pro

Alleviating Spatial Confounding for Areal Data Problems by Displacing the Geographical Centroids

Marcos Oliveira Prates, Renato Martins Assunção, Erica Castilho Rodrigues.

Source: Bayesian Analysis, Volume 14, Number 2, 623--647.

Abstract:
Spatial confounding between the spatial random effects and fixed effects covariates has been recently discovered and showed that it may bring misleading interpretation to the model results. Techniques to alleviate this problem are based on decomposing the spatial random effect and fitting a restricted spatial regression. In this paper, we propose a different approach: a transformation of the geographic space to ensure that the unobserved spatial random effect added to the regression is orthogonal to the fixed effects covariates. Our approach, named SPOCK, has the additional benefit of providing a fast and simple computational method to estimate the parameters. Also, it does not constrain the distribution class assumed for the spatial error term. A simulation study and real data analyses are presented to better understand the advantages of the new method in comparison with the existing ones.




pro

Efficient Acquisition Rules for Model-Based Approximate Bayesian Computation

Marko Järvenpää, Michael U. Gutmann, Arijus Pleska, Aki Vehtari, Pekka Marttinen.

Source: Bayesian Analysis, Volume 14, Number 2, 595--622.

Abstract:
Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. However, many ABC algorithms require a large number of simulations, which can be costly. To reduce the computational cost, Bayesian optimisation (BO) and surrogate models such as Gaussian processes have been proposed. Bayesian optimisation enables one to intelligently decide where to evaluate the model next but common BO strategies are not designed for the goal of estimating the posterior distribution. Our paper addresses this gap in the literature. We propose to compute the uncertainty in the ABC posterior density, which is due to a lack of simulations to estimate this quantity accurately, and define a loss function that measures this uncertainty. We then propose to select the next evaluation location to minimise the expected loss. Experiments show that the proposed method often produces the most accurate approximations as compared to common BO strategies.




pro

A Bayesian Nonparametric Spiked Process Prior for Dynamic Model Selection

Alberto Cassese, Weixuan Zhu, Michele Guindani, Marina Vannucci.

Source: Bayesian Analysis, Volume 14, Number 2, 553--572.

Abstract:
In many applications, investigators monitor processes that vary in space and time, with the goal of identifying temporally persistent and spatially localized departures from a baseline or “normal” behavior. In this manuscript, we consider the monitoring of pneumonia and influenza (P&I) mortality, to detect influenza outbreaks in the continental United States, and propose a Bayesian nonparametric model selection approach to take into account the spatio-temporal dependence of outbreaks. More specifically, we introduce a zero-inflated conditionally identically distributed species sampling prior which allows borrowing information across time and to assign data to clusters associated to either a null or an alternate process. Spatial dependences are accounted for by means of a Markov random field prior, which allows to inform the selection based on inferences conducted at nearby locations. We show how the proposed modeling framework performs in an application to the P&I mortality data and in a simulation study, and compare with common threshold methods for detecting outbreaks over time, with more recent Markov switching based models, and with spike-and-slab Bayesian nonparametric priors that do not take into account spatio-temporal dependence.




pro

Analysis of the Maximal a Posteriori Partition in the Gaussian Dirichlet Process Mixture Model

Łukasz Rajkowski.

Source: Bayesian Analysis, Volume 14, Number 2, 477--494.

Abstract:
Mixture models are a natural choice in many applications, but it can be difficult to place an a priori upper bound on the number of components. To circumvent this, investigators are turning increasingly to Dirichlet process mixture models (DPMMs). It is therefore important to develop an understanding of the strengths and weaknesses of this approach. This work considers the MAP (maximum a posteriori) clustering for the Gaussian DPMM (where the cluster means have Gaussian distribution and, for each cluster, the observations within the cluster have Gaussian distribution). Some desirable properties of the MAP partition are proved: ‘almost disjointness’ of the convex hulls of clusters (they may have at most one point in common) and (with natural assumptions) the comparability of sizes of those clusters that intersect any fixed ball with the number of observations (as the latter goes to infinity). Consequently, the number of such clusters remains bounded. Furthermore, if the data arises from independent identically distributed sampling from a given distribution with bounded support then the asymptotic MAP partition of the observation space maximises a function which has a straightforward expression, which depends only on the within-group covariance parameter. As the operator norm of this covariance parameter decreases, the number of clusters in the MAP partition becomes arbitrarily large, which may lead to the overestimation of the number of mixture components.




pro

A Bayesian Approach to Statistical Shape Analysis via the Projected Normal Distribution

Luis Gutiérrez, Eduardo Gutiérrez-Peña, Ramsés H. Mena.

