The state, which earned a D-plus, scored low on the Chance for Success Index, which tracks a host of socioeconomic factors that can affect the educational environment.

This Quality Counts 2020 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

The court revived a lawsuit claiming that Mississippi's lack of a "uniform" education system violates the 1868 federal law that readmitted the state to the Union.

Victoria Petersen, of the Peninsula Clarion on the Kenai Peninsula, will report on the challenges of rural education, especially in a state as vast as Alaska.

London (No. 4, Hanover Street) : Published ... for the proprietor by T.G. March ; [London] (Threadneedle Street) : Sold also by F. G. Moon ; [London] (Strand) : [Sold also by] Ackermann & Co., April 10, 1842 ([London?] : Printed by R. Lloyd)

London (Poets Gallery Fleet Street) : Publish'd ... by Thos. Macklin, Novr. 30. 1799.

London (46 Pall Mall) : Pub.d by R. Bowyer & M. Parkes, 1st Jan.y 1830.

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

[Rome?] : [publisher not identified], [1807?]

London (6 Pall Mall) : Messrs Moon, Boys & Graves ; Manchester : J.C. Grundy, July 2 1832.

A Paris (rue St Denis No. 214) : chez Bance aîné, [1810?]

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

London : Published for the proprietors, [between 1800 and 1899]

Paris (28 et 29 rue St. Sulpice) : Maison Bouasse-Lebel, Imp. Edit. et ancienne Maison Basset reunies, [1851?]

[London] (17, Church Street, Soho) : Published ... by M.A. Bourlier, August, 1813.

[London], [1795]

[19--?]

[London] (No. 7 Weymouth Street, Portland Place) : Published as the Act directs ... by R. Duppa, Jan.y 1st 1802.

London (Old Bond Street) : published by John Warren ; [London] (Ave Maria Lane) : G & W.B. Whittaker, [between 1800 and 1899]

London (Pater Noster Row) : G.G. & J. Robinson, Nov.r 1st 1798.

[London?], [between 1700 and 1799?]

London : James Nisbet & Co., MDCCCLXXX [1880]

One of many possible plans if the league can resume play.

The Washington Capitals on Friday placed Brendan Leipsic on unconditional waivers to terminate his contract after he made disparaging comments about women and teammates in a private social media chat. In a conversation involving his brother and Florida Panthers minor leaguer Jack Rodewald, Leipsic commented on the physical appearances of Vancouver forward Tanner Pearson's wife and Edmonton captain Connor McDavid's girlfriend. The NHL called it ''inexcusable conduct'' and said it would address the matter with the Capitals and Panthers.

The Washington Capitals have placed former Winterhawks wing Brendan Leipsic on unconditional waivers with the intention of his contract being terminated after private messages revealed misogynistic comments. > The Washington Capitals have placed Brendan Leipsic on unconditional waivers for purposes of terminating his contract.https://t.co/UnADibu2yQ

The Washington Capitals decided to part ways with forward Brendan Leipsic on Friday after he made vulgar and disparaging comments on an social media chat group. The 25-year old Canadian was placed on waivers by the NHL club after his Instagram group chat messages were leaked earlier this week. The chat included inappropriate remarks about Edmonton Oilers forward Connor McDavid's girlfriend and the wife of Vancouver Canucks player Tanner Pearson.

Washington announced it is terminating the contract of forward Brendan Leipsic after his mysogynistic comments on Instagram were leaked.

In three days of rock climbing, orienteering, and other challenging outdoor experiences, principals get to examine their own—and others’—strengths and weaknesses as leaders.

Philippines

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

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

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

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

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

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

Texas moved quickly to hire a new women's basketball coach, luring Vic Schaefer away from powerhouse Mississippi State on Sunday. Texas athletic director Chris Del Conte announced the move by tweeting a picture of himself with Schaefer and his family holding up the “Hook'em Horns” hand signal. The move comes just two days after Texas dismissed eight-year coach Karen Aston, who had only one losing season in her tenure and had led the Longhorns to the Sweet 16 or farther four times.

Mississippi State hired former Old Dominion women’s basketball coach Nikki McCray-Penson to replace Vic Schaefer as the Bulldogs’ head coach. Athletic director John Cohen called McCray-Penson “a proven winner who will lead one of the best programs in the nation” on the department’s website. McCray-Penson, a former Tennessee star and Women’s Basketball Hall of Famer, said it’s been a dream to coach in the Southeastern Conference and she’s “grateful and blessed for this incredible honor and opportunity.”

