unc Digest of researches and criticisms bearing on the revision of the British pharmacopoeia, 1898 : 1899 to 1902 inclusive / prepared for the Pharmacopoeia Committee of the General Council of Medical Education and Registration of the United Kingdom by W. Cha By feedproxy.google.com Published On :: London : printed for the Council by Spottiswoode, 1903. Full Article
unc The disinfection of scarlet fever and other infectious diseases by antiseptic inunction / by J. Brendon Curgenven. By feedproxy.google.com Published On :: London : H.K. Lewis, 1891. Full Article
unc Dissertatio inauguralis medica sistens casum singularem carcinomatis uteri cum graviditate conjuncti ... / auctor Bertramus Zeppenfeld. By feedproxy.google.com Published On :: Berolini : Typis Augusti Petschii, 1828. Full Article
unc Dr J. Matthews Duncan's testimonials etc : first series. By feedproxy.google.com Published On :: [Edinburgh] : [publisher not identified], 1870. Full Article
unc Edinburgh and District Water Bill. St. Mary's Loch scheme. Speeches of Bailie Lewis, convener of works committee, Edinburgh and District Water Trust, at the meetings of town council, on 30th May and 1st June, 1871. By feedproxy.google.com Published On :: [Edinburgh] : [publisher not identified], [1871] Full Article
unc An elementary treatise on the function of vision and its anomalies / by Dr. Giraud-Teulon ; translated from the second French edition by Lloyd Owen. By feedproxy.google.com Published On :: London : Bailliere, Tindall, & Cox, 1880. Full Article
unc Epidemic cerebro-spinal meningitis and its relation to other forms of meningitis : a report to the State Board of Health of Massachusetts / Report made by W.T. Councilman, F.B. Mallory, and J.H. Wright. By feedproxy.google.com Published On :: Boston : Wright & Potter Printing Co, 1898. Full Article
unc Ernestus Godofredus Baldinger ... disputationem inauguralem ... Ioan. Conr. Stockar à Neuforn ... de usu cantharidum interno ... habendam annunciat. Praemittitur: historia mercurii et mercurialium medica, et nunc quidem eius pars III. By feedproxy.google.com Published On :: Goettingae : Litteris Ioann. Christ. Dieterich, Acad. Typogr, [1781] Full Article
unc Groups seek injunction to stop Idaho transgender sports ban By feedproxy.google.com Published On :: Fri, 01 May 2020 00:00:00 +0000 Full Article Idaho
unc West Virginia Superintendent Announces Resignation By feedproxy.google.com Published On :: Fri, 23 Sep 2016 00:00:00 +0000 Michael Martirano led the state's schools through dramatic budget cuts, academic challenges, and a state-versus-local battle over school construction. Full Article West_Virginia
unc West Virginia Teacher Strike Ends After Four Days, Governor Announces Pay Raise By feedproxy.google.com Published On :: Tue, 27 Feb 2018 00:00:00 +0000 Teachers will receive a 5 percent raise, pending a vote by the state legislature. School will resume Thursday. Full Article West_Virginia
unc A council of the Olympian gods provides for good government of France under the regency of Marie de' Medici. Engraving by B. Picart, 1707, after J.M. Nattier after Sir P.P. Rubens. By feedproxy.google.com Published On :: A Paris (rue St. Jacques audessus de la rue des Mathurins) : chez G. Duchange graveur du Roy. Avec privilege du Roy, [1708?] Full Article
unc The head of a Turk, surmounted by an eagle holding thunderbolts, and surmounting a strapwork panel announcing the manners and fashions of the Turks. Process print, 1873, after a woodcut, 1553. By feedproxy.google.com Published On :: Full Article
unc Penguins may face ‘tough decisions’ with goalies thanks to salary cap crunch By sports.yahoo.com Published On :: Tue, 05 May 2020 20:05:51 GMT Could they lose one or more of Murray, Jarry, and DeSmith? Full Article article Sports
unc Administrative scheme for the County of London made by the London County Council on 18th December, 1934, for discharging the functions transferred to the Council by Part I of the Local Government Act, 1929, and orders made bu the Minister of Health under By search.wellcomelibrary.org Published On :: England : London County Council, Public Assistance Department, 1935. Full Article
unc Opiate receptor subtypes and brain function / editors, Roger M. Brown, Doris H. Clouet, David P. Friedman. By search.wellcomelibrary.org Published On :: Rockville, Maryland : National Institute on Drug Abuse, 1986. Full Article
unc Evaluating drug information programs / Panel on the Impact of Information on Drug Use and Misuse, National Research Council ; prepared for National Institute of Mental Health. By search.wellcomelibrary.org Published On :: Springfield, Virginia : National Technical Information Service, 1973. Full Article
unc Kobe, Duncan, Garnett headline Basketball Hall of Fame class By sports.yahoo.com Published On :: Sat, 04 Apr 2020 16:12:32 GMT Kobe Bryant was already immortal. Bryant and fellow NBA greats Tim Duncan and Kevin Garnett headlined a nine-person group announced Saturday as this year’s class of enshrinees into the Naismith Memorial Basketball Hall of Fame. Two-time NBA champion coach Rudy Tomjanovich finally got his call, as did longtime Baylor women’s coach Kim Mulkey, 1,000-game winner Barbara Stevens of Bentley and three-time Final Four coach Eddie Sutton. Full Article article Sports
