When Scott Gann learned his son Dustin had dyslexia, he was shocked at the school's reaction. No one there wanted to use the word.

The NFL is indeed discussing the possibility of playing games on Saturdays in the event that college football doesn't proceed with a season from September through December. As one source with direct knowledge of the discussions tells PFT, however, the league's strong preference is for college football to happen, as scheduled. No college football season [more]

NCAA President Mark Emmert says the coronavirus is making it unlikely all schools will be ready to begin competing in college sports at the same time. Emmert appeared with Dr. Brian Hainline, the NCAA's chief medical officer, in an interview shown on the NCAA's official Twitter account Friday night. Major football conference commissioners have stated their goal is for all 130 teams in 10 conferences across 41 states to begin the season at the same time.

Classrooms across Washington state sat empty last week, as teachers in seven districts went on strike when negotiations over salaries and benefits failed to result in contracts by the first day of school.

Teachers are still on strike in three Washington school districts, and their fights with the districts are escalating.

After three weeks of teacher strikes dotting the state of Washington, students in all districts are back in school. Teachers in the Tacoma and Battle Ground districts returned to school at the beginning of last week after settling contract agreements.

Teachers in seven districts are striking for higher pay—and Seattle may be next.

Self-actualization (Psychology)

Libraries -- Syria -- Dārayyā (Damascus)

Young people across the globe marched, protested, and signaled calls to action today in the name of climate change. Here's what it looked like.

We asked parents, students, and educators to share what their home learning environments look like as nearly all schools are shut down for extended periods because of the coronavirus pandemic.                          

A look at school reopenings around the world, as school leaders imagine what the new school year may bring.

Berlin : J. Springer, 1897.

Tübingen : Mohr, 1911.

Braunschweig : F. Vieweg, 1889.

Berlin : A. Hirschwald, 1896.

Berlin : A. Goldschmidt, 1890.

Leipzig : F. Deuticke, 1891.

Heidelberg : Winter, 1900.

Wien : W. Braumuller, 1867.

In a case implicating the use of schools as voting locations as well as free speech in education, the justices said Minnesota went too far.

Teachers will receive a 5 percent raise, pending a vote by the state legislature. School will resume Thursday.

The West Virginia Senate trimmed the proposed pay raise for teachers from 5 percent to 4 percent, prompting union officials to declare that the strike will continue indefinitely.

The statewide teacher strike could end today if both chambers of the legislature pass the bill to deliver a 5 percent raise to all school employees.

Teachers across the state will walk out of their classrooms on Tuesday to protest an education bill going through the state legislature.

Lawmakers effectively killed the controversial education bill that had prompted the second statewide strike in two years.

A group of high school students from Anchorage spent spring break at a remote Native Village as part of an unusual cultural exchange program in Alaska. See what they learned.

The traditional school calendar, with its long summer break, is outdated, say supporters of year-round schooling. Nearly 4,000 schools, including those in Holt, Mich., are trying something different.

Unless they come to an agreement with the district, Chicago Teachers Union members plan to stop work Oct. 17. And the fight is about more than just pay.

Teachers in the nation's third-largest school system are fighting for salary increases, class-size caps, and a written commitment for more nurses, social workers, and librarians—as well as investments some say are outside the scope of collective bargaining.

The Florida Panthers finally have a deal with Russian forward Grigori Denisenko. The Panthers announced Tuesday that Denisenko has agreed to a three-year entry level contract - though it remains unclear when that contract will be officially signed and begin to take effect, given the uncertainty of the current NHL season because of the coronavirus pandemic. Denisenko was the 15th overall pick by the Panthers in the 2018 draft.

The next Egor Zamula? A package of size and skill could be there for the Flyers in the 2020 NHL draft with William Wallinder. By Jordan Hall

[Place of publication not identified] : [publisher not identified], [18--?]

[Place of publication not identified] : [publisher not identified], [18--?]

[Place of publication not identified] : [publisher not identified], [18--?]

Trier : WVT Wissenschaftlicher Verlag Trier, 2017.

Rockville, Maryland : National Institute on Drug Abuse, 1979.

Pac-12 Networks' Yogi Roth and Ashley Adamson talk with Hall of Fame player and Pac-12 Networks talent Bill Walton during Thursday's Pac-12 Perspective podcast.

François Bachoc, José Betancourt, Reinhard Furrer, Thierry Klein.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1962--2008.

Abstract:
The asymptotic analysis of covariance parameter estimation of Gaussian processes has been subject to intensive investigation. However, this asymptotic analysis is very scarce for non-Gaussian processes. In this paper, we study a class of non-Gaussian processes obtained by regular non-linear transformations of Gaussian processes. We provide the increasing-domain asymptotic properties of the (Gaussian) maximum likelihood and cross validation estimators of the covariance parameters of a non-Gaussian process of this class. We show that these estimators are consistent and asymptotically normal, although they are defined as if the process was Gaussian. They do not need to model or estimate the non-linear transformation. Our results can thus be interpreted as a robustness of (Gaussian) maximum likelihood and cross validation towards non-Gaussianity. Our proofs rely on two technical results that are of independent interest for the increasing-domain asymptotic literature of spatial processes. First, we show that, under mild assumptions, coefficients of inverses of large covariance matrices decay at an inverse polynomial rate as a function of the corresponding observation location distances. Second, we provide a general central limit theorem for quadratic forms obtained from transformed Gaussian processes. Finally, our asymptotic results are illustrated by numerical simulations.

Patrick L. Combettes, Christian L. Müller.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 207--238.

