bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dutch employee relations: supervisors’ and subordinates’ perspectives
Authors: DeRidder Richard 
Published: November 2023

Abstract
The present chapter focuses on relations between employees in The Netherlands. We report a study on subordinates’ and supervisors’ reactions to norm violations or rule breaking behaviour. As spelled out in Chapter 1, reactions to norm violations are expected to be determined by a number of context factors, such as supervisors’ and subordinates’ identification with their own group, their attitudes towards one another and existent differences in power. The strength of these factors, in turn, is the result of a historically grown social-economic, ideological, and political relationship between supervisors and sub-ordinates. Therefore, before reporting the actual study, we will provide a brief overview of the Dutch employment situation.
Neurophysiological mechanisms underlying face processing within and beyond the temporal cortical visual areas
Authors: Rolls Edmund T 
Published: November 2023

Abstract
The ways in which information about faces is represented and stored in the temporal lobe visual areas of primates, as shown by recordings from single neurons in macaques, are considered. Some neurons that respond primarily to faces are found in the cortex in the anterior part of the superior temporal sulcus (in which neurons are especially likely to be tuned to facial expression and to face movement involved in gesture), and in the TE areas more ventrally forming the inferior temporal gyms (in which neurons are more likely to have responses related to the identity of faces). Quantitative studies of the responses of the neurons that respond differently to the faces of different individuals show that information about the identity of the individual is represented by the responses of a population of neurons, that is, ensemble encoding rather than ‘grandmother cell’ encoding is used.
Who Knows what Alternative Lurks in the Hearts of Significance Tests?
Authors: Hodges Jim 
Published: November 2023

Abstract
Bayesians have a long tradition of reviling significance tests as inherently un Bayesian, going back at least to the first edition of Jeffreys’ Theory of Probability. But Hodges (1990) showed that to an arbitrarily close approximation, the P-value is the posterior probability of the null hypothesis for a particular alternative, in a large class of cases to be detailed below. One could say that the alternative behind significance tests has been unmasked and can now be used as a club to beat on P-values. On the other hand, it now appears that P-values are not as un Bayesian as we have been led to think. In view of the tradition of P-value bashing, it is of some interest to examine the new alternative hypothesis to see whether we must admit P-values to the Bayesian family after all.
Recent Extensions to the EM Algorithm
Authors: Meng Xiao-Li , Rubin Donald B 
Published: November 2023

Abstract
Two recent extensions to the EM algorithm have made it even more powerful and applicable in practice. Both extensions follow the theme of EM by using repeated complete-data computations to handle incomplete data. The Supplemented EM or SEM algorithm (Meng and Rubin, 1991a) enables users of EM to calculate the incomplete-data asymptotic variance-covariance matrix associated with the maximum likelihood estimate obtained by EM, using only the computer code for EM and for the complete-data asymptotic variance-covariance matrix. The Expectation/Conditional Maximization or ECM algorithm (Meng and Rubin, 1991b) replaces the M-step of EM with a set of conditional maximization (CM) steps, and thus can eliminate undesirable nested iterations with EM when its M-step is not in closed form. SEM and ECM can also be combined into a Supplemented ECM (SECM) algorithm for computing the incomplete-data asymptotic variance-covariance matrix for maximum likelihood estimates found by ECM. In this paper, we provide a general description of these new techniques, as well as brief discussions of their relevance in Bayesian computations and their relationships with the popular Gibbs sampler (Geman and Geman, 1984), and its extension, GIBS.
Learning in Probabilistic Expert Systems
Authors: Spiegelhalter David J , Cowell Robert G 
Published: November 2023

Abstract
Probabilistic expert systems use a directed graphical structure to express conditional independence relationships, and conditional probability tables to summarise quantitative knowledge. We explore the consequences of assuming these probabilities to be parameters, where beliefs about those parameters are updated as data accumulate. A simple approximate Bayesian procedure is shown to be related to those used in ‘unsupervised learning’ and is investigated by simulations and applied to a difficult real example. The procedure has reasonable properties, although for certain missing data configurations the approximations used are clearly somewhat extreme, and further work is required to handle induced dependencies between parameters.
The Use of Expert Judgement in the Context of a Postulated Mathematical Model
Authors: French S , Cooke R M , Vogt F 
Published: November 2023