unc WNBA Draft Profile: Productive forward Ruthy Hebard has uncanny handling, scoring, rebounding ability By sports.yahoo.com Published On :: Thu, 09 Apr 2020 21:52:59 GMT Ruthy Hebard, who ranks 2nd in Oregon history in points (2,368) and 3rd in rebounds (1,299), prepares to play in the WNBA following four years in Eugene. Hebard is the Oregon and Pac-12 all-time leader in career field-goal percentage (65.1) and averaged 17.3 points per game and a career-high 9.6 rebounds per game as a senior. Full Article video Sports
unc Posterior contraction and credible sets for filaments of regression functions By projecteuclid.org Published On :: Tue, 14 Apr 2020 22:01 EDT Wei Li, Subhashis Ghosal. Source: Electronic Journal of Statistics, Volume 14, Number 1, 1707--1743.Abstract: A filament consists of local maximizers of a smooth function $f$ when moving in a certain direction. A filamentary structure is an important feature of the shape of an object and is also considered as an important lower dimensional characterization of multivariate data. There have been some recent theoretical studies of filaments in the nonparametric kernel density estimation context. This paper supplements the current literature in two ways. First, we provide a Bayesian approach to the filament estimation in regression context and study the posterior contraction rates using a finite random series of B-splines basis. Compared with the kernel-estimation method, this has a theoretical advantage as the bias can be better controlled when the function is smoother, which allows obtaining better rates. Assuming that $f:mathbb{R}^{2}mapsto mathbb{R}$ belongs to an isotropic Hölder class of order $alpha geq 4$, with the optimal choice of smoothing parameters, the posterior contraction rates for the filament points on some appropriately defined integral curves and for the Hausdorff distance of the filament are both $(n/log n)^{(2-alpha )/(2(1+alpha ))}$. Secondly, we provide a way to construct a credible set with sufficient frequentist coverage for the filaments. We demonstrate the success of our proposed method in simulations and one application to earthquake data. Full Article
unc Sparsely observed functional time series: estimation and prediction By projecteuclid.org Published On :: Thu, 27 Feb 2020 22:04 EST Tomáš Rubín, Victor M. Panaretos. Source: Electronic Journal of Statistics, Volume 14, Number 1, 1137--1210.Abstract: Functional time series analysis, whether based on time or frequency domain methodology, has traditionally been carried out under the assumption of complete observation of the constituent series of curves, assumed stationary. Nevertheless, as is often the case with independent functional data, it may well happen that the data available to the analyst are not the actual sequence of curves, but relatively few and noisy measurements per curve, potentially at different locations in each curve’s domain. Under this sparse sampling regime, neither the established estimators of the time series’ dynamics nor their corresponding theoretical analysis will apply. The subject of this paper is to tackle the problem of estimating the dynamics and of recovering the latent process of smooth curves in the sparse regime. Assuming smoothness of the latent curves, we construct a consistent nonparametric estimator of the series’ spectral density operator and use it to develop a frequency-domain recovery approach, that predicts the latent curve at a given time by borrowing strength from the (estimated) dynamic correlations in the series across time. This new methodology is seen to comprehensively outperform a naive recovery approach that would ignore temporal dependence and use only methodology employed in the i.i.d. setting and hinging on the lag zero covariance. Further to predicting the latent curves from their noisy point samples, the method fills in gaps in the sequence (curves nowhere sampled), denoises the data, and serves as a basis for forecasting. Means of providing corresponding confidence bands are also investigated. A simulation study interestingly suggests that sparse observation for a longer time period may provide better performance than dense observation for a shorter period, in the presence of smoothness. The methodology is further illustrated by application to an environmental data set on fair-weather atmospheric electricity, which naturally leads to a sparse functional time series. Full Article
unc Reduction problems and deformation approaches to nonstationary covariance functions over spheres By projecteuclid.org Published On :: Tue, 11 Feb 2020 22:03 EST Emilio Porcu, Rachid Senoussi, Enner Mendoza, Moreno Bevilacqua. Source: Electronic Journal of Statistics, Volume 14, Number 1, 890--916.Abstract: The paper considers reduction problems and deformation approaches for nonstationary covariance functions on the $(d-1)$-dimensional spheres, $mathbb{S}^{d-1}$, embedded in the $d$-dimensional Euclidean space. Given a covariance function $C$ on $mathbb{S}^{d-1}$, we chase a pair $(R,Psi)$, for a function $R:[-1,+1] o mathbb{R}$ and a smooth bijection $Psi$, such that $C$ can be reduced to a geodesically isotropic one: $C(mathbf{x},mathbf{y})=R(langle Psi (mathbf{x}),Psi (mathbf{y}) angle )$, with $langle cdot ,cdot angle $ denoting the dot product. The problem finds motivation in recent statistical literature devoted to the analysis of global phenomena, defined typically over the sphere of $mathbb{R}^{3}$. The application domains considered in the manuscript makes the problem mathematically challenging. We show the uniqueness of the representation in the reduction problem. Then, under some regularity assumptions, we provide an inversion formula to recover the bijection $Psi$, when it exists, for a given $C$. We also give sufficient conditions for reducibility. Full Article
