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Hamburg : Wilhelm Mauke, 1871.

Berlin : S. Karger, 1894.

Wien : A. Holder, 1892.

Leipzig : Franz Deuticke, 1897.

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Stuttgart : F. Enke, 1897.

Leipzig : Toeplitz & Deuticke, 1887.

Leipzig : Toeplitz & Deuticke, 1888.

Cheltenham : printed for G.A. Williams, librarian, [1823]

Edinburgh : printed for William Creech, 1787.

Edinburgh : J. Dunn, 1826.

[London] : E. & F.N. Spon, 1881.

Edinburgh : [publisher not identified], 1812.

Edinburgh : W.P. Nimmo, [1867]

Glasgow : Hedderwick, 1822.

Amsterdam : J. Muller, 1895.

Leipzig : G. Thieme, 1893.

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Springfield, Virginia : National Technical Information Service, 1974.

Anatoli Juditsky, Arkadi Nemirovski.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 458--502.

Abstract:
We consider the problem of recovering linear image of unknown signal belonging to a given convex compact signal set from noisy observation of another linear image of the signal. We develop a simple generic efficiently computable non linear in observations “polyhedral” estimate along with computation-friendly techniques for its design and risk analysis. We demonstrate that under favorable circumstances the resulting estimate is provably near-optimal in the minimax sense, the “favorable circumstances” being less restrictive than the weakest known so far assumptions ensuring near-optimality of estimates which are linear in observations.

Dani Gamerman.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 686--705.

Abstract:
Point processes are one of the most commonly encountered observation processes in Spatial Statistics. Model-based inference for them depends on the likelihood function. In the most standard setting of Poisson processes, the likelihood depends on the intensity function, and can not be computed analytically. A number of approximating techniques have been proposed to handle this difficulty. In this paper, we review recent work on exact solutions that solve this problem without resorting to approximations. The presentation concentrates more heavily on discrete time but also considers continuous time. The solutions are based on model specifications that impose smoothness constraints on the intensity function. We also review approaches to include a regression component and different ways to accommodate it while accounting for additional heterogeneity. Applications are provided to illustrate the results. Finally, we discuss possible extensions to account for discontinuities and/or jumps in the intensity function.

Hays County announced it has joined the Texas Purchasing Group and will be publishing and distributing upcoming bid opportunities on the system along with their current platform in these unprecedented...

(PRWeb April 09, 2020)

Read the full story at https://www.prweb.com/releases/hays_county_joins_the_texas_purchasing_group_by_bidnet_direct/prweb17021429.htm

Davy Paindaveine, Julien Remy, Thomas Verdebout.

Source: The Annals of Statistics, Volume 48, Number 1, 324--345.

Abstract:
We consider the problem of testing, on the basis of a $p$-variate Gaussian random sample, the null hypothesis $mathcal{H}_{0}:oldsymbol{ heta}_{1}=oldsymbol{ heta}_{1}^{0}$ against the alternative $mathcal{H}_{1}:oldsymbol{ heta}_{1} eq oldsymbol{ heta}_{1}^{0}$, where $oldsymbol{ heta}_{1}$ is the “first” eigenvector of the underlying covariance matrix and $oldsymbol{ heta}_{1}^{0}$ is a fixed unit $p$-vector. In the classical setup where eigenvalues $lambda_{1}>lambda_{2}geq cdots geq lambda_{p}$ are fixed, the Anderson ( Ann. Math. Stat. 34 (1963) 122–148) likelihood ratio test (LRT) and the Hallin, Paindaveine and Verdebout ( Ann. Statist. 38 (2010) 3245–3299) Le Cam optimal test for this problem are asymptotically equivalent under the null hypothesis, hence also under sequences of contiguous alternatives. We show that this equivalence does not survive asymptotic scenarios where $lambda_{n1}/lambda_{n2}=1+O(r_{n})$ with $r_{n}=O(1/sqrt{n})$. For such scenarios, the Le Cam optimal test still asymptotically meets the nominal level constraint, whereas the LRT severely overrejects the null hypothesis. Consequently, the former test should be favored over the latter one whenever the two largest sample eigenvalues are close to each other. By relying on the Le Cam’s asymptotic theory of statistical experiments, we study the non-null and optimality properties of the Le Cam optimal test in the aforementioned asymptotic scenarios and show that the null robustness of this test is not obtained at the expense of power. Our asymptotic investigation is extensive in the sense that it allows $r_{n}$ to converge to zero at an arbitrary rate. While we restrict to single-spiked spectra of the form $lambda_{n1}>lambda_{n2}=cdots =lambda_{np}$ to make our results as striking as possible, we extend our results to the more general elliptical case. Finally, we present an illustrative real data example.

Alex Diana, Eleni Matechou, Jim Griffin, Alison Johnston.

