Wiesbaden : J.F. Bergmann, 1891.

Berlin : J. Springer, 1887.

Berlin : Hirschwald, 1873.

Stuttgart : F. Enke, 1878.

Wien : Urban & Schwarzenberg, 1877.

Berlin : A. Hirschwald, 1875.

Weimar : F. Voigt, 1874-1882.

Wiesbaden : J.F. Bergmann, 1899.

Wien : L.W. Seidel, 1875.

Hannover : Schmorl & von Seefeld, 1866.

Berlin : A. Hirschwald, 1875.

Muenchen : Müller & Steinicke, 1911.

Berlin : J. Springer, 1899.

Wiesbaden : C. Limbarth, 1882.

Berlin : A. Hirschwald, 1879.

Bonn : E. Strauss, 1899.

Breslau : W.G. Korn, 1833.

[Germany] : [publisher not identified], 1845.

Leipzig : Wilhelm Engelmann, 1897.

Wiesbaden : J.F. Bergmann, 1891.

London : printed for the author, 1832.

Mannheim : T. Loeffler, 1812.

Wien : SafÏar, 1905.

Berlin : Hirschwald, 1886.

[London] : [Wellcome Trust], [2009]

Ridly Greig, son of Flyers scout Mark Greig, is a center with 200-foot bite and should be available for the club in the first round of the 2020 NHL draft. By Jordan Hall

Rockville, Maryland : National Institute on Drug Abuse, 1988.

[Dorchester] : Dorset Community NHS Trust, 1997.

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.

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.

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.

Wesley Bertoli, Katiane S. Conceição, Marinho G. Andrade, Francisco Louzada.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 826--860.

Abstract:
The primary goal of this paper is to introduce the zero-modified Poisson–Lindley regression model as an alternative to model overdispersed count data exhibiting inflation or deflation of zeros in the presence of covariates. The zero-modification is incorporated by considering that a zero-truncated process produces positive observations and consequently, the proposed model can be fitted without any previous information about the zero-modification present in a given dataset. A fully Bayesian approach based on the g-prior method has been considered for inference concerns. An intensive Monte Carlo simulation study has been conducted to evaluate the performance of the developed methodology and the maximum likelihood estimators. The proposed model was considered for the analysis of a real dataset on the number of bids received by $126$ U.S. firms between 1978–1985, and the impact of choosing different prior distributions for the regression coefficients has been studied. A sensitivity analysis to detect influential points has been performed based on the Kullback–Leibler divergence. A general comparison with some well-known regression models for discrete data has been presented.

Application and use of deep learning algorithms for different healthcare applications is gaining interest at a steady pace. However, use of such algorithms can prove to be challenging as they require large amounts of training data that capture different possible variations. This makes it difficult to use them in a clinical setting since in most health applications researchers often have to work with limited data. Less data can cause the deep learning model to over-fit. In this paper, we ask how can we use data from a different environment, different use-case, with widely differing data distributions. We exemplify this use case by using single-sensor accelerometer data from healthy subjects performing activities of daily living - ADLs (source dataset), to extract features relevant to multi-sensor accelerometer gait data (target dataset) for Parkinson's disease classification. We train the pre-trained model using the source dataset and use it as a feature extractor. We show that the features extracted for the target dataset can be used to train an effective classification model. Our pre-trained source model consists of a convolutional autoencoder, and the target classification model is a simple multi-layer perceptron model. We explore two different pre-trained source models, trained using different activity groups, and analyze the influence the choice of pre-trained model has over the task of Parkinson's disease classification.

Didier Chételat, Martin T. Wells.

Source: The Annals of Statistics, Volume 47, Number 5, 2639--2670.

Abstract:
We study the behavior of a real $p$-dimensional Wishart random matrix with $n$ degrees of freedom when $n,p ightarrowinfty$ but $p/n ightarrow0$. We establish the existence of phase transitions when $p$ grows at the order $n^{(K+1)/(K+3)}$ for every $Kinmathbb{N}$, and derive expressions for approximating densities between every two phase transitions. To do this, we make use of a novel tool we call the $mathcal{F}$-conjugate of an absolutely continuous distribution, which is obtained from the Fourier transform of the square root of its density. In the case of the normalized Wishart distribution, this represents an extension of the $t$-distribution to the space of real symmetric matrices.