unc On Mahalanobis Distance in Functional Settings By Published On :: 2020 Mahalanobis distance is a classical tool in multivariate analysis. We suggest here an extension of this concept to the case of functional data. More precisely, the proposed definition concerns those statistical problems where the sample data are real functions defined on a compact interval of the real line. The obvious difficulty for such a functional extension is the non-invertibility of the covariance operator in infinite-dimensional cases. Unlike other recent proposals, our definition is suggested and motivated in terms of the Reproducing Kernel Hilbert Space (RKHS) associated with the stochastic process that generates the data. The proposed distance is a true metric; it depends on a unique real smoothing parameter which is fully motivated in RKHS terms. Moreover, it shares some properties of its finite dimensional counterpart: it is invariant under isometries, it can be consistently estimated from the data and its sampling distribution is known under Gaussian models. An empirical study for two statistical applications, outliers detection and binary classification, is included. The results are quite competitive when compared to other recent proposals in the literature. Full Article
unc Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping By Published On :: 2020 Consider an unknown smooth function $f: [0,1]^d ightarrow mathbb{R}$, and assume we are given $n$ noisy mod 1 samples of $f$, i.e., $y_i = (f(x_i) + eta_i) mod 1$, for $x_i in [0,1]^d$, where $eta_i$ denotes the noise. Given the samples $(x_i,y_i)_{i=1}^{n}$, our goal is to recover smooth, robust estimates of the clean samples $f(x_i) mod 1$. We formulate a natural approach for solving this problem, which works with angular embeddings of the noisy mod 1 samples over the unit circle, inspired by the angular synchronization framework. This amounts to solving a smoothness regularized least-squares problem -- a quadratically constrained quadratic program (QCQP) -- where the variables are constrained to lie on the unit circle. Our proposed approach is based on solving its relaxation, which is a trust-region sub-problem and hence solvable efficiently. We provide theoretical guarantees demonstrating its robustness to noise for adversarial, as well as random Gaussian and Bernoulli noise models. To the best of our knowledge, these are the first such theoretical results for this problem. We demonstrate the robustness and efficiency of our proposed approach via extensive numerical simulations on synthetic data, along with a simple least-squares based solution for the unwrapping stage, that recovers the original samples of $f$ (up to a global shift). It is shown to perform well at high levels of noise, when taking as input the denoised modulo $1$ samples. Finally, we also consider two other approaches for denoising the modulo 1 samples that leverage tools from Riemannian optimization on manifolds, including a Burer-Monteiro approach for a semidefinite programming relaxation of our formulation. For the two-dimensional version of the problem, which has applications in synthetic aperture radar interferometry (InSAR), we are able to solve instances of real-world data with a million sample points in under 10 seconds, on a personal laptop. Full Article
unc Causal Discovery Toolbox: Uncovering causal relationships in Python By Published On :: 2020 This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling. The cdt package implements an end-to-end approach, recovering the direct dependencies (the skeleton of the causal graph) and the causal relationships between variables. It includes algorithms from the `Bnlearn' and `Pcalg' packages, together with algorithms for pairwise causal discovery such as ANM. Full Article
unc Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification By Published On :: 2020 High dimensional data often contain multiple facets, and several clustering patterns can co-exist under different variable subspaces, also known as the views. While multi-view clustering algorithms were proposed, the uncertainty quantification remains difficult --- a particular challenge is in the high complexity of estimating the cluster assignment probability under each view, and sharing information among views. In this article, we propose an approximate Bayes approach --- treating the similarity matrices generated over the views as rough first-stage estimates for the co-assignment probabilities; in its Kullback-Leibler neighborhood, we obtain a refined low-rank matrix, formed by the pairwise product of simplex coordinates. Interestingly, each simplex coordinate directly encodes the cluster assignment uncertainty. For multi-view clustering, we let each view draw a parameterization from a few candidates, leading to dimension reduction. With high model flexibility, the estimation can be efficiently carried out as a continuous optimization problem, hence enjoys gradient-based computation. The theory establishes the connection of this model to a random partition distribution under multiple views. Compared to single-view clustering approaches, substantially more interpretable results are obtained when clustering brains from a human traumatic brain injury study, using high-dimensional gene expression data. Full Article