Source: The Annals of Applied Statistics, Volume 14, Number 1, 473--493.

Abstract:
Environmental changes in recent years have been linked to phenological shifts which in turn are linked to the survival of species. The work in this paper is motivated by capture-recapture data on blackcaps collected by the British Trust for Ornithology as part of the Constant Effort Sites monitoring scheme. Blackcaps overwinter abroad and migrate to the UK annually for breeding purposes. We propose a novel Bayesian nonparametric approach for expressing the bivariate density of individual arrival and departure times at different sites across a number of years as a mixture model. The new model combines the ideas of the hierarchical and the dependent Dirichlet process, allowing the estimation of site-specific weights and year-specific mixture locations, which are modelled as functions of environmental covariates using a multivariate extension of the Gaussian process. The proposed modelling framework is extremely general and can be used in any context where multivariate density estimation is performed jointly across different groups and in the presence of a continuous covariate.

Javier Cárcamo, Antonio Cuevas, Luis-Alberto Rodríguez.

Source: Bernoulli, Volume 26, Number 3, 2143--2175.

Abstract:
We show that various functionals related to the supremum of a real function defined on an arbitrary set or a measure space are Hadamard directionally differentiable. We specifically consider the supremum norm, the supremum, the infimum, and the amplitude of a function. The (usually non-linear) derivatives of these maps adopt simple expressions under suitable assumptions on the underlying space. As an application, we improve and extend to the multidimensional case the results in Raghavachari ( Ann. Statist. 1 (1973) 67–73) regarding the limiting distributions of Kolmogorov–Smirnov type statistics under the alternative hypothesis. Similar results are obtained for analogous statistics associated with copulas. We additionally solve an open problem about the Berk–Jones statistic proposed by Jager and Wellner (In A Festschrift for Herman Rubin (2004) 319–331 IMS). Finally, the asymptotic distribution of maximum mean discrepancies over Donsker classes of functions is derived.

Abhirup Datta, Sudipto Banerjee, James S. Hodges, Leiwen Gao.

Source: Bayesian Analysis, Volume 14, Number 4, 1221--1244.

Abstract:
Hierarchical models for regionally aggregated disease incidence data commonly involve region specific latent random effects that are modeled jointly as having a multivariate Gaussian distribution. The covariance or precision matrix incorporates the spatial dependence between the regions. Common choices for the precision matrix include the widely used ICAR model, which is singular, and its nonsingular extension which lacks interpretability. We propose a new parametric model for the precision matrix based on a directed acyclic graph (DAG) representation of the spatial dependence. Our model guarantees positive definiteness and, hence, in addition to being a valid prior for regional spatially correlated random effects, can also directly model the outcome from dependent data like images and networks. Theoretical results establish a link between the parameters in our model and the variance and covariances of the random effects. Simulation studies demonstrate that the improved interpretability of our model reaps benefits in terms of accurately recovering the latent spatial random effects as well as for inference on the spatial covariance parameters. Under modest spatial correlation, our model far outperforms the CAR models, while the performances are similar when the spatial correlation is strong. We also assess sensitivity to the choice of the ordering in the DAG construction using theoretical and empirical results which testify to the robustness of our model. We also present a large-scale public health application demonstrating the competitive performance of the model.

Yushu Shi, Michael Martens, Anjishnu Banerjee, Purushottam Laud.

Source: Bayesian Analysis, Volume 14, Number 3, 677--702.

Abstract:
Dirichlet process mixture (DPM) models provide flexible modeling for distributions of data as an infinite mixture of distributions from a chosen collection. Specifying priors for these models in individual data contexts can be challenging. In this paper, we introduce a scheme which requires the investigator to specify only simple scaling information. This is used to transform the data to a fixed scale on which a low information prior is constructed. Samples from the posterior with the rescaled data are transformed back for inference on the original scale. The low information prior is selected to provide a wide variety of components for the DPM to generate flexible distributions for the data on the fixed scale. The method can be applied to all DPM models with kernel functions closed under a suitable scaling transformation. Construction of the low information prior, however, is kernel dependent. Using DPM-of-Gaussians and DPM-of-Weibulls models as examples, we show that the method provides accurate estimates of a diverse collection of distributions that includes skewed, multimodal, and highly dispersed members. With the recommended priors, repeated data simulations show performance comparable to that of standard empirical estimates. Finally, we show weak convergence of posteriors with the proposed priors for both kernels considered.

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.

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.

Benjamin Meyer
Jul 3, 2019; 39:5326-5335
Systems/Circuits

JS Taube
Feb 1, 1990; 10:420-435
Articles

Op-ed by Mr Agustín Carstens, General Manager of the BIS, published in The Business Times Singapore, 13 November 2019.

