and the equity risk premium. Simple empirical experiments are carried out aimed at evaluating how well the GEYR performs as a trading rule.
Local Vega Index and Variance Reduction Methods
Authors: Bermin Hans‐Peter , Kohatsu‐Higa Arturo , Montero Miquel
Published: March 2003

In this article we discuss a generalization of the Greek called vega which is used to study the stability of option prices and hedging portfolios with respect to the volatility in various models. We call this generalization the local vega index. We compute through Monte Carlo simulations this index in the cases of Asian options under the classical Black‐Scholes setup. Simulation methods using Malliavin calculus and kernel density estimation are compared. Variance reduction methods are discussed.
Controlling Risk Exposure and Dividends Payout Schemes:Insurance Company Example
Authors: Jgaard Bjarne Hø , Taksar Michael
Published: March 2003

The paper represents a model for financial valuation of a firm which has control of the dividend payment stream and its risk as well as potential profit by choosing different business activities among those available to it. This model extends the classical Miller–Modigliani theory of firm valuation to the situation of controllable business activities in a stochastic environment. We associate the value of the company with the expected present value of the net dividend distributions (under the optimal policy).

The example we consider is a large corporation, such as an insurance company, whose liquid assets in the absence of control fluctuate as a Brownian motion with a constant positive drift and a constant diffusion coefficient. We interpret the diffusion coefficient as risk exposure, and drift is understood as potential profit. At each moment of time there is an option to reduce risk exposure while simultaneously reducing the potential profit—for example, by using proportional reinsurance with another carrier for an insurance company. Management of a company controls the dividends paid out to the shareholders, and the objective is to find a policy that maximizes the expected total discounted dividends paid out until the time of bankruptcy. Two cases are considered: one in which the rate of dividend payout is bounded by some positive constant M, and one in which there is no restriction on the rate of dividend payout.

We use recently developed techniques of mathematical finance to obtain an easy understandable closed form solution. We show that there are two levels u0 and u1 with u0≤u1. As a function of currently available reserve, the risk exposure monotonically increases on (0,u0) from 0 to the maximum possible. When the reserve exceeds u1 the dividends are paid at the maximal rate in the first case and in the second case every excess above u1 is distributed as dividend. We also show that for M small enough u0=u1 and the optimal risk exposure is always less than the maximal.
On the optimal portfolio for the exponential utility maximization: remarks to the six‐author paper
Authors: Kabanov Yuri M. , Stricker Christophe
Published: March 2003

This note contains ramifications of results of Delbaen et al. (2002). Assuming that the price process is locally bounded and admits an equivalent local martingale measure with finite entropy, we show, without further assumption, that in the case of exponential utility the optimal portfolio process is a martingale with respect to each local martingale measure with finite entropy. Moreover, the optimal value always can be attained on a sequence of uniformly bounded portfolios.
Passport to Duke
Authors: Bourdieu Pierre
Published: March 2003

Editor’s Introduction The following text was prepared by Pierre Bourdieu for delivery at a conference on his work held at Duke University, April 21–23, 1995. Entitled “Pierre Bourdieu: Fieldwork in Culture,” the conference was sponsored by the Duke Graduate Program in Literature and included such well‐known literary scholars as Barbara Herrnstein Smith, Jonathan Culler, and Fredric Jameson. Bourdieu, of course, was the invited guest of honor, but was uncertain as to whether he should make the effort of attending, particularly since he was recovering from a short period of poor health. As I too had been invited (and seemed more familiar with the American scene), Bourdieu discussed the question with me in Paris.

He was rather concerned about wrongheaded, trendy applications of his theories by American literary scholars, who often misunderstand his work because they simply do not know the intellectual landscape to which it relates. Reading such conference paper titles as “Cross‐Dressing for Success: The Scramble for Symbolic Power in Tabitha Sweeney’s Female Quixotism,” Bourdieu confessed his fear of being taken as simply the French intellectual flavor of the month, one whose theory is used simply as grist for the American academy’s industrious mills of literary interpretation.

He ultimately decided to send the following text to be read at the conference in his absence. It treats, with polite frankness, his worries about being misinterpreted through importation into the American theoretical field with its peculiar conception of French philosophy; Bourdieu’s paper situates these particular worries within a more general account of “allodoxic”distortions caused by the international travel of theory; but it also tries to prevent further misunderstanding by offering a brief contextualization of his theory and a brief summary of his method of analysis through fields. The translation of Bourdieu’s text was prepared by Loïc Wacquant, and is presented here with only minor adjustments.
Does Philosophy of Action Rest on a Mistake?
Authors: Sneddon Andrew
Published: March 2003

Philosophers of action tend to take for granted the concept of basic actions – actions that are done at will, or directly – as opposed to others that are performed in other ways. This concept does foundational work in action theory; many theorists, especially causalists, take part of their task to be showing that normal, complex actions necessarily stem from basic ones somehow. The case for the concept of basic actions is driven by a family of observations and a cluster of closely related anti‐infinite regress arguments. I review this case in the work of Arthur Danto, Donald Davidson, and Jennifer Hornsby – three of the most important developers of the concept – and find it lacking. I conclude by sketching the possibility of non‐foundationalist action theory.
Philosophy of Mind, Cognitive Science, andPedagogical Technique
Authors: Croy Marvin
Published: March 2003

Changes in the aims and methods of the philosophy of mind have occurred in recent decades. In particular, computer simulations have emerged as a means of constructing empirically and conceptually defensible theories of mind. This article explores pedagogical innovations that may be necessitated by these changes. One question raised is whether hands‐on teaching of simulation methods should be a standard part of philosophy of mind courses. These courses, because of an increasing empirical orientation, are becoming more interdisciplinary, and certain consequences of this development are elaborated here.
Building Simple Mechanical MindsUsing LEGO® Robots for Research and Teaching in Philosophy
Authors: Sullins John P.
Published: March 2003

In this essay I discuss how I built a cognitive‐robotics lab using inexpensive LEGO® MINDSTORMS™ robot kits. The lab has provided pedagogical and research opportunities for a number of philosophy courses, and I briefly describe the results of those efforts. I also describe how one might build a similar lab. Philosophers need to be more directly involved in the field of robotics. There is much work to do in tidying up the philosophical debris left by the last wave of robotics research, which the researchers have been unwilling or unable to do themselves. The major barriers to participation have been hardware costs and a lack of technical expertise among most philosophers. In recent years, though, these barriers have become less of a problem. At the end, I present some ideas about the kinds of experiments that such kits make possible and how to expand beyond the limitations of the basic LEGO® hardware.