bluuebunny/crossref_metadata_2025_split huggingface dataset:
Functional and anatomical decomposition of face processing: evidence from prosopagnosia and PET study of normal subjects
Authors: Signoret Justine , Sergent Jean-Louis 
Published: November 2023

Abstract
Studies of brain-damaged patients have revealed the existence of a selective impairment of face processing, prosopagnosia, resulting from lesions at different loci in the occipital and temporal lobes. The lesions are often extensive, and it is unclear what functional aspects of face processing are normally served by the damaged areas, and whether they are uniquely devoted to the processing of faces. These issues are further addressed through a combined magnetic resonance imaging (MRI) and positron emission tomography (PET) study of regional cerebral blood flow (rCBF) in normal subjects performing different tasks of face and object processing. The results indicate different patterns of cerebral activation depending on the requirements of the tasks within the processing of faces, as well as a clear dissociation of the neural substrates underlying face and object processing. These results are compared with radiological data from prosopagnosic patients, and are put in relation with the patterns of deficits observed in the patients as a function of the location of their lesions. Together, the findings offer new evidence regarding the functional neuroanatomy of face and object processing.
Bayesian Analysis of Mixtures: Some Results on Exact Estimability and Identification
Authors: Florens J -P , Mouchart M , Rolin J -M 
Published: November 2023

Abstract
Models frequently used for car insurance portfolios assume that the distribution of the expected number of accidents caused by a client is a mixture of Dirichlet processes. Given that the number of clients is typically rather large, insurance companies may find it relevant to inquire whether the posterior distributions (given the number of individual accidents) of the mixture, of the mixing parameter and the predictive distribution of a new observation, given the number of individual accidents, are consistently convergent. For most models of this rather general class, answering these questions requires unfathomable manipulations. In this paper, it is shown that answers are “easily” obtainable from general results, presented in the authors’ monograph Elements of Bayesian Statistics. These answers rely on no specific assumption on the form of the distribution but only on simple conditions on latent processes. Some problems of approximations are also discussed.
Some Bayesian Numerical Analysis
Authors: O’hagan A 
Published: November 2023

Abstract
Bayesian approaches to interpolation, quadrature and optimisation are discussed, based on representing prior information about the function in question in terms of a Gaussian process. Emphasis is placed on how different methods are appropriate when the function is cheap or expensive to evaluate. A particular case of expensive functions is a regression function, where ‘evaluation’ consists of gaining observations (with the small added complication of measurement error).
A Numerical Integration Strategy in Bayesian Analysis
Authors: Dellaportas Petros , Wright David E 
Published: November 2023

Abstract
This paper describes how embedded sequences of positive interpolatory integration rules, described in Dellaportas and Wright (1991), can be applied in Bayesian statistics. In particular, we discuss how a new numerical integration strategy can extend the applicability and increase the efficiency of currently available numerical methods. The proposed strategy is demonstrated using a 7-parameter proportional hazards model.
A Bayesian Justification for the Analysis of Residuals and Influence Measures
Authors: Girón F J , Martínez M L , Morcillo C 
Published: November 2023

Abstract
The usual approach to the analysis of residuals and influence measures in linear regression is based mostly on intuitive grounds. However, from a Bayesian perspective, the sequential updating of parameters via the Kalman filter applied to the regression model provides a sound basis for this analysis. Equivalence with the classical approach, and some possible generalizations are considered. First, we give some definitions of outlying and influential measures in a more general context.
Analysis of Multistage Survey as a Bayesian Hierarchical Model
Authors: Pérez M E , Pericchi L R 
Published: November 2023

Abstract
Using the hierarchical approach to the usual linear model we obtain a satisfactory Bayesian solution to multistage survey with unknown variances. This generalizes some results and corrects other results related to a unknown variances and regression relation, previously obtained by Scott and Smith (1969). Furthermore a reparameterization is employed which permits to reduce the dimension of the integration involved for HPD intervals. Finally, the results are applied in the context of a simulation study.
Dynamic Graphical Models
Authors: Queen Catriona M , Smith Jim Q 
Published: November 2023