Abstract
We consider the expert problem in which an analyst consults one or more experts for their views about uncertain quantities required in a decision or risk analysis. We extend the expert problem to the case in which the uncertain quantities are related in the analyst’s mind by a physical model. 1be problem is first formulated as a Bayesian hierarchical model. Because of many computational difficulties, we explore a more pragmatic approach for circumstances in which the experts’ judgements are brought together through a weighted linear opinion pool. Details are worked through for the case where the physical model is a simple normal regression model and the classically weighted linear opinion pool is used. We also note that the approach may be developed to allow the physical model to be drawn from the GLIM family. Unfortunately, applications of expert judgement seem to encounter physical models which are far from linear and these methods may still not be tractable in some important practical cases. For these we indicate a further approach, which may be more computationally feasible.
Expected Logarithmic Divergence for Exponential Families
Authors: Gutierrez-Peña Eduardo 
Published: November 2023

Abstract
Kullback-Leibler logarithmic divergence (Kullback-Leibler information) may be used within a Bayesian framework to produce a class of utility functions in statistical decision problems concerning parametric families of distributions. In such a case it is necessary to compute the expected value of the logarithmic divergence (ELD). We show that the ELD takes a simple form when the distribution of the sampling process belongs to a regular exponential family and the natural parameter has a distribution in the corresponding conjugate family. An application is outlined.
Patterns of marriage in the Isle of Harris
Authors: Clegg E J 
Published: November 2023

Abstract
It is well recognized that mate selection in marriage does not occur at random (see van den Berghe 1979). Many factors may influence it, for example: the degree of relatedness (O’Brien et al. 1979); the high correlation between spouses’ ages (Hajnal 1963; Leslie 1983a,b); the decline in mating frequency with increasing separation between spouse’s residences (Boyce et al. 1967; Boyce 1984; Swedlund 1988); and the existence of assortative mating for various overt biological or cultural characteristics (Rao et al. 1979). In addition there may be a secular factor in a given population, resulting from changes in residence and social class distribution, improvements in communications, etc. (Ktichemann et al. 1967).
Living with schistosomes: adaptation, accommodation, or severe ill-health?
Authors: Parker M 
Published: November 2023

Abstract
More than 200 million people world-wide live with schistosomes. Academics and public health professionals generally equate schistosomal infection with ill-health and, until recently, it has been sufficient to cite epidemiological data documenting the prevalence of infection to show that schistosomiasis presents a major public health problem in the tropical and subtropical world. This chapter draws attention to our limited understanding of the relationship between infection, intensity of infection, and ill-health and raises the following questions: are there any data to suggest that people adapt or accommodate to life with schisto-somes? Is it appropriate for governments and international agencies to continue to allocate scarce resources to control schistosomiasis in the absence of information documenting severe ill-health for the majority of people harbouring schistosomes? This chapter is divided into four parts. Part one discusses the extent to which Schistosoma mansoni affects reproductive success and part two explores the relationship between S. mansoni and some of the parasitological, pathological, and clinical indicators of morbidity. The third part of the chapter examines the relationship between schistosomes and daily activities and the fourth and final part discusses the implications of this large body of work for public health policy. The chapter focuses on S. mansoni, but the questions raised are applicable to other species of schistosome, especially S. haematobium, S. intercalatum and, to a lesser extent, S. japonicum.
Nutrition Intervention
Authors: Geissler Ca Therine 
Published: November 2023

Abstract
The term nutrition intervention is often used in the narrow definition of feeding and education programmes. However, a broader definition covers any programme or policy that has a positive impact on nutritional status. It is the broader definition that is considered in this chapter. For an intervention to improve nutrition it must either treat existing malnutrition or prevent it by altering factors that are underlying causes.
On using crime statistics for prediction
Authors: Copas John 
Published: November 2023

Abstract
The uses of statistics on crime can perhaps be grouped into three somewhat overlapping categories - description, administration, and research. The first is the most important: rational discussion of crime needs to be informed by accurate statistics on the present state of the criminal justice system and how it is evolving over time. In administration, statistics are needed for the allocation of resources, for decision-making, and in consideration of issues of policy. In research we aim to understand, and to pose (if not answer) ‘what if questions. The overlaps are obvious: good administration needs both description and the results of research on policy changes and evaluation. Some applications of statistics involve prediction, uses covering both administration (decision-making) and research (policy evaluation). A classic example of the former is decision-making in parole, where it can be useful to summarize background information on a prisoner in the form of a prediction score for the risk that the subject will reoffend. An example of the latter is when a prediction score is used to stratify subjects into risk groups for the purpose of monitoring some policy development. It is the use of statistics in the construction and validation of prediction scores which forms the subject of this chapter.
The Intrinsic Bayes Factor for Linear Models¹
Authors: Berger J O , Pericchi L R 
Published: November 2023

