bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nonparametric estimation for quantities of interest in queues
Authors: Pitts Susan M 
Published: November 2023

Abstract
Various techniques from nonparametric statistics may be applied to stochastic models. The theoretical results are summarized here, and their application is illustrated with reference to the GI /GI /1 queue. Suppose that a particular queueing situation is modelled by an M(λ.)/M(μ)/1 queue. A common procedure in practice is to derive estimators for the parameters λ and μ from observations of the arrival and service times. Depending on the purpose of the investigation, a possible next step is statistical inference for such quantities as the traffic intensity or the mean stationary waiting time.
The asymptotic behaviour of large loss networks
Authors: Zachary Stan 
Published: November 2023

Abstract
We study the limit behaviour of controlled loss networks as capacity and offered traffic are allowed to increase in proportion, reviewing and extending recent work based on the functional law of large numbers of Hunt and Kurtz. We consider in detail single and two-resource networks. In this paper we study large loss networks in which the offered traffic is subject to acceptance controls. We review recent work of Hunt and Kurtz (1994), who established rigorous results for the asymptotic dynamics of such networks as capacity and offered traffic are allowed to increase in proportion, and we relate these results to asymptotic equilibrium behaviour. We further study the detailed behaviour of networks with at most two resources, extending results of Bean et al. (1994b, 1995), and giving some additional results.
Introducing GLIM4
Authors: Aitkin Murray , Francis Brain , Hinde John 
Published: November 2023

Abstract
Statistical modelling is an iterative process. We have a research question of interest, the experimental or observational context, the sampling scheme and the data. Based on these data and the other information, we develop an initial model, fit it to the data and examine the model output. We then plot the residuals, check the model assumptions and examine the parameter estimates. Based on this model output, we may modify the first model possibly through some transformation or the addition or subtraction of model terms. Fitting the second model may in turn lead to further models for the data, with the form of the current model being based on the information provided by the previous models. This interactivitybetween the data and the data modeller is a crucial feature of the process of statistical modelling. While automatic model selection procedures provided in many software packages can provide guidance in large problems, we take a different route in this book.
Multinomial and Poisson response data
Authors: Aitkin Murray , Francis Brain , Hinde John 
Published: November 2023

Abstract
The file faults gives the number n of faults in 32 rolls of material of length l metres; the data come from Bissell (1972), and are reproduced in Table 5.1. The number of faults is a non-negative integer, and is naturally modelled by the Poissondistribution, the standard model for count data.
Survival data
Authors: Aitkin Murray , Francis Brain , Hinde John 
Published: November 2023

Abstract
Over the last 30 years there has been a rapid development of probability models and statistical analysis for technological and medical survival data. Many studies have been made of the length of life or of periods of remission of animal or human subjects being treated for serious diseases. These studies have used probability models for duration of life which originated in engineering reliability studies of the operating lifetimes of electrical and mechanical systems. We will use the terms “death” and “failure” interchangeably to represent the events and “survival time” to represent the times to these events. In this chapter we give an account of the main probability models and their use in data analysis. The recognition of the special form of the likelihood in an important class of these distributions allows their simple fitting as generalized linear models. Many books are now available on survival analysis; a concise account can be found in Cox and Oakes (1984), detailed discussions in Kalbfleisch and Prentice (1980) and Lawless (1982), and more applied treatments in Collett (2003) and Hosmer and Lemeshow (1999).
Finite mixture models
Authors: Aitkin Murray , Francis Brain , Hinde John 
Published: November 2023

Abstract
The exponential family of distributions and its extensions discussed in earlier chapters are extremely useful in statistical analysis, but they cannot represent all types of data of scientific interest. In this chapter we consider a general extension of the exponential family to mixturesof distributions from this family. The need for this extension will become clear in subsequent chapters in which we discuss random effect models and their fitting by ML.
Random effect models
Authors: Aitkin Murray , Francis Brain , Hinde John 
Published: November 2023