Abstract
Dynamic graphical models (OOM’s) are defined to forecast multivariate time series for which there is believed to be symmetry between certain subsets of variables and a causal driving mechanism between these subsets. They are a specific type of graphical chain model (Wermuth and Lauritzen, 1990) which are both non-linear and non-normal. OOM’s are a combination of dynamic multivariate regression models (Quintana, 1985, 1987, Quintana and West, 1987, 1988) and multiregression dynamic models (Queen and Smith, 1990) and as such they inherit the simplicity of both these models. If conditional normality is assumed across each subset, important quantities can be calculated and updating formulae can be found explicitly. The models are illustrated by a simple example.
Convergence Diagnostics of the Gibbs Sampler
Authors: Roberts G O 
Published: November 2023

Abstract
The emergence of the Gibbs sampler, and other associated stochastic simulation techniques, is clearly an important advance in the generation of samples from multi-dimensional distributions, which has particular application in Bayesian statistics. The advantages of the algorithm are its ease of implementation, its speed of convergence, and the lack of regularity conditions necessary to ensure convergence. Its major drawback, however, is that although convergence is well understood theoretically, diagnosis of convergence is often difficult. In this paper, we address the issue of convergence and its diagnosis. In particular, we focus our attention on the search for a one-dimensional summary statistic which attempts to describe the convergence mechanism.
Incorporating Prior Knowledge in a Marked Point Process Model
Authors: Sølna Knut 
Published: November 2023

Abstract
A procedure for 3D stochastic modelling of geometric deformation of rock caused by tectonic activity will be presented. The rationale is to enable an analysis of the impact of faulting on fluid flow in petroleum reservoirs. The 3D stochastic model for the break patterns occurring in faulted zones is based on the theory of marked point processes. In order to generate realistic break patterns it is necessary to incorporate prior knowledge about the tectonic deformations, and a Bayesian framework making this possible will be specified. Each fault is associated with a prior marginal distribution and probabilistic modelling of spatial interaction between faults enables modelling of homogeneous release of the stress field. Using a Bayesian approach facilitates the incorporation of seismic information as a global constraint. The structural heterogeneities can be reproduced by stochastic simulation, which also makes possible a quantification of the uncertainty associated with the description.
Bayesian Analysis of Generalised Linear Models with Covariate Measurement Error
Authors: Stephens D A , Dellaportas P 
Published: November 2023

Abstract
Use of generalised linear models when covariates are masked by measurement errors is appropriate in many practical epidemiological problems. However, inference based on such models is by no means straightforward. In previous analyses, simplifying assumptions were made. In this paper, we analyse such models in full generality under a Bayesian formulation. In order to compute the necessary posterior distributions, we utilize various numerical and sampling-based approximate and exact techniques. A specific example involving a logistic regression is considered.
On Asymptotic Posterior Normality in the Multiparameter Case
Authors: Sweeting Trevor J 
Published: November 2023

Abstract
Current approaches to demonstrating asymptotic posterior normality in the multiparameter case fail to cover certain cases where the data arise from a stochastic process. In this paper we present a set of hypotheses for asymptotic posterior normality which are satisfied in such cases. Continuity-type conditions on observed information are imposed on suitable shrinking neighbourhoods and workable conditions relating to the tail behaviour of the posterior distribution are given. The theory is illustrated with an example of a two-parameter nonhomogeneous Poisson process.
REML estimation of variance components and analysis of unbalanced designs
Authors: Payne R W , Lane P W , Digby P G N , Harding S A , Leech P K , Morgan G W , Todd AD , Thompson R , Wilson G Tunnicliffe , Welham S J , White R P 
Published: November 2023

