Gov. Kate Brown ordered the public release of annual school performance ratings last week after Oregon's biggest newspaper reported that a Brown appointee had delayed the release of the statistical rankings until after the high-stakes gubernatorial election Nov. 6.

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.

This report provides an overview of the implementation of the Water plan (Mary Basin) 2006 and summarises the findings of the assessments undertaken for the last five years. The plan supports urban water supply and a variety of industries including significant agricultural production, a growing tourism industry and fisheries.

Information technology -- Political aspects.

Information organization.

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.

Paris : Delahaye, 1863.

Argentorati : Sumtibus Amandi Koenig, 1786-1787.

Paris : V.A. Delahaye, 1880.

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.

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.

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.

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.

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

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.

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

Springfield, Virginia : National Technical Information Service, 1973.

Springfield, Virginia : National Technical Information Service, 1974.

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

[Manchester] : School of Nursing Studies, University of Manchester, [1995?]

[West Point, Pa.] : [Merck Sharp & Dohme], [1977?]

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.

Daniel R. Kowal, Antonio Canale.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1744--1772.

Abstract:
We propose a simple yet powerful framework for modeling integer-valued data, such as counts, scores, and rounded data. The data-generating process is defined by Simultaneously Transforming and Rounding (STAR) a continuous-valued process, which produces a flexible family of integer-valued distributions capable of modeling zero-inflation, bounded or censored data, and over- or underdispersion. The transformation is modeled as unknown for greater distributional flexibility, while the rounding operation ensures a coherent integer-valued data-generating process. An efficient MCMC algorithm is developed for posterior inference and provides a mechanism for adaptation of successful Bayesian models and algorithms for continuous data to the integer-valued data setting. Using the STAR framework, we design a new Bayesian Additive Regression Tree model for integer-valued data, which demonstrates impressive predictive distribution accuracy for both synthetic data and a large healthcare utilization dataset. For interpretable regression-based inference, we develop a STAR additive model, which offers greater flexibility and scalability than existing integer-valued models. The STAR additive model is applied to study the recent decline in Amazon river dolphins.

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.