Idaho's highest court says teachers are not public figures and a former teacher need not prove that news outlets acted with malice in stories about sexual misconduct.

Many are whipsawed by shutdown recommendations aimed at stemming the coronavirus, and the logistical and financial consequences of those actions.

While thousands of students in wildfire-ravaged Northern California resumed classes last week, thousands of others in Alaska stayed home after a 7.0 magnitude earthquake struck Nov. 30.

A Paris : Chez Iean Dhourry, au bout du Pont-Neuf, près les Augustins, à l'Image S. Iean, M. DC. LXI. [1661]

Bruxelles (rue de l'Industrie) : Félix Callewaert père, éditeur, 1875.

[Rome]

London (Gt. Newport Street) : Published by John P. Thompson, Jan.y 1 1802.

[London], [1795]

[London?]

[19--?]

In Roma [Rome] (alla Pace) : Gio. Iacomo Rossi le stampa ... cum privil. S.P.

[Paris] : Calcographie du Louvre, Musées Imperiaux, [1849?]

State data show more than 200 of Michigan's private schools have closed over the past decade, and many school leaders are blaming a shrinking student population, fewer resources, and rising costs.

After an investigation found Illinois schools put children in "isolated timeout" for illegal reasons, a group of the state's federal lawmakers have asked U.S. Secretary of Education to ban seclusion in schools nationwide.

Troy Brouwer scored the game-winning goal as the Capitals beat the Blackhawks at Nationals Park on Jan. 1, 2015. Relive the excitement with Monday night's replay.

The justices declined to hear the appeal of the Ferguson-Florissant district over its at-large board elections, which lower courts invalidated as violating the Voting Rights Act.

Calcutta, India : Cleanair, Campaign for Smoke-free Environment, [198-?]

[London, United Kingdom?] : Health Visitor, 1993.

Rockville, Maryland : National Institute on Drug Abuse, 1975.

Rockville, Maryland : National Institute on Drug Abuse, 1976.

Rockville, Maryland : National Institute on Drug Abuse, 1981.

Rockville, Maryland : National Institute on Drug Abuse, 1985.

Rockville, Maryland : National Institute on Drug Abuse, 1986.

Rockville, Maryland : National Institute on Drug Abuse, 1987.

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

Rockville, Maryland : National Institute on Drug Abuse, 1986.

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

Rockville, Maryland : National Institute on Drug Abuse, 1980.

London : London Research Centre, 1991.

London : European Collaborating Centres in Addiction Studies, 1998.

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.

A look at the newest members of the Naismith Memorial Basketball Hall of Fame, announced on Saturday:

Sabrina Ionescu wins the Wooden Award for the second year in a row, becoming the fifth in the trophy's history to win in back-to-back seasons. With the honor, she completes a complete sweep of the national postseason player of the year awards. As a senior, Ionescu matched her own single-season mark with eight triple-doubles in 2019-20, and she was incredibly efficient from the field with a career-best 51.8 field goal percentage.

UCLA guard Japreece Dean is primed to shine at the next level as she heads to the WNBA Draft in April. The do-it-all point-woman was an All-Pac-12 honoree last season, and one of only seven D-1 hoopers with at least 13 points and 5.5 assists per game.

Pac-12 Networks' Mike Yam has a conversation with UCLA's Natalie Chou during Wednesday's "Pac-12 Perspective" podcast. Chou reflects on her role models, passion for basketball and how her mom has made a big impact on her hoops career.

Wenjing Wang, Xin Zhang, Qing Mai.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 82--109.

Abstract:
Clustering analysis is an important unsupervised learning technique in multivariate statistics and machine learning. In this paper, we propose a set of new mixture models called CLEMM (in short for Clustering with Envelope Mixture Models) that is based on the widely used Gaussian mixture model assumptions and the nascent research area of envelope methodology. Formulated mostly for regression models, envelope methodology aims for simultaneous dimension reduction and efficient parameter estimation, and includes a very recent formulation of envelope discriminant subspace for classification and discriminant analysis. Motivated by the envelope discriminant subspace pursuit in classification, we consider parsimonious probabilistic mixture models where the cluster analysis can be improved by projecting the data onto a latent lower-dimensional subspace. The proposed CLEMM framework and the associated envelope-EM algorithms thus provide foundations for envelope methods in unsupervised and semi-supervised learning problems. Numerical studies on simulated data and two benchmark data sets show significant improvement of our propose methods over the classical methods such as Gaussian mixture models, K-means and hierarchical clustering algorithms. An R package is available at https://github.com/kusakehan/CLEMM.

Claudia Wehrhahn, Samuel Leonard, Abel Rodriguez, Tatiana Xifara.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1449--1478.

Abstract:
Identifying disease clusters (areas with an unusually high incidence of a particular disease) is a common problem in epidemiology and public health. We describe a Bayesian nonparametric mixture model for disease clustering that constrains clusters to be made of adjacent areal units. This is achieved by modifying the exchangeable partition probability function associated with the Ewen’s sampling distribution. We call the resulting prior the Restricted Chinese Restaurant Process, as the associated full conditional distributions resemble those associated with the standard Chinese Restaurant Process. The model is illustrated using synthetic data sets and in an application to oral cancer mortality in Germany.

Anja Janßen, Phyllis Wan.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1211--1233.

Abstract:
The $k$-means clustering algorithm and its variant, the spherical $k$-means clustering, are among the most important and popular methods in unsupervised learning and pattern detection. In this paper, we explore how the spherical $k$-means algorithm can be applied in the analysis of only the extremal observations from a data set. By making use of multivariate extreme value analysis we show how it can be adopted to find “prototypes” of extremal dependence and derive a consistency result for our suggested estimator. In the special case of max-linear models we show furthermore that our procedure provides an alternative way of statistical inference for this class of models. Finally, we provide data examples which show that our method is able to find relevant patterns in extremal observations and allows us to classify extremal events.

Alessandro Casa, José E. Chacón, Giovanna Menardi.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 835--856.

Abstract:
Density-based clustering relies on the idea of linking groups to some specific features of the probability distribution underlying the data. The reference to a true, yet unknown, population structure allows framing the clustering problem in a standard inferential setting, where the concept of ideal population clustering is defined as the partition induced by the true density function. The nonparametric formulation of this approach, known as modal clustering, draws a correspondence between the groups and the domains of attraction of the density modes. Operationally, a nonparametric density estimate is required and a proper selection of the amount of smoothing, governing the shape of the density and hence possibly the modal structure, is crucial to identify the final partition. In this work, we address the issue of density estimation for modal clustering from an asymptotic perspective. A natural and easy to interpret metric to measure the distance between density-based partitions is discussed, its asymptotic approximation explored, and employed to study the problem of bandwidth selection for nonparametric modal clustering.

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.