Abstract
This chapter describes the facilities for estimation of variance components and analysis of linear mixed models using the method of residual maximum likelihood (REML), sometimes also known as restricted maximum likelihood.
The REML algorithm estimates the treatment effects and variance components in a linear mixed model: that is, a linear model with both fixed and random effects. Like regression, REML can be used to analyse unbalanced data sets; but, unlike regression, it can also account for more than one source of variation in the data, providing an estimate of the variance components associated with the random terms in the model.
The first Americans: different waves of migration to the New World inferred from mitochondrial DNA sequence polymorphisms
Authors: Horai Satoshi , Kondo Rumi , Sonoda Shunro , Tajima Kazuo 
Published: November 2023

Abstract
Mongoloid descendants are now distributed over a wide area of the Pacific-rim region, having adapted to a variety of environments. One of the major issues for research into the prehistoric dispersal of Asiatic Mongoloid peoples is the question of the first Americans, the ‘peopling of the New World’. No authority doubts that the ancestors of Native Americans came from northeast Asia across the Bering land bridge, then dispersed and settled in various parts of the Americas, and finally reached the southernmost part of South America. How ever, when they came and with what genetic backgrounds and cultures is not yet fully resolved.
Early agriculture and the dispersal of the southern Mongoloids
Authors: Bellwood Peter 
Published: November 2023

Abstract
In my archaeological and linguistic perspective on southeast Asia (e.g. Bellwood 1985, 1991a, 1992a) I have taken the view that populations ancestral to present day Mongoloids began to colonize the region from the north during the early phases of dependence on agriculture. This colonization started in the fourth millennium BC and is continuing locally in some remote regions today. In eastern Indonesia these incoming Mongoloid groups intermarried gradually with the Melanesian populations who already occupied the area in relatively high den sities. While pre-agricultural populations with some clinal degrees of Mongoloid affinity might have existed in the northern and western parts of southeast Asia, the southeast Asian Mongoloid populations as a whole cannot have evolved in situ within southeast Asia from low latitude Pleistocene forerunners, separate from the major (and obviously related) populations of east Asian Mongoloids outside the tropics. The phenotypic trends in skin pigmentation and hair form developed due to equatorial propinquity elsewhere in the Old World (Africa, southern India, Australia, Melanesia) do not occur in this region (Bellwood 1985; Robbins 1991). The southeast Asian Mongoloids do not represent an equatorial adaptation, except on a superficial level. They must have expanded, allowing for intermarriage with indigenous populations, into their southern regions of distribution from somewhere north of the tropics.
Are animal experiments in toxicological research the ‘right’ size?
Authors: Morgan B J T 
Published: November 2023

Abstract
The use of animals in biomedical research involves complex ethical issues. Whether or not any pain and suffering experienced by the animals can be justified in terms of better future human and animal health is hotly debated. However, both scientists and those concerned with animal welfare would agree that the unnecessary or wasteful use of animals cannot be justified.
Estimating dose-response for toxic end-points
Authors: Salsburg David 
Published: November 2023

Abstract
The above quotation lies at the heart of almost all toxicological investigations. It tends to be ignored in the popular press and in political discussions of the consequences of these investigations, so we often read about ‘toxic substances’ that are ‘the most toxic known to man’ or that are ‘very toxic’. The resulting political debates sometimes assume that some substances are ‘toxic’ and some are not. Then, the debate is over whether a particular substance belongs in that class. However, a little careful thought about the biological effects of chemicals will soon produce a Paracelsian conclusion. One molecule of anything is not going to produce damage to a living organism; and enough of anything, no matter how essential it may be to life, will kill. The ability of modern analytical chemistry to detect levels of contaminants down to the parts per billion level has made it essential that even the political debates come back to Paracelsus’ insights.
Overdispersion in logistic-linear models
Authors: Williams David A 
Published: November 2023