Abstract
In Chapter 5 we found overdispersion in the fabric fault data; the Poisson GLM did not fit or represent the data adequately. The failure of a GLM to fit may be due to several causes. The distribution of Ymay not be the specified exponential family member, or the regression model fitted may be mis-specified. A fruitful way of expressing the problem, which unifies these two possibilities, is through omitted variablesfrom the regression model: the full model we have fitted is missing one or more important variables. If these have been recorded then they can be added to the model and fitted in the usual way (e.g. interactions). The common reality however, especially in survey or observational studies, is that we do not know what variables shouldhave been recorded or measured to construct an adequate model.
Radial Basis Function Networks and Statistics
Authors: Lowe David 
Published: November 2023

Abstract
The class of neural network architectures known as radial basis functions has a structure and history which has produced strong links with various areas in the statistical sciences. This chapter reviews some of the more salient aspects, which include the links with conditional and unconditional density estimation (such as Gaussian mixture models), regression problems and links with kernel estimators and linear smooths, supervised feature extraction and discriminant analysis, and topographic feature extraction and links to multidimensional scaling, Sammon mappings and nonlinear principal component analysis.
Robust Prediction in Many-parameter Models
Authors: Intrator Nathan 
Published: November 2023

Abstract
When parameter estimation is based on a set of training patterns with size that is of the order of the number of free parameters, estimation may become very unreliable. Until recently, estimation in such cases had sounded unrealistic, but it has now been accepted that such estimation is possible under certain conditions. For example, linear discriminant analysis (Fisher, 1936) requires some adjustment when the input dimensionality is large (Buckheit and Donoho, 1995) to account for the added variability of the covariance matrices. In simple terms, innovative ways to reduce the variance portion of the error are required, as well as methods to impose (reasonable) bias.
Density Networks
Authors: MacKay David J C , Gibbs Mark N 
Published: November 2023

Abstract
A density network is a neural network that maps from unobserved inputs to observable outputs. The inputs are treated as latent variables so that, for given network parameters, a nontrivial probability density is defined over the output variables. This probabilistic model can be trained by various Monte Carlo methods. The model can discover a description of the observed data in terms of an underlying latent variable space of lower dimensionality. We review results of the application of these models to toy problems with categorical and real-valued observables and to protein data.
Analysis of Latent Structure Models with Multidimensional Latent Variables
Authors: Dunmur A P , Titterington D M 
Published: November 2023

Abstract
Within the total interface between statistics and neural computing, we shall concentrate in this chapter on the particular area of latent structure models, which has attracted considerable recent interest in the neural-computation literature. We shall review this activity and attempt to relate it to the statistical literature. There are new slants, especially in the form of new models and new computational ideas. The plan of the chapter is as follows. In Section 1 we outline the general framework of latent structure models, following this in Section 2 with statements of the structures associated with latent trait, latent profile and latent class models, with particular emphasis on the case of multidimensional latent variables. Section 3 discusses maximum likelihood estimation of the parameters in the models, using the EM algorithm, and identifies a problem of computational complexity in the E-step. Section 4 describes how so-called mean-field approximations have been developed in response to this difficulty. Bayesian approaches are described in Section 5, implemented mainly using versions of the Gibbs sampler.
The secret of the intermittent fevers
Authors: Mazzarello Paolo 
Published: November 2023

Abstract
Golgi’s experimental work on intestinal parasites and the transmission of anthrax in the early 1880s was not a momentary fancy after many years of almost exclusive focus on the nervous system. Indeed, Golgi’s long-lasting interest in the study of infectious diseases had been overshadowed only by his passion for neurohistology. The electrifying discoveries made in microbiology at the beginning of the 1880s, however, would be instrumental in transforming what had been only a latent interest into the main focus of his research activity.
The prophets of the neuron
Authors: Mazzarello Paolo 
Published: November 2023