Source: Bayesian Analysis, Volume 14, Number 2, 427--447.

Abstract:
This work presents a Bayesian predictive approach to statistical shape analysis. A modeling strategy that starts with a Gaussian distribution on the configuration space, and then removes the effects of location, rotation and scale, is studied. This boils down to an application of the projected normal distribution to model the configurations in the shape space, which together with certain identifiability constraints, facilitates parameter interpretation. Having better control over the parameters allows us to generalize the model to a regression setting where the effect of predictors on shapes can be considered. The methodology is illustrated and tested using both simulated scenarios and a real data set concerning eight anatomical landmarks on a sagittal plane of the corpus callosum in patients with autism and in a group of controls.




pro

Modeling Population Structure Under Hierarchical Dirichlet Processes

Lloyd T. Elliott, Maria De Iorio, Stefano Favaro, Kaustubh Adhikari, Yee Whye Teh.

Source: Bayesian Analysis, Volume 14, Number 2, 313--339.

Abstract:
We propose a Bayesian nonparametric model to infer population admixture, extending the hierarchical Dirichlet process to allow for correlation between loci due to linkage disequilibrium. Given multilocus genotype data from a sample of individuals, the proposed model allows inferring and classifying individuals as unadmixed or admixed, inferring the number of subpopulations ancestral to an admixed population and the population of origin of chromosomal regions. Our model does not assume any specific mutation process, and can be applied to most of the commonly used genetic markers. We present a Markov chain Monte Carlo (MCMC) algorithm to perform posterior inference from the model and we discuss some methods to summarize the MCMC output for the analysis of population admixture. Finally, we demonstrate the performance of the proposed model in a real application, using genetic data from the ectodysplasin-A receptor (EDAR) gene, which is considered to be ancestry-informative due to well-known variations in allele frequency as well as phenotypic effects across ancestry. The structure analysis of this dataset leads to the identification of a rare haplotype in Europeans. We also conduct a simulated experiment and show that our algorithm outperforms parametric methods.




pro

Separable covariance arrays via the Tucker product, with applications to multivariate relational data

Peter D. Hoff

Source: Bayesian Anal., Volume 6, Number 2, 179--196.

Abstract:
Modern datasets are often in the form of matrices or arrays, potentially having correlations along each set of data indices. For example, data involving repeated measurements of several variables over time may exhibit temporal correlation as well as correlation among the variables. A possible model for matrix-valued data is the class of matrix normal distributions, which is parametrized by two covariance matrices, one for each index set of the data. In this article we discuss an extension of the matrix normal model to accommodate multidimensional data arrays, or tensors. We show how a particular array-matrix product can be used to generate the class of array normal distributions having separable covariance structure. We derive some properties of these covariance structures and the corresponding array normal distributions, and show how the array-matrix product can be used to define a semi-conjugate prior distribution and calculate the corresponding posterior distribution. We illustrate the methodology in an analysis of multivariate longitudinal network data which take the form of a four-way array.




pro

A Tale of Two Parasites: Statistical Modelling to Support Disease Control Programmes in Africa

Peter J. Diggle, Emanuele Giorgi, Julienne Atsame, Sylvie Ntsame Ella, Kisito Ogoussan, Katherine Gass.

Source: Statistical Science, Volume 35, Number 1, 42--50.

