This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

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

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

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

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

The legislature sent a proposal last week to Gov. Jim Justice's desk to shutter the state's advisory education and the arts department, leaving the Gayle Manchin and her staff in the lurch.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

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

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

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

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

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

Se vend à Paris (rue St. Jacques au dessus de la rue des Mathurins) : chez le S.r Duchange graveur ordinaire du Roy. Avec privilege du Roy, [1708?]

A Paris (rue St. Jacques audessus de la rue des Mathurins) : chez G. Duchange graveur du Roy. Avec privilege du Roy, [1708?]

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

[London?] : [Illustrated London News?], [between 1850 and 1870?]

London (No. 18 & 19 Southampton Place, Euston Square, New Road) : Published ... by the proprietors [E. & W. Finden], May 1, 1839.

[London?] : Cassell & Company Limited, [between 1800 and 1899]

[London] (No 69 St Paul's Churchyard) : Printed for Carington Bowles ; [London] (opposite Fetter Lane, Fleet Street) : Robt. Sayer ; [London] (in Cornhil) : Robert Wilkinson, [between 1700 and 1799]

A Paris (rue St Jacques au dessus de celle des Mathurins au Gd. St. Remy) : ches Huquier fils, graveur, [between 1700 and 1799]

[The Netherlands] : [publisher not identified], [1688]

Brian Whiston since 2015 has led the state's education department while it put together its plan under the Every Student Succeeds Act and took control of the state's school takeover district.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

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

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

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

The century-old Reno Rodeo has canceled the 10-day event in June because of the coronavirus pandemic. Reno Rodeo President Craig Downie said in a letter to the rodeo’s board of directors that canceling the event scheduled for June 18-27 was necessary to ensure the safety of participants, fans, vendors, sponsors and volunteers. General Manager George Combs said it was a difficult decision, made in consultation with health experts as well as the Professional Rodeo Cowboys Association.

This Quality Counts 2019 Highlights Report captures all the data you need to assess your state's performance on key educational outcomes.

Controversy over an intiative aimed a reducing inequities in Lee's Summit, Mo., schools led the police department to provide security protection for the district's first African-American superintendent. Now the school board has reversed course.

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

Glasgow : Beatson Institute for Cancer Research, 2008-

Rockville, Maryland : National Institute on Drug Abuse, [1983]

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

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

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

Washington : Dept. of Transportation, National Highway Traffic Safety Administration, 1976.

London : European Collaborating Centres in Addiction Studies, 1998.

Helsinki : Department of Pharmacology and Toxicology, University of Helsinki, 1985.

Olivier Roustant, Fabrice Gamboa, Bertrand Iooss.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 386--412.

Abstract:
The so-called polynomial chaos expansion is widely used in computer experiments. For example, it is a powerful tool to estimate Sobol’ sensitivity indices. In this paper, we consider generalized chaos expansions built on general tensor Hilbert basis. In this frame, we revisit the computation of the Sobol’ indices with Parseval equalities and give general lower bounds for these indices obtained by truncation. The case of the eigenfunctions system associated with a Poincaré differential operator leads to lower bounds involving the derivatives of the analyzed function and provides an efficient tool for variable screening. These lower bounds are put in action both on toy and real life models demonstrating their accuracy.

Arvind Prasadan, Raj Rao Nadakuditi, Debashis Paul.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 345--385.

Abstract:
Singular value decomposition (SVD) based principal component analysis (PCA) breaks down in the high-dimensional and limited sample size regime below a certain critical eigen-SNR that depends on the dimensionality of the system and the number of samples. Below this critical eigen-SNR, the estimates returned by the SVD are asymptotically uncorrelated with the latent principal components. We consider a setting where the left singular vector of the underlying rank one signal matrix is assumed to be sparse and the right singular vector is assumed to be equisigned, that is, having either only nonnegative or only nonpositive entries. We consider six different algorithms for estimating the sparse principal component based on different statistical criteria and prove that by exploiting sparsity, we recover consistent estimates in the low eigen-SNR regime where the SVD fails. Our analysis reveals conditions under which a coordinate selection scheme based on a sum-type decision statistic outperforms schemes that utilize the $ell _{1}$ and $ell _{2}$ norm-based statistics. We derive lower bounds on the size of detectable coordinates of the principal left singular vector and utilize these lower bounds to derive lower bounds on the worst-case risk. Finally, we verify our findings with numerical simulations and a illustrate the performance with a video data where the interest is in identifying objects.

Gianluca Finocchio, Johannes Schmidt-Hieber.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 239--271.

