The Patriots didn't draft a quarterback, but they signed two who went undrafted. Michigan State quarterback Brian Lewerke's agent revealed a week ago that Lewerke had signed with the Patriots. Patriots coach Bill Belichick wouldn't confirm the team had signed Louisiana Tech quarterback J'mar Smith as well. But Tuesday's official transactions report brought confirmation of [more]

Institute of Ophthalmology., producer.

Caterpillars -- Australia, Southern -- Identification -- Pictorial works.

Organized crime -- New South Wales -- Sydney -- History -- 20th century.

Medical ethics.

Punk rock music -- History and criticism.

195?

An online collection of British magazines, journals and specialty newspapers, 19th Century UK Periodicals provides an in-depth view of British life in the Victorian age. Series 2: Empire addresses the economic as well as the non-mercantile aspects of British expansionism. Wellcome Library also subscribes to Series 1: new readerships.

British Periodicals provides access to the searchable full text of hundreds of periodicals from 1681-1937. Topics covered include literature, music, art, drama, archaeology and architecture.

This is a subset of full text journal backfiles in the arts, humanities, and social sciences from the Periodicals Archive Online collection, dating from 1891-.

Paris : G. Masson, 1890.

Londres : J.-B. Baillière, 1878.

Paris : G. Steinheil, 1893.

London : printed for B. and J. White, 1797.

London : Walton & Maberly, 1866.

Torino : Rosenberg & Sellier, 1890.

London : Longmans, Green, 1894.

Leipzig : Veit, 1898.

Braunschweig : F. Vieweg, 1889.

Bonn : O. Paul, 1895.

Stuttgart : F. Enke, 1878.

Jena : G. Fischer, 1893.

Hamburg : Voss, 1887.

Wien : L.W. Seidel, 1875.

Lipsiae : Sumtibus Baumgaertneri, 1829.

Paris : O. Doin, 1883.

London : Kenny & Co, [1895?]

Leipzig : G. Thieme, 1893.

London : Chapman & Hall, 1908.

New York : R.R. Russell, 1891.

Londres : L’Auteur, 1834.

A Paris (rue des deux portes, St André des arts no. 7) : chez Noel et c.ie, [1820?] ([Paris?] : Litho. de F. Noel)

Paris (rue de la Vannerie a l'image St. Michel) : Dupuis graveur du Roy, [1721]

[19--?]

London (in Cheapside) : Published according to Act of Parliament, by J. Boydell engraver, Sept.r 3rd, 1763.

[London] : Waterlow Bros. & Layton, Limited, [1893]

Rockville, Maryland : U.S. Dept. of Health, Education, and Welfare, Public Health Service, Alcohol, Drug Abuse and Mental Health Administration, 1976.

[London] : [publisher not identified], [2019]

[London] : [publisher not identified], [2019]

Beverly Hills : Sage Publications, 1976.

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.

María F. Gil–Leyva, Ramsés H. Mena, Theodoros Nicoleris.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1479--1507.

Abstract:
A new class of nonparametric prior distributions, termed Beta-Binomial stick-breaking process, is proposed. By allowing the underlying length random variables to be dependent through a Beta marginals Markov chain, an appealing discrete random probability measure arises. The chain’s dependence parameter controls the ordering of the stick-breaking weights, and thus tunes the model’s label-switching ability. Also, by tuning this parameter, the resulting class contains the Dirichlet process and the Geometric process priors as particular cases, which is of interest for MCMC implementations. Some properties of the model are discussed and a density estimation algorithm is proposed and tested with simulated datasets.