Abstract
By the mid-1880s the black reaction, although discovered more than ten years earlier, was still known to only a small number of researchers. Only a few years later, in the early 1890s, the most important neurobiologists were hailing it as a breakthrough. It was an irony of fate that a crucial role in this dramatic change was played by a young Spanish scientist who would later become Golgi’s most formidable scientific opponent.
Golgi versus Cajal: holism versus reductionism at the dawn of the neurosciences
Authors: Mazzarello Paolo 
Published: November 2023

Abstract
In 1891, while Golgi was making a last attempt to convince his critics of the reality of the diffuse neural net, Cajal and van Gehuchten developed a physiological model in which dendrites, cell body, and axon played well-defined roles in the mechanism of nervous transmission. In contrast to the static model propounded by His Sr and Forel, Cajal was in fact propounding a more dynamic vision of the nerve cell, and van Gehuchten moved in the same direction, albeit with some small differences. We have seen that Cajal began to hint at the idea of directional flow of nerve currents in 1889 while investigating the relationship between the basket cells and Purkinje cells. Two years later, this vague intuition became the ‘law of dynamic polarization’, according to which the dendrites (and the cell body as well) receive nerve impulses, which are then transmitted to the cylindraxis (the conduction apparatus), which in turn distributes them to the terminal varicose arborization (emission apparatus).
Siamese twins joined at the shoulder
Authors: Mazzarello Paolo 
Published: November 2023

Abstract
After these telegrams, Golgi sent more formal letters of thanks to both Holmgren and Morner. In the letter to Holmgren, he also expressed regret that the news was by now widely known, ‘though I’m not responsible for this. I believe that this occurred because [as soon as] the telegram was received by the Post Office it quickly made the rounds of a small town like Pavia, and from here to all of Italy.’
Back to research
Authors: Mazzarello Paolo 
Published: November 2023

Abstract
He used the prestige associated with the Nobel Prize to further his efforts for the renovation of the University and to ward off the Milanese foe. Meanwhile, in July 1905 the Parliament had approved a law that allowed the creation in Milan of Clinical Institutes for Specialist Studies, which it endowed with a modest State contribution. In December a non-profit organization was created to manage the Clinics of Obstetrics and Gynaecology, of Occupational Diseases, and of Infectious Diseases. The courses would be taught by instructors equivalent in status to university professors. The Clinic of Obstetrics and Gynaecology was inaugurated in 1906 while the Clinic for Occupational Diseases was in advanced stages of construction.
The representation of space in the primate hippocampus, and its role in memory
Authors: Rolls Edmund T 
Published: November 2023

Abstract
The aims of this chapter are to consider how space is represented in the primate hippocampus, how this is related to the memory functions performed by the hippocampus, and how the hippocampus performs these functions. In addition to the evidence that is available from anatomical connections, the effects of lesions to the system, and recordings of the activity of single neurones in the system, neuronal network models of hippocampal function will also be dis¬ cussed, as they have the promise of enabling one to understand what and how the hippocampus computes, and thus to understand the functions being performed by the hippocampus. The neurophysiological and brain lesion studies described have been performed (unless stated otherwise) with macaque monkeys in order to provide information as relevant as possible to understanding memory systems and amnesia in humans.
Hippocampal and parietal contribution to topokinetic and topographic memory
Authors: Berthoz Alain 
Published: November 2023