The Pac-12 Student-Athlete Advisory Committee voted to award the Oregon State women’s basketball team with the Pac-12 Sportsmanship Award for the 2019-20 season, honoring their character and sportsmanship before a rivalry game against Oregon in Jan. 2020 -- the day Kobe Bryant, his daughter, Gigi, and seven others passed away in a helicopter crash in Southern California. In the above video, Aleah Goodman and Madison Washington share how the teams came together as one in a circle of prayer before the game.

On the day Kobe Bryant suddenly passed away, the Beavers embraced their rivals at midcourt in a moment of strength to support the Ducks, many of whom had personal connections to Bryant and his daughter, Gigi. For this, Oregon State is the 2020 recipient of the Pac-12 Sportsmanship Award.

Heiko Werner, Hajo Holzmann, Pierre Vandekerkhove.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1816--1871.

Abstract:
We investigate a flexible two-component semiparametric mixture of regressions model, in which one of the conditional component distributions of the response given the covariate is unknown but assumed symmetric about a location parameter, while the other is specified up to a scale parameter. The location and scale parameters together with the proportion are allowed to depend nonparametrically on covariates. After settling identifiability, we provide local M-estimators for these parameters which converge in the sup-norm at the optimal rates over Hölder-smoothness classes. We also introduce an adaptive version of the estimators based on the Lepski-method. Sup-norm bounds show that the local M-estimator properly estimates the functions globally, and are the first step in the construction of useful inferential tools such as confidence bands. In our analysis we develop general results about rates of convergence in the sup-norm as well as adaptive estimation of local M-estimators which might be of some independent interest, and which can also be applied in various other settings. We investigate the finite-sample behaviour of our method in a simulation study, and give an illustration to a real data set from bioinformatics.

Ben Jones, Andreas Artemiou, Bing Li.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1--23.

Abstract:
We give a probabilistic analysis of a phenomenon in statistics which, until recently, has not received a convincing explanation. This phenomenon is that the leading principal components tend to possess more predictive power for a response variable than lower-ranking ones despite the procedure being unsupervised. Our result, in its most general form, shows that the phenomenon goes far beyond the context of linear regression and classical principal components — if an arbitrary distribution for the predictor $X$ and an arbitrary conditional distribution for $Yvert X$ are chosen then any measureable function $g(Y)$, subject to a mild condition, tends to be more correlated with the higher-ranking kernel principal components than with the lower-ranking ones. The “arbitrariness” is formulated in terms of unitary invariance then the tendency is explicitly quantified by exploring how unitary invariance relates to the Cauchy distribution. The most general results, for technical reasons, are shown for the case where the kernel space is finite dimensional. The occurency of this tendency in real world databases is also investigated to show that our results are consistent with observation.

Benjamin Colling, Ingrid Van Keilegom.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 769--800.

Abstract:
Consider the following semiparametric transformation model $Lambda_{ heta }(Y)=m(X)+varepsilon $, where $X$ is a $d$-dimensional covariate, $Y$ is a univariate response variable and $varepsilon $ is an error term with zero mean and independent of $X$. We assume that $m$ is an unknown regression function and that ${Lambda _{ heta }: heta inTheta }$ is a parametric family of strictly increasing functions. Our goal is to develop two new estimators of the transformation parameter $ heta $. The main idea of these two estimators is to minimize, with respect to $ heta $, the $L_{2}$-distance between the transformation $Lambda _{ heta }$ and one of its fully nonparametric estimators. We consider in particular the nonparametric estimator based on the least-absolute deviation loss constructed in Colling and Van Keilegom (2019). We establish the consistency and the asymptotic normality of the two proposed estimators of $ heta $. We also carry out a simulation study to illustrate and compare the performance of our new parametric estimators to that of the profile likelihood estimator constructed in Linton et al. (2008).

Principal component analysis (PCA) is a well-established tool in machine learning and data processing. The principal axes in PCA were shown to be equivalent to the maximum marginal likelihood estimator of the factor loading matrix in a latent factor model for the observed data, assuming that the latent factors are independently distributed as standard normal distributions. However, the independence assumption may be unrealistic for many scenarios such as modeling multiple time series, spatial processes, and functional data, where the outcomes are correlated. In this paper, we introduce the generalized probabilistic principal component analysis (GPPCA) to study the latent factor model for multiple correlated outcomes, where each factor is modeled by a Gaussian process. Our method generalizes the previous probabilistic formulation of PCA (PPCA) by providing the closed-form maximum marginal likelihood estimator of the factor loadings and other parameters. Based on the explicit expression of the precision matrix in the marginal likelihood that we derived, the number of the computational operations is linear to the number of output variables. Furthermore, we also provide the closed-form expression of the marginal likelihood when other covariates are included in the mean structure. We highlight the advantage of GPPCA in terms of the practical relevance, estimation accuracy and computational convenience. Numerical studies of simulated and real data confirm the excellent finite-sample performance of the proposed approach.