unc A Convex Parametrization of a New Class of Universal Kernel Functions By Published On :: 2020 The accuracy and complexity of kernel learning algorithms is determined by the set of kernels over which it is able to optimize. An ideal set of kernels should: admit a linear parameterization (tractability); be dense in the set of all kernels (accuracy); and every member should be universal so that the hypothesis space is infinite-dimensional (scalability). Currently, there is no class of kernel that meets all three criteria - e.g. Gaussians are not tractable or accurate; polynomials are not scalable. We propose a new class that meet all three criteria - the Tessellated Kernel (TK) class. Specifically, the TK class: admits a linear parameterization using positive matrices; is dense in all kernels; and every element in the class is universal. This implies that the use of TK kernels for learning the kernel can obviate the need for selecting candidate kernels in algorithms such as SimpleMKL and parameters such as the bandwidth. Numerical testing on soft margin Support Vector Machine (SVM) problems show that algorithms using TK kernels outperform other kernel learning algorithms and neural networks. Furthermore, our results show that when the ratio of the number of training data to features is high, the improvement of TK over MKL increases significantly. Full Article
unc Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes By Published On :: 2020 We present new excess risk bounds for general unbounded loss functions including log loss and squared loss, where the distribution of the losses may be heavy-tailed. The bounds hold for general estimators, but they are optimized when applied to $eta$-generalized Bayesian, MDL, and empirical risk minimization estimators. In the case of log loss, the bounds imply convergence rates for generalized Bayesian inference under misspecification in terms of a generalization of the Hellinger metric as long as the learning rate $eta$ is set correctly. For general loss functions, our bounds rely on two separate conditions: the $v$-GRIP (generalized reversed information projection) conditions, which control the lower tail of the excess loss; and the newly introduced witness condition, which controls the upper tail. The parameter $v$ in the $v$-GRIP conditions determines the achievable rate and is akin to the exponent in the Tsybakov margin condition and the Bernstein condition for bounded losses, which the $v$-GRIP conditions generalize; favorable $v$ in combination with small model complexity leads to $ ilde{O}(1/n)$ rates. The witness condition allows us to connect the excess risk to an 'annealed' version thereof, by which we generalize several previous results connecting Hellinger and Rényi divergence to KL divergence. Full Article
unc Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions By Published On :: 2020 We consider the standard model of distributed optimization of a sum of functions $F(mathbf z) = sum_{i=1}^n f_i(mathbf z)$, where node $i$ in a network holds the function $f_i(mathbf z)$. We allow for a harsh network model characterized by asynchronous updates, message delays, unpredictable message losses, and directed communication among nodes. In this setting, we analyze a modification of the Gradient-Push method for distributed optimization, assuming that (i) node $i$ is capable of generating gradients of its function $f_i(mathbf z)$ corrupted by zero-mean bounded-support additive noise at each step, (ii) $F(mathbf z)$ is strongly convex, and (iii) each $f_i(mathbf z)$ has Lipschitz gradients. We show that our proposed method asymptotically performs as well as the best bounds on centralized gradient descent that takes steps in the direction of the sum of the noisy gradients of all the functions $f_1(mathbf z), ldots, f_n(mathbf z)$ at each step. Full Article
unc The weight function in the subtree kernel is decisive By Published On :: 2020 Tree data are ubiquitous because they model a large variety of situations, e.g., the architecture of plants, the secondary structure of RNA, or the hierarchy of XML files. Nevertheless, the analysis of these non-Euclidean data is difficult per se. In this paper, we focus on the subtree kernel that is a convolution kernel for tree data introduced by Vishwanathan and Smola in the early 2000's. More precisely, we investigate the influence of the weight function from a theoretical perspective and in real data applications. We establish on a 2-classes stochastic model that the performance of the subtree kernel is improved when the weight of leaves vanishes, which motivates the definition of a new weight function, learned from the data and not fixed by the user as usually done. To this end, we define a unified framework for computing the subtree kernel from ordered or unordered trees, that is particularly suitable for tuning parameters. We show through eight real data classification problems the great efficiency of our approach, in particular for small data sets, which also states the high importance of the weight function. Finally, a visualization tool of the significant features is derived. Full Article
unc Town launches new Community Support Hotline By www.eastgwillimbury.ca Published On :: Tue, 28 Apr 2020 23:15:02 GMT Full Article