Abstract
The purpose of this paper is to address the question of a particular aspect of spatial memory: the memory of self-produced movement in a spatial environment. The mathematician H. Poincare wrote: ‘ To localise a point in space is to imagine the movement necessary to reach it’ (Poincare 1970, p. 67). By this statement he proposed a dynamic theory of spatial memory very different from the current theories, which suppose that, because there are ‘ place cells’ in the hippocampus, or because spatial coordinates seem to be represented in the parietal cortex, the brain codes space on static ‘ topographical maps’ (O’ Keefe & Nadel 1978; Anderson 1995). Indeed, most studies concerning spatial memory have considered visual representational memory in static object recognition and recall processes (Smith & Milner 1981; Mishkin et al. 1983). In addition, most studies of the neural basis of spatial memory have been based on types of mental exploration of the environment that adopt a ‘ survey’ type of strategy, although dissociations between route and topographical memory have been found (Incisa della Rocchetta et al. 1996). In the field of visual exploration, alternative hypotheses have been proposed (Droulez & Berthoz 1990, 1991; Colby et al. 1995), which promote the idea that the brain is using some dynamic processes to reprsent space and to control either executed or imagined movements.
Sensory gain control (amplification) as a mechanism of selective attention: electrophysiological and neuroimaging evidence
Authors: Hillyard Steven A , Vogel Edward K , Luck Steven J 
Published: November 2023

Abstract
Both physiological and behavioral studies have suggested that stimulus-driven neural activity in the sensory pathways can be modulated in amplitude during selective attention. Recordings of event-related brain potentials indicate that such sensory gain control or amplification processes play an important role in visual-spatial attention. Combined event-related brain potential and neuroimaging experiments provide strong evidence that attentional gain control operates at an early stage of visual processing in extrastriate cortical areas. These data support early selection theories of attention and provide a basis for distinguishing between separate mechanisms of attentional suppression (of unattended inputs) and attentional facilitation (of attended inputs).
Microglia in the Developing and Mature Central Nervous System
Authors: Perry V H 
Published: November 2023

Abstract
The term microglia is used to describe cells with a distinct morphology and phenotype that lie within the parenchyma of the central nervous system (CNS) (Figure 4.1). These cells, described and named by de! Rio Hortega (1932), are the resident macrophages of the CNS parenchyma. Although there has been considerable debate as to whether these cells were derived from monocytes or not, there is now overwhelming evidence to support the idea that they are of monocytic origin (for references, see Ling, 1981; Ling and Wong, 1993). There is no convincing evidence for a population of cells within the CNS, of neuroectodermal or other non-monocytic origin, with the morphology and phenotype of those illustrated in Figure 4.1.
Concepts in Myelin Sheath Evolution
Authors: Colman D R , Pedraza L , Yoshida M 
Published: November 2023

Abstract
What were the evolutionary pressures that led to the recent (in evolutionary terms) invention of the morphologically complex, biochemically unique and functionally essential vertebrate myelin sheath? It is frustrating that a definitive answer to this question may remain elusive. There is a single major reason for this unsatisfying situation. The specialized proteins and membrane domains which form the myelin sheath must have evolved gradually, and the myelin protein genes themselves heterochronically in the first jawed vertebrates. Semi-myelinated vertebrate organisms must have once existed, but none exist in our time; the myelin sheath is either fully developed in both the central nervous system (CNS) and peripheral nervous system (PNS) as it is in the vast majority of vertebrates, or it is absent as in the jawless fish (Bullock et al., 1984). We can only guess then, about the original features of the first myelin-like ensheathment, that we believe appeared only about 440 million years ago. As molecular biological investigations are expanded to include extensive phylogenetic analyses of myelin-related genes, and these data are compared with significant landmarks in vertebrate evolution gleaned from the paleontological record, we may arrive at probable and perhaps compelling arguments about myelin sheath origins. There have been several interesting viewpoints on how we might reconcile in general terms the fossil record with molecular studies, and why the two lines of investigation are interdependent (see, for example, Graur, 1993; Eeernisse and Kluge, 1993; Honeycutt and Adkins, 1993; Patterson et al., 1993; Maizels and Ashburner, 1994; Morris, 1994a,b; Meyer, 1995).
Axon-Schwann Cell Interactions during Peripheral Nerve Degeneration and Regeneration
Authors: Scherer S S , Salzer J L 
Published: November 2023