Abstract
The logistic-linear model is well established as the basis for analysing regression data, or data from a designed experiment, when the response variable is a proportion. Suppose that the data consist of a set of n cases. In the ith case ri successes, mi - ri failures, and a vector Xi of p explanatory variables are recorded. The logistic-linear model assumes that the ri are realizations of random variables Ri distributed independently Binomial(mi, θi), where θi is related to a linear predictor through the logistic link logit(θi) = log{θi/(l - θi)} = ηi;• An alternative link is the probit, given by probit(θi) = &lt;I&gt;-1(θi) = ηi, where &lt;1&gt;-1 denotes the inverse Normal distribution function.
Exploratory and model-based inference in toxicokinetics
Authors: Urfer W 
Published: November 2023

Abstract
Risk assessment of potentially harmful chemicals is fundamental in toxicology. Thorough research depends on When animals serve as models of human systems, a critical step is extrapolation from the risk observed in the experimental animals to the risk associated with the human organism. To avoid serious error, the researcher must consider both toxicokinetics in specific experimental animals (including uptake, distribution, elimination, and metabolism of the chemicals), as well as interspecific differences among various animals.
Bayesian Questions and Answers in Queues¹
Authors: Armero C , Bayarri M J 
Published: November 2023

Abstract
A queueing system occurs when “customers” arrive at some facility requiring a certain type of “service” provided by the “servers”. Both the arrival pattern and the service requirements are usually taken to be random. If all servers are busy when customers arrive, they usually wait in line to get served. The statistical analysis of queueing systems poses a number of interesting and challenging questions and Bayesian methods are especially well suited to deal with them. In this paper we address some of these questions for a number of Markovian queueing systems, in which both the interarrival and the service time are assumed to be exponential, and the number of servers can vary.
Plausible Bayesian Games
Authors: Smith J Q 
Published: November 2023

Abstract
Many situations which a Bayesian wants to model, especially when in an economic or business environment, are, in essence, complicated games between more than one player. In this paper I discuss the extent to which I believe ideas of rationality can help a Bayesian to formulate practical and plausible models of her competitors’ behaviour. A Bayesian model is a subjective representation of the world as that Bayesian sees it. One purpose is to provoke others into adjusting their own beliefs structures after studying it. To have an impact, therefore, every Bayesian model should be supported by well-reasoned arguments about the various modelling assumptions on which it is based. The aim of this paper is to discuss how these arguments might be constructed and then appraised when the model of interest concerns the behaviour of more than one rational decision maker.
Intrinsic Priors via Kullback-Leibler Geometry
Authors: Dmochowski J 
Published: November 2023

Abstract
In Bayesian model selection or hypothesis testing problems the Bayes factor (BF) cannot be used directly if at least one of the priors is improper. Berger and Pericchi (1993) proposed the use of the so-called intrinsic Bayes factor (IBF). The IBF is a Bayes factor (BF) multiplied by a data dependent “correction” term. The asymptotic behavior of both the BF and the “correction” term will be described in the iid case. Intrinsic priors are priors for which the resulting BF is asymptotically equivalent to the IBF. Asymptotic considerations lead to one (nested case) or two (non-nested case) functional equations. These intrinsic equations involve Kullback-Leibler projections. A geometrical interpretation and the general form of the solutions for the nested case will be presented and illustrated through examples.
Conditional External Bayesianity in Decomposable Influence Diagrams
Authors: Faria A E , Smith J Q 
Published: November 2023

Abstract
This paper addresses the group decision problem, where the members of a group may be jointly responsible for a decision. We shall assume that the group has agreed on the structure of associations of variables in a problem, as might be represented by a commonly agreed influence diagram (ID). However, the members of that group diverge about the actual conditional probability distributions for the variables in that ID. The algorithm we suggest they adopt is one which demands, at least on learning information which preserves the ID structure, that the pool of conditional distributions associated with the ID are externally Bayesian (EB). It is illustrated why such a generalisation of external Bayesianity is practically quite useful.
Bayes Factors, Nuisance Parameters and Imprecise Tests
Authors: Verdinelli I , Wasserman L 
Published: November 2023