Abstract
In Berger and Pericchi (1993) a general automatic Bayesian method for comparing models, the Intrinsic Bayes Factor (IBP) was proposed. One version, the Arithmetic IBF, was shown to essentially correspond to an actual Bayes factor for a reasonable Intrinsic Prior. A second version, the Geometric IBF,is justified in Pericchi and Smith (1994), using a prequential type of loss function, without assuming that one of the entertained models is the true sampling model. Here we analyze the general normal linear model, determining the intrinsic Bayes factors for any model comparisons, nested or separate, as well as for multiple model comparisons. In these situations we also calculate the Expected Arithmetic IBF. We also generalize model elaboration ideas to linear models with fixed mean structure but arbitrary error distributions. The method is illustrated on examples and compared with other model selection methods.
Testing for Mixtures: A Bayesian Entropic Approach
Authors: Mengersen* K L , Robert** C P 
Published: November 2023

Abstract
The determination of the number of components in a mixture of parametrised distributions has important bearing in homogeneity testing, cluster analysis and mixture modelling, among other applications. In this paper, we focus on a two-component gaussian model and a test for the presence of a mixture. The proposed test is based on an indifference zone representation of the null hypothesis defined by the Kullback-Leibler distance, and a derivation of the posterior probability that this distance is less than a given threshold using the Gibbs sampler. Due to a new parametrisation of the mixture model, estimation performances are markedly improved, while the Bayesian prior input is reduced to the specification of a single scale parameter. Moreover, the determination of this scale parameter is equivalent to the choice of the above threshold. We illustrate our approach with a Monte Carlo study.
Computation on Bayesian Graphical Models
Authors: Spiegelhalter D J , Thomas A , Best N G 
Published: November 2023

Abstract
We describe the graphical expression of conditional independence assumptions and associated factorisations of the joint distribution of both observed and unobserved quantities. Such factorisations encourage local computational techniques, and a language and program for Gibbs sampling is discussed with examples concerning measurement error and spatial smoothing of cancer rates.
Bayesian Analysis of Longitudinal Data Studies
Authors: Gamerman D , Smith A F M 
Published: November 2023

Abstract
The problem of inferring and predicting a population time trend from repeated measurements on individuals from the population is considered. Different modelling strategies incorporating the dynamic hierarchical approach are proposed. These include the use of mixtures to combine information at individual and population levels. Applications are presented and specific topics such as prediction, different follow-up intervals and design considerations are discussed.
An Introduction to Local Spectral Theory
Authors: Laursen Kjeld B , Neumann Michael M 
Published: November 2023

Abstract
Modern local spectral theory is built on the classical spectral theorem, a fundamental result in single-operator theory and Hilbert spaces. This book provides an in-depth introduction to the natural expansion of this fascinating topic of Banach space operator theory, whose pioneers include Dunford, Bishop, Foias, and others. Assuming only modest prerequisites of its readership, it gives complete coverage of the field, including the fundamental recent work by Albrecht and Eschmeier which provides the full duality theory for Banach space operators. It is highlighted by many characterizations of decomposable operators, and of other related, important classes of operators, as well as an in-depth study of their spectral properties, including identifications of distinguished parts, and results on permanence properties of spectra with respect to several types of similarity. Also found is a thorough and quite elementary treatment of the modern single- operator duality theory; this theory has many applications, both to general issues of classification and to such celebrated problems as the invariant subspace problems. A long chapter - almost a book in itself - is devoted to the use of local spectral theory in the study of spectral properties of multipliers and convolution operators. Another one describes its connections to automatic continuity theory. Written in a careful and detailed style, it contains numerous examples, many simplified proofs of classical results, and extensive references. It concludes with a list of interesting open problems, suitable for continued research.
Decomposable operators
Authors: Laursen Kjeld B , Neumann Michael M 
Published: November 2023

Abstract
The purpose of this chapter is to provide a streamlined introduction to the theory of decomposable operators. We assume that the reader is familiar with the elementary spectral theory of bounded linear operators on Banach spaces and the theory of normal operators on Hilbert spaces, as presented, for instance, in Conway (1990) or Rudin (1991). However, we do not presuppose any knowledge of spectral operators and local spectral theory.
Applied Smoothing Techniques for Data Analysis
Authors: Bowman Adrian W , Azzalini Adelchi 
Published: November 2023