unc Branching random walks with uncountably many extinction probability vectors By projecteuclid.org Published On :: Mon, 04 May 2020 04:00 EDT Daniela Bertacchi, Fabio Zucca. Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 2, 426--438.Abstract: Given a branching random walk on a set $X$, we study its extinction probability vectors $mathbf{q}(cdot,A)$. Their components are the probability that the process goes extinct in a fixed $Asubseteq X$, when starting from a vertex $xin X$. The set of extinction probability vectors (obtained letting $A$ vary among all subsets of $X$) is a subset of the set of the fixed points of the generating function of the branching random walk. In particular here we are interested in the cardinality of the set of extinction probability vectors. We prove results which allow to understand whether the probability of extinction in a set $A$ is different from the one of extinction in another set $B$. In many cases there are only two possible extinction probability vectors and so far, in more complicated examples, only a finite number of distinct extinction probability vectors had been explicitly found. Whether a branching random walk could have an infinite number of distinct extinction probability vectors was not known. We apply our results to construct examples of branching random walks with uncountably many distinct extinction probability vectors. Full Article
unc Recent developments in complex and spatially correlated functional data By projecteuclid.org Published On :: Mon, 04 May 2020 04:00 EDT Israel Martínez-Hernández, Marc G. Genton. Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 2, 204--229.Abstract: As high-dimensional and high-frequency data are being collected on a large scale, the development of new statistical models is being pushed forward. Functional data analysis provides the required statistical methods to deal with large-scale and complex data by assuming that data are continuous functions, for example, realizations of a continuous process (curves) or continuous random field (surfaces), and that each curve or surface is considered as a single observation. Here, we provide an overview of functional data analysis when data are complex and spatially correlated. We provide definitions and estimators of the first and second moments of the corresponding functional random variable. We present two main approaches: The first assumes that data are realizations of a functional random field, that is, each observation is a curve with a spatial component. We call them spatial functional data . The second approach assumes that data are continuous deterministic fields observed over time. In this case, one observation is a surface or manifold, and we call them surface time series . For these two approaches, we describe software available for the statistical analysis. We also present a data illustration, using a high-resolution wind speed simulated dataset, as an example of the two approaches. The functional data approach offers a new paradigm of data analysis, where the continuous processes or random fields are considered as a single entity. We consider this approach to be very valuable in the context of big data. Full Article
unc On estimating the location parameter of the selected exponential population under the LINEX loss function By projecteuclid.org Published On :: Mon, 03 Feb 2020 04:00 EST Mohd Arshad, Omer Abdalghani. Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 1, 167--182.Abstract: Suppose that $pi_{1},pi_{2},ldots ,pi_{k}$ be $k(geq2)$ independent exponential populations having unknown location parameters $mu_{1},mu_{2},ldots,mu_{k}$ and known scale parameters $sigma_{1},ldots,sigma_{k}$. Let $mu_{[k]}=max {mu_{1},ldots,mu_{k}}$. For selecting the population associated with $mu_{[k]}$, a class of selection rules (proposed by Arshad and Misra [ Statistical Papers 57 (2016) 605–621]) is considered. We consider the problem of estimating the location parameter $mu_{S}$ of the selected population under the criterion of the LINEX loss function. We consider three natural estimators $delta_{N,1},delta_{N,2}$ and $delta_{N,3}$ of $mu_{S}$, based on the maximum likelihood estimators, uniformly minimum variance unbiased estimator (UMVUE) and minimum risk equivariant estimator (MREE) of $mu_{i}$’s, respectively. The uniformly minimum risk unbiased estimator (UMRUE) and the generalized Bayes estimator of $mu_{S}$ are derived. Under the LINEX loss function, a general result for improving a location-equivariant estimator of $mu_{S}$ is derived. Using this result, estimator better than the natural estimator $delta_{N,1}$ is obtained. We also shown that the estimator $delta_{N,1}$ is dominated by the natural estimator $delta_{N,3}$. Finally, we perform a simulation study to evaluate and compare risk functions among various competing estimators of $mu_{S}$. Full Article
unc Nonparametric discrimination of areal functional data By projecteuclid.org Published On :: Mon, 03 Feb 2020 04:00 EST Ahmad Younso. Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 1, 112--126.Abstract: We consider a new nonparametric rule of classification, inspired from the classical moving window rule, that allows for the classification of spatially dependent functional data containing some completely missing curves. We investigate the consistency of this classifier under mild conditions. The practical use of the classifier will be illustrated through simulation studies. Full Article