This work is concerned with the non-negative rank-1 robust principal component analysis (RPCA), where the goal is to recover the dominant non-negative principal components of a data matrix precisely, where a number of measurements could be grossly corrupted with sparse and arbitrary large noise. Most of the known techniques for solving the RPCA rely on convex relaxation methods by lifting the problem to a higher dimension, which significantly increase the number of variables. As an alternative, the well-known Burer-Monteiro approach can be used to cast the RPCA as a non-convex and non-smooth $ell_1$ optimization problem with a significantly smaller number of variables. In this work, we show that the low-dimensional formulation of the symmetric and asymmetric positive rank-1 RPCA based on the Burer-Monteiro approach has benign landscape, i.e., 1) it does not have any spurious local solution, 2) has a unique global solution, and 3) its unique global solution coincides with the true components. An implication of this result is that simple local search algorithms are guaranteed to achieve a zero global optimality gap when directly applied to the low-dimensional formulation. Furthermore, we provide strong deterministic and probabilistic guarantees for the exact recovery of the true principal components. In particular, it is shown that a constant fraction of the measurements could be grossly corrupted and yet they would not create any spurious local solution.

The horseshoe prior is frequently employed in Bayesian analysis of high-dimensional models, and has been shown to achieve minimax optimal risk properties when the truth is sparse. While optimization-based algorithms for the extremely popular Lasso and elastic net procedures can scale to dimension in the hundreds of thousands, algorithms for the horseshoe that use Markov chain Monte Carlo (MCMC) for computation are limited to problems an order of magnitude smaller. This is due to high computational cost per step and growth of the variance of time-averaging estimators as a function of dimension. We propose two new MCMC algorithms for computation in these models that have significantly improved performance compared to existing alternatives. One of the algorithms also approximates an expensive matrix product to give orders of magnitude speedup in high-dimensional applications. We prove guarantees for the accuracy of the approximate algorithm, and show that gradually decreasing the approximation error as the chain extends results in an exact algorithm. The scalability of the algorithm is illustrated in simulations with problem size as large as $N=5,000$ observations and $p=50,000$ predictors, and an application to a genome-wide association study with $N=2,267$ and $p=98,385$. The empirical results also show that the new algorithm yields estimates with lower mean squared error, intervals with better coverage, and elucidates features of the posterior that were often missed by previous algorithms in high dimensions, including bimodality of posterior marginals indicating uncertainty about which covariates belong in the model.

Danilo Covaes Nogarotto, Caio Lucidius Naberezny Azevedo, Jorge Luis Bazán.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 2, 304--322.

Abstract:
The interest on the analysis of the zero–one augmented beta regression (ZOABR) model has been increasing over the last few years. In this work, we developed a Bayesian inference for the ZOABR model, providing some contributions, namely: we explored the use of Jeffreys-rule and independence Jeffreys prior for some of the parameters, performing a sensitivity study of prior choice, comparing the Bayesian estimates with the maximum likelihood ones and measuring the accuracy of the estimates under several scenarios of interest. The results indicate, in a general way, that: the Bayesian approach, under the Jeffreys-rule prior, was as accurate as the ML one. Also, different from other approaches, we use the predictive distribution of the response to implement Bayesian residuals. To further illustrate the advantages of our approach, we conduct an analysis of a real psychometric data set including a Bayesian residual analysis, where it is shown that misleading inference can be obtained when the data is transformed. That is, when the zeros and ones are transformed to suitable values and the usual beta regression model is considered, instead of the ZOABR model. Finally, future developments are discussed.

Uttam Bandyopadhyay, Shirsendu Mukherjee, Atanu Biswas.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 2, 291--303.

Abstract:
In adaptive crossover design, our goal is to allocate more patients to a promising treatment sequence. The present work contains a very simple three period crossover design for two competing treatments where the allocation in period 3 is done on the basis of the data obtained from the first two periods. Assuming normality of response variables we use a reliability functional for the choice between two treatments. We calculate the allocation proportions and their standard errors corresponding to the possible treatment combinations. We also derive some asymptotic results and provide solutions on related inferential problems. Moreover, the proposed procedure is compared with a possible competitor. Finally, we use a data set to illustrate the applicability of the proposed design.

Saralees Nadarajah, Yuancheng Si.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 1, 183--187.

Abstract:
Da Paz, Balakrishnan and Bazan [Braz. J. Probab. Stat. 33 (2019), 455–479] introduced the L-logistic distribution, studied its properties including estimation issues and illustrated a data application. This note derives a closed form expression for moment properties of the distribution. Some computational issues are discussed.