Abstract:
We introduce a flexible optimization model for maximum likelihood-type estimation (M-estimation) that encompasses and generalizes a large class of existing statistical models, including Huber’s concomitant M-estimator, Owen’s Huber/Berhu concomitant estimator, the scaled lasso, support vector machine regression, and penalized estimation with structured sparsity. The model, termed perspective M-estimation, leverages the observation that convex M-estimators with concomitant scale as well as various regularizers are instances of perspective functions, a construction that extends a convex function to a jointly convex one in terms of an additional scale variable. These nonsmooth functions are shown to be amenable to proximal analysis, which leads to principled and provably convergent optimization algorithms via proximal splitting. We derive novel proximity operators for several perspective functions of interest via a geometrical approach based on duality. We then devise a new proximal splitting algorithm to solve the proposed M-estimation problem and establish the convergence of both the scale and regression iterates it produces to a solution. Numerical experiments on synthetic and real-world data illustrate the broad applicability of the proposed framework.

Cheryl Flynn, Patrick Perry.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 731--768.

Abstract:
Biclustering, the process of simultaneously clustering the rows and columns of a data matrix, is a popular and effective tool for finding structure in a high-dimensional dataset. Many biclustering procedures appear to work well in practice, but most do not have associated consistency guarantees. To address this shortcoming, we propose a new biclustering procedure based on profile likelihood. The procedure applies to a broad range of data modalities, including binary, count, and continuous observations. We prove that the procedure recovers the true row and column classes when the dimensions of the data matrix tend to infinity, even if the functional form of the data distribution is misspecified. The procedure requires computing a combinatorial search, which can be expensive in practice. Rather than performing this search directly, we propose a new heuristic optimization procedure based on the Kernighan-Lin heuristic, which has nice computational properties and performs well in simulations. We demonstrate our procedure with applications to congressional voting records, and microarray analysis.

Andreas Anastasiou, Robert E. Gaunt.

Source: Brazilian Journal of Probability and Statistics, Volume 34, Number 1, 136--149.

Abstract:
We use the delta method and Stein’s method to derive, under regularity conditions, explicit upper bounds for the distributional distance between the distribution of the maximum likelihood estimator (MLE) of a $d$-dimensional parameter and its asymptotic multivariate normal distribution. Our bounds apply in situations in which the MLE can be written as a function of a sum of i.i.d. $t$-dimensional random vectors. We apply our general bound to establish a bound for the multivariate normal approximation of the MLE of the normal distribution with unknown mean and variance.

Sylvia Frühwirth-Schnatter.

Source: Brazilian Journal of Probability and Statistics, Volume 33, Number 4, 706--733.

Abstract:
Finite mixture models and their extensions to Markov mixture and mixture of experts models are very popular in analysing data of various kind. A challenge for these models is choosing the number of components based on marginal likelihoods. The present paper suggests two innovative, generic bridge sampling estimators of the marginal likelihood that are based on constructing balanced importance densities from the conditional densities arising during Gibbs sampling. The full permutation bridge sampling estimator is derived from considering all possible permutations of the mixture labels for a subset of these densities. For the double random permutation bridge sampling estimator, two levels of random permutations are applied, first to permute the labels of the MCMC draws and second to randomly permute the labels of the conditional densities arising during Gibbs sampling. Various applications show very good performance of these estimators in comparison to importance and to reciprocal importance sampling estimators derived from the same importance densities.

George Karabatsos, Fabrizio Leisen.

Source: Statistics Surveys, Volume 12, 66--104.

Abstract:
We are living in the big data era, as current technologies and networks allow for the easy and routine collection of data sets in different disciplines. Bayesian Statistics offers a flexible modeling approach which is attractive for describing the complexity of these datasets. These models often exhibit a likelihood function which is intractable due to the large sample size, high number of parameters, or functional complexity. Approximate Bayesian Computational (ABC) methods provides likelihood-free methods for performing statistical inferences with Bayesian models defined by intractable likelihood functions. The vastity of the literature on ABC methods created a need to review and relate all ABC approaches so that scientists can more readily understand and apply them for their own work. This article provides a unifying review, general representation, and classification of all ABC methods from the view of approximate likelihood theory. This clarifies how ABC methods can be characterized, related, combined, improved, and applied for future research. Possible future research in ABC is then outlined.

The $p$-tensor Curie-Weiss model is a two-parameter discrete exponential family for modeling dependent binary data, where the sufficient statistic has a linear term and a term with degree $p geq 2$. This is a special case of the tensor Ising model and the natural generalization of the matrix Curie-Weiss model, which provides a convenient mathematical abstraction for capturing, not just pairwise, but higher-order dependencies. In this paper we provide a complete description of the limiting properties of the maximum likelihood (ML) estimates of the natural parameters, given a single sample from the $p$-tensor Curie-Weiss model, for $p geq 3$, complementing the well-known results in the matrix ($p=2$) case (Comets and Gidas (1991)). Our results unearth various new phase transitions and surprising limit theorems, such as the existence of a 'critical' curve in the parameter space, where the limiting distribution of the ML estimates is a mixture with both continuous and discrete components. The number of mixture components is either two or three, depending on, among other things, the sign of one of the parameters and the parity of $p$. Another interesting revelation is the existence of certain 'special' points in the parameter space where the ML estimates exhibit a superefficiency phenomenon, converging to a non-Gaussian limiting distribution at rate $N^{frac{3}{4}}$. We discuss how these results can be used to construct confidence intervals for the model parameters and, as a byproduct of our analysis, obtain limit theorems for the sample mean, which provide key insights into the statistical properties of the model.