Abstract:
Vector-borne diseases have long presented major challenges to the health of rural communities in the wet tropical regions of the world, but especially in sub-Saharan Africa. In this paper, we describe the contribution that statistical modelling has made to the global elimination programme for one vector-borne disease, onchocerciasis. We explain why information on the spatial distribution of a second vector-borne disease, Loa loa, is needed before communities at high risk of onchocerciasis can be treated safely with mass distribution of ivermectin, an antifiarial medication. We show how a model-based geostatistical analysis of Loa loa prevalence survey data can be used to map the predictive probability that each location in the region of interest meets a WHO policy guideline for safe mass distribution of ivermectin and describe two applications: one is to data from Cameroon that assesses prevalence using traditional blood-smear microscopy; the other is to Africa-wide data that uses a low-cost questionnaire-based method. We describe how a recent technological development in image-based microscopy has resulted in a change of emphasis from prevalence alone to the bivariate spatial distribution of prevalence and the intensity of infection among infected individuals. We discuss how statistical modelling of the kind described here can contribute to health policy guidelines and decision-making in two ways. One is to ensure that, in a resource-limited setting, prevalence surveys are designed, and the resulting data analysed, as efficiently as possible. The other is to provide an honest quantification of the uncertainty attached to any binary decision by reporting predictive probabilities that a policy-defined condition for action is or is not met.




pro

Model-Based Approach to the Joint Analysis of Single-Cell Data on Chromatin Accessibility and Gene Expression

Zhixiang Lin, Mahdi Zamanighomi, Timothy Daley, Shining Ma, Wing Hung Wong.

Source: Statistical Science, Volume 35, Number 1, 2--13.

Abstract:
Unsupervised methods, including clustering methods, are essential to the analysis of single-cell genomic data. Model-based clustering methods are under-explored in the area of single-cell genomics, and have the advantage of quantifying the uncertainty of the clustering result. Here we develop a model-based approach for the integrative analysis of single-cell chromatin accessibility and gene expression data. We show that combining these two types of data, we can achieve a better separation of the underlying cell types. An efficient Markov chain Monte Carlo algorithm is also developed.




pro

Models as Approximations—Rejoinder

Andreas Buja, Arun Kumar Kuchibhotla, Richard Berk, Edward George, Eric Tchetgen Tchetgen, Linda Zhao.

Source: Statistical Science, Volume 34, Number 4, 606--620.

Abstract:
We respond to the discussants of our articles emphasizing the importance of inference under misspecification in the context of the reproducibility/replicability crisis. Along the way, we discuss the roles of diagnostics and model building in regression as well as connections between our well-specification framework and semiparametric theory.




pro

Discussion: Models as Approximations

Dalia Ghanem, Todd A. Kuffner.

Source: Statistical Science, Volume 34, Number 4, 604--605.




pro

Comment: Models as (Deliberate) Approximations

David Whitney, Ali Shojaie, Marco Carone.

Source: Statistical Science, Volume 34, Number 4, 591--598.




pro

Comment: Models Are Approximations!

Anthony C. Davison, Erwan Koch, Jonathan Koh.

Source: Statistical Science, Volume 34, Number 4, 584--590.

Abstract:
This discussion focuses on areas of disagreement with the papers, particularly the target of inference and the case for using the robust ‘sandwich’ variance estimator in the presence of moderate mis-specification. We also suggest that existing procedures may be appreciably more powerful for detecting mis-specification than the authors’ RAV statistic, and comment on the use of the pairs bootstrap in balanced situations.




pro

Comment: “Models as Approximations I: Consequences Illustrated with Linear Regression” by A. Buja, R. Berk, L. Brown, E. George, E. Pitkin, L. Zhan and K. Zhang

Roderick J. Little.

Source: Statistical Science, Volume 34, Number 4, 580--583.




pro

Discussion of Models as Approximations I & II

Dag Tjøstheim.

Source: Statistical Science, Volume 34, Number 4, 575--579.




pro

Comment: Models as Approximations

Nikki L. B. Freeman, Xiaotong Jiang, Owen E. Leete, Daniel J. Luckett, Teeranan Pokaprakarn, Michael R. Kosorok.

Source: Statistical Science, Volume 34, Number 4, 572--574.




pro

Comment on Models as Approximations, Parts I and II, by Buja et al.

Jerald F. Lawless.