unc Bayesian inference on power Lindley distribution based on different loss functions By projecteuclid.org Published On :: Mon, 26 Aug 2019 04:00 EDT Abbas Pak, M. E. Ghitany, Mohammad Reza Mahmoudi. Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 894--914.Abstract: This paper focuses on Bayesian estimation of the parameters and reliability function of the power Lindley distribution by using various symmetric and asymmetric loss functions. Assuming suitable priors on the parameters, Bayes estimates are derived by using squared error, linear exponential (linex) and general entropy loss functions. Since, under these loss functions, Bayes estimates of the parameters do not have closed forms we use lindley’s approximation technique to calculate the Bayes estimates. Moreover, we obtain the Bayes estimates of the parameters using a Markov Chain Monte Carlo (MCMC) method. Simulation studies are conducted in order to evaluate the performances of the proposed estimators under the considered loss functions. Finally, analysis of a real data set is presented for illustrative purposes. Full Article
unc Hierarchical modelling of power law processes for the analysis of repairable systems with different truncation times: An empirical Bayes approach By projecteuclid.org Published On :: Mon, 04 Mar 2019 04:00 EST Rodrigo Citton P. dos Reis, Enrico A. Colosimo, Gustavo L. Gilardoni. Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 2, 374--396.Abstract: In the data analysis from multiple repairable systems, it is usual to observe both different truncation times and heterogeneity among the systems. Among other reasons, the latter is caused by different manufacturing lines and maintenance teams of the systems. In this paper, a hierarchical model is proposed for the statistical analysis of multiple repairable systems under different truncation times. A reparameterization of the power law process is proposed in order to obtain a quasi-conjugate bayesian analysis. An empirical Bayes approach is used to estimate model hyperparameters. The uncertainty in the estimate of these quantities are corrected by using a parametric bootstrap approach. The results are illustrated in a real data set of failure times of power transformers from an electric company in Brazil. Full Article
unc The equivalence of dynamic and static asset allocations under the uncertainty caused by Poisson processes By projecteuclid.org Published On :: Mon, 14 Jan 2019 04:01 EST Yong-Chao Zhang, Na Zhang. Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 1, 184--191.Abstract: We investigate the equivalence of dynamic and static asset allocations in the case where the price process of a risky asset is driven by a Poisson process. Under some mild conditions, we obtain a necessary and sufficient condition for the equivalence of dynamic and static asset allocations. In addition, we provide a simple sufficient condition for the equivalence. Full Article
unc Odysseus asleep : uncollected sequences, 1994-2019 By dal.novanet.ca Published On :: Fri, 1 May 2020 19:34:09 -0300 Author: Sanger, Peter, 1943- author.Callnumber: PS 8587 A372 O44 2019ISBN: 9781554472048 Full Article
unc Flexible, boundary adapted, nonparametric methods for the estimation of univariate piecewise-smooth functions By projecteuclid.org Published On :: Tue, 04 Feb 2020 04:00 EST Umberto Amato, Anestis Antoniadis, Italia De Feis. Source: Statistics Surveys, Volume 14, 32--70.Abstract: We present and compare some nonparametric estimation methods (wavelet and/or spline-based) designed to recover a one-dimensional piecewise-smooth regression function in both a fixed equidistant or not equidistant design regression model and a random design model. Wavelet methods are known to be very competitive in terms of denoising and compression, due to the simultaneous localization property of a function in time and frequency. However, boundary assumptions, such as periodicity or symmetry, generate bias and artificial wiggles which degrade overall accuracy. Simple methods have been proposed in the literature for reducing the bias at the boundaries. We introduce new ones based on adaptive combinations of two estimators. The underlying idea is to combine a highly accurate method for non-regular functions, e.g., wavelets, with one well behaved at boundaries, e.g., Splines or Local Polynomial. We provide some asymptotic optimal results supporting our approach. All the methods can handle data with a random design. We also sketch some generalization to the multidimensional setting. To study the performance of the proposed approaches we have conducted an extensive set of simulations on synthetic data. An interesting regression analysis of two real data applications using these procedures unambiguously demonstrates their effectiveness. Full Article
unc Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists By projecteuclid.org Published On :: Tue, 05 Nov 2019 22:03 EST John J. Dziak, Donna L. Coffman, Matthew Reimherr, Justin Petrovich, Runze Li, Saul Shiffman, Mariya P. Shiyko. Source: Statistics Surveys, Volume 13, 150--180.Abstract: Researchers are sometimes interested in predicting a distal or external outcome (such as smoking cessation at follow-up) from the trajectory of an intensively recorded longitudinal variable (such as urge to smoke). This can be done in a semiparametric way via scalar-on-function regression. However, the resulting fitted coefficient regression function requires special care for correct interpretation, as it represents the joint relationship of time points to the outcome, rather than a marginal or cross-sectional relationship. We provide practical guidelines, based on experience with scientific applications, for helping practitioners interpret their results and illustrate these ideas using data from a smoking cessation study. Full Article