Abstract
Wallerian degeneration refers to all of the events that occur distal to the site of axotomy (for recent reviews, see Ide, 1996; Fu and Gordon, 1997; Stoll and Muller, 1999; Terenghi, 1999). Although usually induced by mechanical injury in experimental studies, Wallerian degeneration is a feature of any insult that causes axonal degeneration. During the first week post-axotomy, axons fragment and disappear, and the myelin sheaths separate at incisures, breaking up into so-called ovoids. Over the next few weeks, these myelin ovoids are phagocytosed in part by Schwann cells but mainly by macrophages that invade the degenerating nerve. The clearance of myelin debris by macrophages promotes axonal regeneration (Dahlin, 1995), perhaps owing to the ability of myelin proteins to inhibit axonal regeneration (see Chapter 14). Schwann cells undergo extensive proliferation between 3 and 5 days post-axotomy (Oaklander and Spencer, 1988). The basal lamina persists and surrounds the column of ‘denervated’ Schwann cells, traditionally called a band of Büngner..
Analysis of variance methods
Authors: Diggle Peter J , Heagerty Patrick J , Liang Kung-yee. , Zeger Scott L 
Published: November 2023

Abstract
In this chapter we describe how simple methods for the analysis of data from designed experiments, in particular the analysis of variance (ANOVA) can be adapted to longitudinal studies. ANOVA has limitations which prevent its recommendation as a general approach for longitudinal data. The first is that it fails to exploit the potential gains in efficiency from modelling the covariance among repeated observations. A second is that, to a greater or lesser extent, ANOVA is a simple method only with a complete, balanced array of data. This second limitation has been substantially diminished by the development of general methodolodgy for incomplete data problems (Little and Rubin, 1987), but the first is fundamental. Whether it is crucial in practice depends on the details of the particular application, for example the size and shape of the data array. As noted in Section 4.1, modelling the covariance structure is most valuable for data consisting of a small number of long sequences of measurements.
Generalized linear models for longitudinal data
Authors: Diggle Peter J , Heagerty Patrick J , Liang Kung-yee. , Zeger Scott L 
Published: November 2023

Abstract
This chapter surveys approaches to the analysis of discrete and continuous longitudinal data using extensions of generalized linear models (GLMs). We have shown in Chapter 4 how regression inferences using linear models can be made robust to assumptions about the correlation, especially when the number of observations per person, ni, is small relative to the number of individuals, m. With linear models, although the estimation of the regression parameters must take into account the correlations in the data, their interpretation is essentially independent of the correlation structure. With non-linear models for discrete data, such as logistic regression, different assumptions about the source of correlation can lead to regression coeficients with distinct interpretations. The data-analyst must therefore think even more carefully about the objectives of the analysis and the source of correlation in choosing an approach.
Local Ring Theory
Authors: Boffi Giandomenico , Buchsbaum David A 
Published: November 2023

Abstract
The development of the first five sections of this chapter is guided by the desire to establish a string of inequalities for local rings (always assumed to be noetherian here) which lead to the result, in Section II.5, that a local ring is regular if and only if its global dimension is finite. The development here is very close to that used in the notes of Reference [19] and rests on the work found in References [7] and [8].
Generalized Koszul Complexes
Authors: Boffi Giandomenico , Buchsbaum David A 
Published: November 2023

Abstract
In the preceding chapter, we saw the fundamental role that the Koszul complex played in the study of certain aspects of local ring theory. One of the main ideas tied up with that complex is that of regular sequence, with the Cohen–Macaulay Theorem one of the major results obtained through its use.
Structure Theorems for Finite Free Resolutions
Authors: Boffi Giandomenico , Buchsbaum David A 
Published: November 2023

Abstract
In the previous chapter, we saw a whole class of complexes associated with a given map, f , of finite free modules. We proved that all of them were sensitive to the depth of the ideal generated by the maximal minors of f , and gave a necessary and sufficient condition for them to be finite free resolutions of the cokernels of the exterior powers of f in terms of the depth of the ideal generated by the maximal minors of f .