Source: Statistical Science, Volume 34, Number 4, 569--571.

Abstract:
I comment on the papers Models as Approximations I and II, by A. Buja, R. Berk, L. Brown, E. George, E. Pitkin, M. Traskin, L. Zhao and K. Zhang.




pro

Discussion of Models as Approximations I & II

Sara van de Geer.

Source: Statistical Science, Volume 34, Number 4, 566--568.

Abstract:
We discuss the papers “Models as Approximations” I & II, by A. Buja, R. Berk, L. Brown, E. George, E. Pitkin, M. Traskin, L. Zao and K. Zhang (Part I) and A. Buja, L. Brown, A. K. Kuchibhota, R. Berk, E. George and L. Zhao (Part II). We present a summary with some details for the generalized linear model.




pro

Models as Approximations II: A Model-Free Theory of Parametric Regression

Andreas Buja, Lawrence Brown, Arun Kumar Kuchibhotla, Richard Berk, Edward George, Linda Zhao.

Source: Statistical Science, Volume 34, Number 4, 545--565.

Abstract:
We develop a model-free theory of general types of parametric regression for i.i.d. observations. The theory replaces the parameters of parametric models with statistical functionals, to be called “regression functionals,” defined on large nonparametric classes of joint ${x extrm{-}y}$ distributions, without assuming a correct model. Parametric models are reduced to heuristics to suggest plausible objective functions. An example of a regression functional is the vector of slopes of linear equations fitted by OLS to largely arbitrary ${x extrm{-}y}$ distributions, without assuming a linear model (see Part I). More generally, regression functionals can be defined by minimizing objective functions, solving estimating equations, or with ad hoc constructions. In this framework, it is possible to achieve the following: (1) define a notion of “well-specification” for regression functionals that replaces the notion of correct specification of models, (2) propose a well-specification diagnostic for regression functionals based on reweighting distributions and data, (3) decompose sampling variability of regression functionals into two sources, one due to the conditional response distribution and another due to the regressor distribution interacting with misspecification, both of order $N^{-1/2}$, (4) exhibit plug-in/sandwich estimators of standard error as limit cases of ${x extrm{-}y}$ bootstrap estimators, and (5) provide theoretical heuristics to indicate that ${x extrm{-}y}$ bootstrap standard errors may generally be preferred over sandwich estimators.




pro

Models as Approximations I: Consequences Illustrated with Linear Regression

Andreas Buja, Lawrence Brown, Richard Berk, Edward George, Emil Pitkin, Mikhail Traskin, Kai Zhang, Linda Zhao.

Source: Statistical Science, Volume 34, Number 4, 523--544.

Abstract:
In the early 1980s, Halbert White inaugurated a “model-robust” form of statistical inference based on the “sandwich estimator” of standard error. This estimator is known to be “heteroskedasticity-consistent,” but it is less well known to be “nonlinearity-consistent” as well. Nonlinearity, however, raises fundamental issues because in its presence regressors are not ancillary, hence cannot be treated as fixed. The consequences are deep: (1) population slopes need to be reinterpreted as statistical functionals obtained from OLS fits to largely arbitrary joint ${x extrm{-}y}$ distributions; (2) the meaning of slope parameters needs to be rethought; (3) the regressor distribution affects the slope parameters; (4) randomness of the regressors becomes a source of sampling variability in slope estimates of order $1/sqrt{N}$; (5) inference needs to be based on model-robust standard errors, including sandwich estimators or the ${x extrm{-}y}$ bootstrap. In theory, model-robust and model-trusting standard errors can deviate by arbitrary magnitudes either way. In practice, significant deviations between them can be detected with a diagnostic test.




pro

Producing Official County-Level Agricultural Estimates in the United States: Needs and Challenges

Nathan B. Cruze, Andreea L. Erciulescu, Balgobin Nandram, Wendy J. Barboza, Linda J. Young.

Source: Statistical Science, Volume 34, Number 2, 301--316.