Abstract
Bayes factors are often used to test precise null hypotheses. One objection to the Bayes factor B is that a precise null hypothesis can sometimes be unrealistic. But it can be shown that B approximates the Bayes factor for an imprecise null hypothesis, under very mild conditions. We show that a similar approximation holds when there are nuisance parameters. Specifically, if the likelihood is bounded and continuous, then it suffices that the prior under the null hypothesis be the geometric conditional of the prior under the alternative. The form of the conditional is determined by the sequence of approximating null hypotheses. If the likelihood is not continuous, it may not be possible to approximate an imprecise test with the precise test.
Bayes Linear Adjustment for Variance Matrices
Authors: Wilkinson D J , Goldstein M 
Published: November 2023

Abstract
We examine the problem of covariance belief revision using a geometric approach. We exhibit an inner-product space where covariance matrices live naturally—a space of random real symmetric matrices. Toe inner product on this space captures aspects of our beliefs about the relationship between covariance matrices of interest to us, providing a structure rich enough for us to adjust beliefs about unknown matrices in the light of data such as sample covariance matrices, exploiting second-order exchangeability specifications.
On Improving a Model for Combining Experts’ Forecasts
Authors: Wiper M P , Pettit L I 
Published: November 2023

Abstract
In Wiper and French (1994), a model was introduced for updating a Decision Maker’s forecasts in the light of hearing an expert’s quantiles for the forecast variable. This model contained the unrealistic assumption that the Decision Maker believed it possible that the expert’s quantiles could be unordered. Here, we use the Gibbs sampler to remove this flaw in the initial model.
Local spectral theory for multipliers
Authors: Laursen Kjeld B , Neumann Michael M 
Published: November 2023

Abstract
This chapter is devoted to the discussion of an area where spectral theory, harmonic analysis, and the theory of Banach algebras overlap and interact. Our main interest is in the spectral properties of convolution operators on certain group or measure algebras. A general frame is provided by the theory of multipliers on commutative Banach algebras. The simple and natural notion of a multiplier extends that of a multiplication operator, and leads to a uniform approach to convolution operators in various settings. The basics of this theory and a series of important examples are presented in Section 4.1.
Practitioners and Patricians
Authors: Field J V 
Published: November 2023

Abstract
Choosing to look exclusively at first class works of art has its disadvantages for the historian. Among other things, one tends to avoid seeing the snags inherent in a particular style. For the fifteenth and sixteenth centuries, excellent correctives are available, both in the form of second class works of art and in the form of unkind comments by artists about other people’s work. Renaissance man, as revealed in the vivid pages of Niccolo Machiavelli’s The Prince (written in 1513), is chiefly characterised by his determination to see off the opposition.
Large deviations of stationary reflected Brownian motions
Authors: Majewski Kurt 
Published: November 2023

Abstract
Large deviation principles (LDPs) for queue length distributions of queueing systems are of great interest for the analysis of tail probabilities and thus for the analysis of the performance of these systems. So far, for queueing systems with more than one queue LDPs are only available in particular cases; see Bertsimas et al. (1994), Dupuis and Ellis (1995) and O’Connell (1996). Working in a heavy traffic regime, stochastic queueing networks can be approximated by multidimensional reflected Brownian motions (RBMs); see Harrison and Nguyen (1993). In particular, for multiclass feedforward queueing systems Peterson (1991) proved the weak convergence of the properly scaled workload process to an RBM, when the traffic intensity converges to one with a certain speed. Here, a feedforward queueing system is a queueing system in which the nodes can be numbered in such a way that each customer is routed along nodes with increasing numbers. The resulting limit RBMs, which we call foedforward RBMs, have a reflection matrix of the form (I+ Q)-1, where I is the identity matrix and Q is a lower diagonal matrix with non-negative entries. In this paper, we prove an LDP for the steady-state distribution of feedforward RBMs. We also give an example of an explicit calculation of a certain large deviation rate.