unc $M$-functionals of multivariate scatter By projecteuclid.org Published On :: Fri, 20 Mar 2015 09:11 EDT Lutz Dümbgen, Markus Pauly, Thomas Schweizer. Source: Statistics Surveys, Volume 9, 32--105.Abstract: This survey provides a self-contained account of $M$-estimation of multivariate scatter. In particular, we present new proofs for existence of the underlying $M$-functionals and discuss their weak continuity and differentiability. This is done in a rather general framework with matrix-valued random variables. By doing so we reveal a connection between Tyler’s (1987a) $M$-functional of scatter and the estimation of proportional covariance matrices. Moreover, this general framework allows us to treat a new class of scatter estimators, based on symmetrizations of arbitrary order. Finally these results are applied to $M$-estimation of multivariate location and scatter via multivariate $t$-distributions. Full Article
unc Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain By projecteuclid.org Published On :: Mon, 28 Oct 2013 09:06 EDT Sean L. Simpson, F. DuBois Bowman, Paul J. LaurientiSource: Statist. Surv., Volume 7, 1--36.Abstract: Complex functional brain network analyses have exploded over the last decade, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrated system that produces complex behaviors. While the field of statistics has been integral in advancing activation analyses and some connectivity analyses in functional neuroimaging research, it has yet to play a commensurate role in complex network analyses. Fusing novel statistical methods with network-based functional neuroimage analysis will engender powerful analytical tools that will aid in our understanding of normal brain function as well as alterations due to various brain disorders. Here we survey widely used statistical and network science tools for analyzing fMRI network data and discuss the challenges faced in filling some of the remaining methodological gaps. When applied and interpreted correctly, the fusion of network scientific and statistical methods has a chance to revolutionize the understanding of brain function. Full Article
unc Curse of dimensionality and related issues in nonparametric functional regression By projecteuclid.org Published On :: Thu, 14 Apr 2011 08:17 EDT Gery GeenensSource: Statist. Surv., Volume 5, 30--43.Abstract: Recently, some nonparametric regression ideas have been extended to the case of functional regression. Within that framework, the main concern arises from the infinite dimensional nature of the explanatory objects. Specifically, in the classical multivariate regression context, it is well-known that any nonparametric method is affected by the so-called “curse of dimensionality”, caused by the sparsity of data in high-dimensional spaces, resulting in a decrease in fastest achievable rates of convergence of regression function estimators toward their target curve as the dimension of the regressor vector increases. Therefore, it is not surprising to find dramatically bad theoretical properties for the nonparametric functional regression estimators, leading many authors to condemn the methodology. Nevertheless, a closer look at the meaning of the functional data under study and on the conclusions that the statistician would like to draw from it allows to consider the problem from another point-of-view, and to justify the use of slightly modified estimators. In most cases, it can be entirely legitimate to measure the proximity between two elements of the infinite dimensional functional space via a semi-metric, which could prevent those estimators suffering from what we will call the “curse of infinite dimensionality”. References:[1] Ait-Saïdi, A., Ferraty, F., Kassa, K. and Vieu, P. (2008). Cross-validated estimations in the single-functional index model, Statistics, 42, 475–494.[2] Aneiros-Perez, G. and Vieu, P. (2008). 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Two-step estimation of functional linear models with application to longitudinal data, J. Roy. Stat. Soc. B, 62, 303–322.[10] Ferraty, F. and Vieu, P. (2006). Nonparametric Functional Data Analysis, Springer-Verlag, New York.[11] Ferraty, F., Laksaci, A. and Vieu, P. (2006). Estimating Some Characteristics of the Conditional Distribution in Nonparametric Functional Models, Statist. Inf. Stoch. Proc., 9, 47–76.[12] Ferraty, F., Mas, A. and Vieu, P. (2007). Nonparametric regression on functional data: inference and practical aspects, Aust. NZ. J. Stat., 49, 267–286.[13] Ferraty, F., Van Keilegom, I. and Vieu, P. (2010). On the validity of the bootstrap in nonparametric functional regression, Scand. J. Stat., 37, 286–306.[14] Ferraty, F., Laksaci, A., Tadj, A. and Vieu, P. (2010). Rate of uniform consistency for nonparametric estimates with functional variables, J. Stat. Plan. Inf., 140, 335–352.[15] Ferraty, F. and Romain, Y. (2011). 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Stat., 20, 413–430.[23] Rachdi, M. and Vieu, P. (2007). Nonparametric regression for functional data: automatic smoothing parameter selection, J. Stat. Plan. Inf., 137, 2784–2801.[24] Ramsay, J. and Silverman, B.W. (1997). Functional Data Analysis, Springer-Verlag, New York.[25] Ramsay, J. and Silverman, B.W. (2002). Applied functional data analysis; methods and case study, Springer-Verlag, New York.[26] Ramsay, J. and Silverman, B.W. (2005). Functional Data Analysis, 2nd Edition, Springer-Verlag, New York.[27] Stone, C.J. (1982). Optimal global rates of convergence for nonparametric regression, Ann. Stat., 10, 1040–1053.[28] Watson, G.S. (1964). Smooth regression analysis, Sankhya A, 26, 359–372.[29] Yeung, D.T., Chang, H., Xiong, Y., George, S., Kashi, R., Matsumoto, T. and Rigoll, G. (2004). SVC2004: First International Signature Verification Competition, Proceedings of the International Conference on Biometric Authentication (ICBA), Hong Kong, July 2004. Full Article