Navigation often requires movement in three-dimensional (3D) space. Recent studies have postulated two different models for how head direction (HD) cells encode 3D space: the rotational plane hypothesis and the dual-axis model. To distinguish these models, we recorded HD cells in female rats while they traveled different routes along both horizontal and vertical surfaces from an elevated platform to the top of a cuboidal apparatus. We compared HD cell preferred firing directions (PFDs) in different planes and addressed the issue of whether HD cell firing is commutative—does the order of the animal's route affect the final outcome of the cell's PFD? Rats locomoted a direct or indirect route from the floor to the cube top via one, two, or three vertical walls. Whereas the rotational plane hypothesis accounted for PFD shifts when the animal traversed horizontal corners, the cell's PFD was better explained by the dual-axis model when the animal traversed vertical corners. Responses also followed the dual-axis model (1) under dark conditions, (2) for passive movement of the rat, (3) following apparatus rotation, (4) for movement around inside vertical corners, and (5) across a 45° outside vertical corner. The order in which the animal traversed the different planes did not affect the outcome of the cell's PFD, indicating that responses were commutative. HD cell peak firing rates were generally equivalent along each surface. These findings indicate that the animal's orientation with respect to gravity plays an important role in determining a cell's PFD, and that vestibular and proprioceptive cues drive these computations.

SIGNIFICANCE STATEMENT Navigating in a three-dimensional (3D) world is a complex task that requires one to maintain a proper sense of orientation relative to both local and global cues. Rodent head direction (HD) cells have been suggested to subserve this sense of orientation, but most HD cell studies have focused on navigation in 2D environments. We investigated the responses of HD cells as rats moved between multiple vertically and horizontally oriented planar surfaces, demonstrating that HD cells align their directional representations to both local (current plane of locomotion) and global (gravity) cues across several experimental conditions, including darkness and passive movement. These findings offer critical insights into the processing of 3D space in the mammalian brain.

One of the first signs of viral infection is body-wide aches and pain. Although this type of pain usually subsides, at the extreme, viral infections can induce painful neuropathies that can last for decades. Neither of these types of pain sensitization is well understood. A key part of the response to viral infection is production of interferons (IFNs), which then activate their specific receptors (IFNRs) resulting in downstream activation of cellular signaling and a variety of physiological responses. We sought to understand how type I IFNs (IFN-α and IFN-β) might act directly on nociceptors in the dorsal root ganglion (DRG) to cause pain sensitization. We demonstrate that type I IFNRs are expressed in small/medium DRG neurons and that their activation produces neuronal hyper-excitability and mechanical pain in mice. Type I IFNs stimulate JAK/STAT signaling in DRG neurons but this does not apparently result in PKR-eIF2α activation that normally induces an anti-viral response by limiting mRNA translation. Rather, type I IFNs stimulate MNK-mediated eIF4E phosphorylation in DRG neurons to promote pain hypersensitivity. Endogenous release of type I IFNs with the double-stranded RNA mimetic poly(I:C) likewise produces pain hypersensitivity that is blunted in mice lacking MNK-eIF4E signaling. Our findings reveal mechanisms through which type I IFNs cause nociceptor sensitization with implications for understanding how viral infections promote pain and can lead to neuropathies.

SIGNIFICANCE STATEMENT It is increasingly understood that pathogens interact with nociceptors to alert organisms to infection as well as to mount early host defenses. Although specific mechanisms have been discovered for diverse bacterial and fungal pathogens, mechanisms engaged by viruses have remained elusive. Here we show that type I interferons, one of the first mediators produced by viral infection, act directly on nociceptors to produce pain sensitization. Type I interferons act via a specific signaling pathway (MNK-eIF4E signaling), which is known to produce nociceptor sensitization in inflammatory and neuropathic pain conditions. Our work reveals a mechanism through which viral infections cause heightened pain sensitivity

A British frigate fires on the USS Constitution - but its shots either miss or rebound off its tough oak hull

A protein shredder that occurs in cell membranes of brain cells apparently also indirectly regulates the fat metabolism.

Curbside pickups offer a bit of hope after months of being shuttered by COVID-19, but while retailers are happy to start getting back to business some are raising questions of fairness and access to opportunity.

Stephen Brandon takes on the role of national director for OM New Zealand after serving as OM’s International Web Manager for six years.

Alan Scott, creative director at Johnstons of Elgin

At a time when there are no entertainment events being held, players in the industry are looking at options to remain relevant.Music video director Rohan Kashwayne Edwards believes there is an even greater demand for music videos during this COVID-19 crisis.

OM Director's Update from Lawrence Tong, International Director

OM Director's Update from Lawrence Tong, International Director

OM Director's Update from Lawrence Tong, International Director

OM Director's Update from Lawrence Tong, International Director

OM Director's Update from Lawrence Tong, International Director

OM Director's Update from Lawrence Tong, International Director