Integration software. Middleware is the term coined to describe software that connects other software together. In the early days of computing, each software system in an organization was a separate 'stovepipe' or 'silo' that stood alone and was dedicated to automating a specific part of the business or its IT operations. Middleware aims to connect those individual islands of automation, both within an enterprise and out to external systems (for example at customers and suppliers). For a long while, middleware has either been custom coded for individual projects or has come in the form of proprietary products or suites, most notably as enterprise application integration (EAI) software. The emergence of industry-agreed web services specifications is now enabling convergence on standards-based distributed middleware, which in theory should allow all systems to automatically connect together on demand.

Eduard Belitser, Nurzhan Nurushev.

Source: Bernoulli, Volume 26, Number 1, 191--225.

Abstract:
In the general signal$+$noise (allowing non-normal, non-independent observations) model, we construct an empirical Bayes posterior which we then use for uncertainty quantification for the unknown, possibly sparse, signal. We introduce a novel excessive bias restriction (EBR) condition, which gives rise to a new slicing of the entire space that is suitable for uncertainty quantification. Under EBR and some mild exchangeable exponential moment condition on the noise, we establish the local (oracle) optimality of the proposed confidence ball. Without EBR, we propose another confidence ball of full coverage, but its radius contains an additional $sigma n^{1/4}$-term. In passing, we also get the local optimal results for estimation , posterior contraction problems, and the problem of weak recovery of sparsity structure . Adaptive minimax results (also for the estimation and posterior contraction problems) over various sparsity classes follow from our local results.

Boeing CEO Dave Calhoun said the company was aiming to resume production this month, despite the ongoing grounding and coronavirus pandemic.

The infections and deaths are spread across roughly two farms and 189 meat and processed food factories.

Yuan Ke, Stanislav Minsker, Zhao Ren, Qiang Sun, Wen-Xin Zhou.

Source: Statistical Science, Volume 34, Number 3, 454--471.

Abstract:
We provide a survey of recent results on covariance estimation for heavy-tailed distributions. By unifying ideas scattered in the literature, we propose user-friendly methods that facilitate practical implementation. Specifically, we introduce elementwise and spectrumwise truncation operators, as well as their $M$-estimator counterparts, to robustify the sample covariance matrix. Different from the classical notion of robustness that is characterized by the breakdown property, we focus on the tail robustness which is evidenced by the connection between nonasymptotic deviation and confidence level. The key insight is that estimators should adapt to the sample size, dimensionality and noise level to achieve optimal tradeoff between bias and robustness. Furthermore, to facilitate practical implementation, we propose data-driven procedures that automatically calibrate the tuning parameters. We demonstrate their applications to a series of structured models in high dimensions, including the bandable and low-rank covariance matrices and sparse precision matrices. Numerical studies lend strong support to the proposed methods.

Vladimir Koltchinskii, Richard Nickl, Philippe Rigollet.

Source: Statistical Science, Volume 34, Number 1, 169--175.

Abstract:
Richard Mansfield Dudley (Dick Dudley) was born in 1938. He received the A.B. from Harvard in 1952 and the Ph.D. from Princeton in 1962 (under the supervision of Gilbert Hunt and Edward Nelson). Following an appointment at UC Berkeley as an assistant professor, he joined the Department of Mathematics at MIT in 1967. Dick Dudley has made fundamental contributions to the theory of Gaussian processes and Probability in Banach Spaces. Among his major achievements is the development of a general framework for empirical processes theory, in particular, for uniform central limit theorems. These results have had and continue having tremendous impact in contemporary statistics and in mathematical foundations of machine learning. A more extensive biographical sketch is contained in the preface to the Selected works of R. M. Dudley (editors: E. Giné, V. Koltchinskii and R. Norvaisa) published in 2010. This conversation took place (mostly, via email) in the fall of 2017.

Keep your feet comfy and your wallet fat.

WT Newsome
Jun 1, 1988; 8:2201-2211
Articles

Bees pollinate a third of what we eat and play a vital role in sustaining the planet’s ecosystems. Some 84% of the crops grown for human consumption need bees or other insects to pollinate them to increase their yields and quality. Bee pollination not only results in a higher number of fruits, berries or seeds, it may also give a [...]

On February 9, 1945, two submarines are poised to engage in a direct, underwater confrontation. It's the first - and only - underwater submarine dog fight

The cats also cross the road an average of 4.5 times in six days, putting themselves in danger

An online exhibition from the National WWI Museum and Memorial features recipes detailed in 1918 cookbook

The nearly 2,800-mile-long walking route runs all the way around the English coast