unc Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection. (arXiv:2005.01889v2 [cs.LG] UPDATED) By arxiv.org Published On :: Building a scalable machine learning system for unsupervised anomaly detection via representation learning is highly desirable. One of the prevalent methods is using a reconstruction error from variational autoencoder (VAE) via maximizing the evidence lower bound. We revisit VAE from the perspective of information theory to provide some theoretical foundations on using the reconstruction error, and finally arrive at a simpler and more effective model for anomaly detection. In addition, to enhance the effectiveness of detecting anomalies, we incorporate a practical model uncertainty measure into the metric. We show empirically the competitive performance of our approach on benchmark datasets. Full Article
unc Fast multivariate empirical cumulative distribution function with connection to kernel density estimation. (arXiv:2005.03246v1 [cs.DS]) By arxiv.org Published On :: This paper revisits the problem of computing empirical cumulative distribution functions (ECDF) efficiently on large, multivariate datasets. Computing an ECDF at one evaluation point requires $mathcal{O}(N)$ operations on a dataset composed of $N$ data points. Therefore, a direct evaluation of ECDFs at $N$ evaluation points requires a quadratic $mathcal{O}(N^2)$ operations, which is prohibitive for large-scale problems. Two fast and exact methods are proposed and compared. The first one is based on fast summation in lexicographical order, with a $mathcal{O}(N{log}N)$ complexity and requires the evaluation points to lie on a regular grid. The second one is based on the divide-and-conquer principle, with a $mathcal{O}(Nlog(N)^{(d-1){vee}1})$ complexity and requires the evaluation points to coincide with the input points. The two fast algorithms are described and detailed in the general $d$-dimensional case, and numerical experiments validate their speed and accuracy. Secondly, the paper establishes a direct connection between cumulative distribution functions and kernel density estimation (KDE) for a large class of kernels. This connection paves the way for fast exact algorithms for multivariate kernel density estimation and kernel regression. Numerical tests with the Laplacian kernel validate the speed and accuracy of the proposed algorithms. A broad range of large-scale multivariate density estimation, cumulative distribution estimation, survival function estimation and regression problems can benefit from the proposed numerical methods. Full Article
unc Classification of pediatric pneumonia using chest X-rays by functional regression. (arXiv:2005.03243v1 [stat.AP]) By arxiv.org Published On :: An accurate and prompt diagnosis of pediatric pneumonia is imperative for successful treatment intervention. One approach to diagnose pneumonia cases is using radiographic data. In this article, we propose a novel parsimonious scalar-on-image classification model adopting the ideas of functional data analysis. Our main idea is to treat images as functional measurements and exploit underlying covariance structures to select basis functions; these bases are then used in approximating both image profiles and corresponding regression coefficient. We re-express the regression model into a standard generalized linear model where the functional principal component scores are treated as covariates. We apply the method to (1) classify pneumonia against healthy and viral against bacterial pneumonia patients, and (2) test the null effect about the association between images and responses. Extensive simulation studies show excellent numerical performance in terms of classification, hypothesis testing, and efficient computation. Full Article
unc Collective Loss Function for Positive and Unlabeled Learning. (arXiv:2005.03228v1 [cs.LG]) By arxiv.org Published On :: People learn to discriminate between classes without explicit exposure to negative examples. On the contrary, traditional machine learning algorithms often rely on negative examples, otherwise the model would be prone to collapse and always-true predictions. Therefore, it is crucial to design the learning objective which leads the model to converge and to perform predictions unbiasedly without explicit negative signals. In this paper, we propose a Collectively loss function to learn from only Positive and Unlabeled data (cPU). We theoretically elicit the loss function from the setting of PU learning. We perform intensive experiments on the benchmark and real-world datasets. The results show that cPU consistently outperforms the current state-of-the-art PU learning methods. Full Article
unc Shortlists announced for 2020 NSW Premier’s Literary Awards By feedproxy.google.com Published On :: Thu, 19 Mar 2020 21:24:32 +0000 Friday 20 March 2020 Contemporary works by leading and emerging Australian writers have been shortlisted for the 2020 NSW Premier's Literary Awards, the State Library of NSW announced today. Full Article
unc 2020 NSW Premier’s Literary Awards announced By feedproxy.google.com Published On :: Sat, 25 Apr 2020 01:29:17 +0000 Sunday 26 April 2020 A total of $295,000 awarded across 12 prize categories. Full Article