Abstract
The book describes the use of smoothing techniques in statistics, including both density estimation and nonparametric regression. Considerable advances in research in this area have been made in recent years. The aim of this text is to describe a variety of ways in which these methods can be applied to practical problems in statistics. The role of smoothing techniques in exploring data graphically is emphasised, but the use of nonparametric curves in drawing conclusions from data, as an extension of more standard parametric models, is also a major focus of the book. Examples are drawn from a wide range of applications. The book is intended for those who seek an introduction to the area, with an emphasis on applications rather than on detailed theory. It is therefore expected that the book will benefit those attending courses at an advanced undergraduate, or postgraduate, level, as well as researchers, both from statistics and from other disciplines, who wish to learn about and apply these techniques in practical data analysis. The text makes extensive reference to S-Plus, as a computing environment in which examples can be explored. S-Plus functions and example scripts are provided to implement many of the techniques described. These parts are, however, clearly separate from the main body of text, and can therefore easily be skipped by readers not interested in S-Plus.
On the approximation of queueing networks in heavy traffic
Authors: Williams R J 
Published: November 2023

Abstract
This paper reports on the current state of research concerning approximations to the queue length or workload processes in stable but heavily loaded queueing networks. Most of the approximations that have been considered to date are justified by heavy traffic limit theorems that have a certain conventional form, namely (i) the queue length or workload processes in a sequence of heavily loaded networks are normalized by a central limit theorem type of scaling, and (ii) the weak limit of these normalized processes is a reflecting Brownian motion. Such conventional limit theorems for both open and closed networks are surveyed first, with a view to outlining their general form. Whilst there is a reasonable collection of limit theorems for single class networks and some multiclass networks (especially those with feedforward structure), recent developments have indicated that not all multiclass networks with feedback have conventional heavy traffic behavior. These developments are surveyed here and in particular, the Harrison-Williams example of an unconventional heavy traffic limit theorem for a closed analog of the Lu-Kumar/Rybko-Stolyar priority network is described.
Notes on effective bandwidths
Authors: Kelly Frank 
Published: November 2023

Abstract
This paper presents a personal view of work to date on effective bandwidths, emphasizing the unifying role of the concept: as a summary of the statistical characteristics of sources over different time and space scales; in bounds, limits and approximations for various models of multiplexing under quality of service constraints; and as the basis for simple and robust tariffing and connection acceptance control mechanisms for poorly characterized traffic. The framework assumes only stationarity of sources, and illustrative examples include periodic streams, fractional Brownian input, policed and shaped sources, and deterministic multiplexing.
Statistical Modelling in GLIM 4
Authors: Aitkin Murray , Francis Brain , Hinde John 
Published: November 2023

Abstract
This new edition of the successful multi-disciplinary text Statistical Modelling in GLIM takes into account new developments in both statistical software and statistical modelling. Including three new chapters on mixture and random effects models, it provides a comprehensive treatment of the theory of statistical modelling with generalised linear models with an emphasis on applications to practical problems and an expanded discussion of statistical theory. A wide range of case studies is also provided, using the normal, binomial, Poisson, multinomial, gamma, exponential and Weibull distributions. This book is ideal for graduates and research students in applied statistics and a wide range of quantitative disciplines, including biology, medicine and the social sciences. Professional statisticians at all levels will also find it an invaluable desktop companion.
Neural Networks for Unsupervised Learning Based on Information Theory
Authors: Kay Jim 
Published: November 2023

Abstract
We consider the statistical and information-theoretic basis of a class of artificial neural networks for unsupervised learning and processing. Taking a single processing unit as the basic component of the networks, we define information-theoretic objective functions and a class of activation functions, and derive the gradient-ascent learning rules. Several versions of the processor are considered, namely, the case in which the processor has a single binary output, the case in which the outputs are multinomial-winner-take-all, and that in which they are multivariate binary. In this latter case, local versions of the objective function are developed and these lead to local learning rules. Finally, the issue of computational complexity is briefly discussed, an alternative approach to the modelling is developed, and approximations are mentioned.
Cognitive slowing among older adults: what kind and how much?
Authors: Fisher Donald L , Duffy Susan A , Katsikopoulos Konstantinos V 
Published: November 2023

Abstract
Older adults generally perform more poorly than younger adults on a variety of different measures of performance. These measures include the large range of reaction time tasks so prevalent today (e.g. Sternberg 1969), as well as the many different tests of memory, reasoning and spatial abilities first administered back in the 1940s (e.g. Hebb 1942; Cattell 1943). The argument has been made that much of the decline in performance among older adults can be traced to a single factor, cognitive slowing. Birren (e.g. 1965, 1974) was the first to propose this general line of argument, an argument which has since been vigorously pursued by a number of different investigators (e.g. Cerella 1985; Salthouse 1980, 1985). Although cognitive slowing is not the only factor that has been put forward as an explanation for the differences between younger and older adults, it has remained the dominant one and is the one upon which we will focus (for a review of the alternative potential mediators, see Salthouse 1991).