Abstract:
In the United States, county-level estimates of crop yield, production, and acreage published by the United States Department of Agriculture’s National Agricultural Statistics Service (USDA NASS) play an important role in determining the value of payments allotted to farmers and ranchers enrolled in several federal programs. Given the importance of these official county-level crop estimates, NASS continually strives to improve its crops county estimates program in terms of accuracy, reliability and coverage. In 2015, NASS engaged a panel of experts convened under the auspices of the National Academies of Sciences, Engineering, and Medicine Committee on National Statistics (CNSTAT) for guidance on implementing models that may synthesize multiple sources of information into a single estimate, provide defensible measures of uncertainty, and potentially increase the number of publishable county estimates. The final report titled Improving Crop Estimates by Integrating Multiple Data Sources was released in 2017. This paper discusses several needs and requirements for NASS county-level crop estimates that were illuminated during the activities of the CNSTAT panel. A motivating example of planted acreage estimation in Illinois illustrates several challenges that NASS faces as it considers adopting any explicit model for official crops county estimates.




pro

A Kernel Regression Procedure in the 3D Shape Space with an Application to Online Sales of Children’s Wear

Gregorio Quintana-Ortí, Amelia Simó.

Source: Statistical Science, Volume 34, Number 2, 236--252.

Abstract:
This paper is focused on kernel regression when the response variable is the shape of a 3D object represented by a configuration matrix of landmarks. Regression methods on this shape space are not trivial because this space has a complex finite-dimensional Riemannian manifold structure (non-Euclidean). Papers about it are scarce in the literature, the majority of them are restricted to the case of a single explanatory variable, and many of them are based on the approximated tangent space. In this paper, there are several methodological innovations. The first one is the adaptation of the general method for kernel regression analysis in manifold-valued data to the three-dimensional case of Kendall’s shape space. The second one is its generalization to the multivariate case and the addressing of the curse-of-dimensionality problem. Finally, we propose bootstrap confidence intervals for prediction. A simulation study is carried out to check the goodness of the procedure, and a comparison with a current approach is performed. Then, it is applied to a 3D database obtained from an anthropometric survey of the Spanish child population with a potential application to online sales of children’s wear.




pro

Gaussian Integrals and Rice Series in Crossing Distributions—to Compute the Distribution of Maxima and Other Features of Gaussian Processes

Georg Lindgren.

Source: Statistical Science, Volume 34, Number 1, 100--128.

Abstract:
We describe and compare how methods based on the classical Rice’s formula for the expected number, and higher moments, of level crossings by a Gaussian process stand up to contemporary numerical methods to accurately deal with crossing related characteristics of the sample paths. We illustrate the relative merits in accuracy and computing time of the Rice moment methods and the exact numerical method, developed since the late 1990s, on three groups of distribution problems, the maximum over a finite interval and the waiting time to first crossing, the length of excursions over a level, and the joint period/amplitude of oscillations. We also treat the notoriously difficult problem of dependence between successive zero crossing distances. The exact solution has been known since at least 2000, but it has remained largely unnoticed outside the ocean science community. Extensive simulation studies illustrate the accuracy of the numerical methods. As a historical introduction an attempt is made to illustrate the relation between Rice’s original formulation and arguments and the exact numerical methods.




pro

The Joyful Reduction of Uncertainty: Music Perception as a Window to Predictive Neuronal Processing




pro

Brain-Derived Neurotrophic Factor Protection of Cortical Neurons from Serum Withdrawal-Induced Apoptosis Is Inhibited by cAMP

Steven Poser
Jun 1, 2003; 23:4420-4427
Cellular




pro

Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control

William W. Seeley
Feb 28, 2007; 27:2349-2356
BehavioralSystemsCognitive




pro

Synaptic Specificity and Application of Anterograde Transsynaptic AAV for Probing Neural Circuitry

Brian Zingg
Apr 15, 2020; 40:3250-3267
Systems/Circuits




pro

Dural Calcitonin Gene-Related Peptide Produces Female-Specific Responses in Rodent Migraine Models

Amanda Avona
May 29, 2019; 39:4323-4331
Systems/Circuits




pro

What Visual Information Is Processed in the Human Dorsal Stream?