Abstract:
Consider the Gaussian sequence model under the additional assumption that a fixed fraction of the means is known. We study the problem of variance estimation from a frequentist Bayesian perspective. The maximum likelihood estimator (MLE) for $sigma^{2}$ is biased and inconsistent. This raises the question whether the posterior is able to correct the MLE in this case. By developing a new proving strategy that uses refined properties of the posterior distribution, we find that the marginal posterior is inconsistent for any i.i.d. prior on the mean parameters. In particular, no assumption on the decay of the prior needs to be imposed. Surprisingly, we also find that consistency can be retained for a hierarchical prior based on Gaussian mixtures. In this case we also establish a limiting shape result and determine the limit distribution. In contrast to the classical Bernstein-von Mises theorem, the limit is non-Gaussian. We show that the Bayesian analysis leads to new statistical estimators outperforming the correctly calibrated MLE in a numerical simulation study.

Emilio Porcu, Rachid Senoussi, Enner Mendoza, Moreno Bevilacqua.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 890--916.

Abstract:
The paper considers reduction problems and deformation approaches for nonstationary covariance functions on the $(d-1)$-dimensional spheres, $mathbb{S}^{d-1}$, embedded in the $d$-dimensional Euclidean space. Given a covariance function $C$ on $mathbb{S}^{d-1}$, we chase a pair $(R,Psi)$, for a function $R:[-1,+1] o mathbb{R}$ and a smooth bijection $Psi$, such that $C$ can be reduced to a geodesically isotropic one: $C(mathbf{x},mathbf{y})=R(langle Psi (mathbf{x}),Psi (mathbf{y}) angle )$, with $langle cdot ,cdot angle $ denoting the dot product. The problem finds motivation in recent statistical literature devoted to the analysis of global phenomena, defined typically over the sphere of $mathbb{R}^{3}$. The application domains considered in the manuscript makes the problem mathematically challenging. We show the uniqueness of the representation in the reduction problem. Then, under some regularity assumptions, we provide an inversion formula to recover the bijection $Psi$, when it exists, for a given $C$. We also give sufficient conditions for reducibility.

Daniel Rudolf, Björn Sprungk.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 857--889.

Abstract:
A classical approach for approximating expectations of functions w.r.t. partially known distributions is to compute the average of function values along a trajectory of a Metropolis–Hastings (MH) Markov chain. A key part in the MH algorithm is a suitable acceptance/rejection of a proposed state, which ensures the correct stationary distribution of the resulting Markov chain. However, the rejection of proposals causes highly correlated samples. In particular, when a state is rejected it is not taken any further into account. In contrast to that we consider a MH importance sampling estimator which explicitly incorporates all proposed states generated by the MH algorithm. The estimator satisfies a strong law of large numbers as well as a central limit theorem, and, in addition to that, we provide an explicit mean squared error bound. Remarkably, the asymptotic variance of the MH importance sampling estimator does not involve any correlation term in contrast to its classical counterpart. Moreover, although the analyzed estimator uses the same amount of information as the classical MH estimator, it can outperform the latter in scenarios of moderate dimensions as indicated by numerical experiments.

Mahalanobis distance is a classical tool in multivariate analysis. We suggest here an extension of this concept to the case of functional data. More precisely, the proposed definition concerns those statistical problems where the sample data are real functions defined on a compact interval of the real line. The obvious difficulty for such a functional extension is the non-invertibility of the covariance operator in infinite-dimensional cases. Unlike other recent proposals, our definition is suggested and motivated in terms of the Reproducing Kernel Hilbert Space (RKHS) associated with the stochastic process that generates the data. The proposed distance is a true metric; it depends on a unique real smoothing parameter which is fully motivated in RKHS terms. Moreover, it shares some properties of its finite dimensional counterpart: it is invariant under isometries, it can be consistently estimated from the data and its sampling distribution is known under Gaussian models. An empirical study for two statistical applications, outliers detection and binary classification, is included. The results are quite competitive when compared to other recent proposals in the literature.

We consider the problem of jointly estimating multiple inverse covariance matrices from high-dimensional data consisting of distinct classes. An $ell_2$-penalized maximum likelihood approach is employed. The suggested approach is flexible and generic, incorporating several other $ell_2$-penalized estimators as special cases. In addition, the approach allows specification of target matrices through which prior knowledge may be incorporated and which can stabilize the estimation procedure in high-dimensional settings. The result is a targeted fused ridge estimator that is of use when the precision matrices of the constituent classes are believed to chiefly share the same structure while potentially differing in a number of locations of interest. It has many applications in (multi)factorial study designs. We focus on the graphical interpretation of precision matrices with the proposed estimator then serving as a basis for integrative or meta-analytic Gaussian graphical modeling. Situations are considered in which the classes are defined by data sets and subtypes of diseases. The performance of the proposed estimator in the graphical modeling setting is assessed through extensive simulation experiments. Its practical usability is illustrated by the differential network modeling of 12 large-scale gene expression data sets of diffuse large B-cell lymphoma subtypes. The estimator and its related procedures are incorporated into the R-package rags2ridges.