Martin N. Hebart
Jun 13, 2012; 32:8107-8109
Journal Club




pro

Endothelial Adora2a Activation Promotes Blood-Brain Barrier Breakdown and Cognitive Impairment in Mice with Diet-Induced Insulin Resistance

Masaki Yamamoto
May 22, 2019; 39:4179-4192
Neurobiology of Disease




pro

Sleep Loss Promotes Astrocytic Phagocytosis and Microglial Activation in Mouse Cerebral Cortex

Michele Bellesi
May 24, 2017; 37:5263-5273
Cellular




pro

Visualization of Microtubule Growth in Cultured Neurons via the Use of EB3-GFP (End-Binding Protein 3-Green Fluorescent Protein)

Tatiana Stepanova
Apr 1, 2003; 23:2655-2664
Cellular




pro

Neurons Containing Hypocretin (Orexin) Project to Multiple Neuronal Systems

Christelle Peyron
Dec 1, 1998; 18:9996-10015
Articles




pro

Molecular cloning, functional properties, and distribution of rat brain alpha 7: a nicotinic cation channel highly permeable to calcium

P Seguela
Feb 1, 1993; 13:596-604
Articles




pro

Pax6, Tbr2, and Tbr1 Are Expressed Sequentially by Radial Glia, Intermediate Progenitor Cells, and Postmitotic Neurons in Developing Neocortex

Chris Englund
Jan 5, 2005; 25:247-251
BRIEF COMMUNICATION




pro

Mice Deficient in Cellular Glutathione Peroxidase Show Increased Vulnerability to Malonate, 3-Nitropropionic Acid, and 1-Methyl-4-Phenyl-1,2,5,6-Tetrahydropyridine

Peter Klivenyi
Jan 1, 2000; 20:1-7
Cellular




pro

Cortical Excitatory Neurons and Glia, But Not GABAergic Neurons, Are Produced in the Emx1-Expressing Lineage

Jessica A. Gorski
Aug 1, 2002; 22:6309-6314
BRIEF COMMUNICATION




pro

Neurogenesis in the dentate gyrus of the adult rat: age-related decrease of neuronal progenitor proliferation

HG Kuhn
Mar 15, 1996; 16:2027-2033
Articles




pro

High-Level Neuronal Expression of A{beta}1-42 in Wild-Type Human Amyloid Protein Precursor Transgenic Mice: Synaptotoxicity without Plaque Formation

Lennart Mucke
Jun 1, 2000; 20:4050-4058
Cellular




pro

Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control

William W. Seeley
Feb 28, 2007; 27:2349-2356
BehavioralSystemsCognitive




pro

Afterschool Program Instructors




pro

Rassegna trimestrale BRI settembre 2017: Le prospettive positive in un contesto di bassa inflazione alimentano l'assunzione di rischio

Italian translation of the BIS press release about the BIS Quarterly Review, September 2017




pro

A fronte della diffusione delle criptovalute, le autorità devono essere pronte ad agire - Agustín Carstens

Italian translation of Press Release about BIS General Manager Agustín Carstens giving a speech on "Money in the digital age: what role for central banks?" (6 February 2018)




pro

Questions fréquemment posées sur les exigences de fonds propres en regard du risque de marché

French translation of "Frequently asked questions on market risk capital requirements" by the Basel Committee, March 2018.




pro

Informe Trimestral del BPI, marzo de 2018: La volatilidad vuelve a cobrar protagonismo tras un episodio de inestabilidad en los mercados bursátiles

Spanish translation of the BIS press release about the BIS Quarterly Review, March 2018




pro

Aprovechar el momento para lograr un crecimiento sostenido

Spanish translation of the BIS press release on the presentation of the Annual Economic Report 2018, 24 June 2018. Las autoridades pueden prolongar el actual repunte económico más allá del corto plazo aplicando reformas estructurales, reconstruyendo el espacio de las políticas monetaria y fiscal para afrontar futuras amenazas y fomentando una pronta implementación de las reformas reguladoras, sostiene el Banco de Pagos Internacionales (BPI) en su Informe Económico Anual. ...







pro

Academy launches online events programme