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

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

Rockville, Maryland : National Institute on Drug Abuse, 1991.

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

Rockville, Maryland : National Institute on Drug Abuse, 1984.

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

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

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

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

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

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

Rockville, Maryland : National Institute on Drug Abuse, 1978.

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

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

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

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

Rockville, Maryland : National Institute on Drug Abuse, 1978.

Rockville, Maryland : National Institute on Drug Abuse, 1978.

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

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

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

Rockville, Maryland : National Institute on Drug Abuse, 1977.

[United States] : National Technical Information Service, United States Department of Commerce, 1984.

Springfield, Virginia. : National Technical Information Service, 1984.

Springfield, Virginia : National Technical Information Service, 1974.

[Hong Kong] : [Art In Hospitals], [2019]

Springfield, Virginia : National Technical Information Service, 1973.

Springfield, Virginia : National Technical Information Service, 1974.

New York : Experimental Manpower Laboratory at Mobilization for Youth, Inc., [1971]

Washington, D.C. : Department of Transportation, Federal Railroad Administration, 1976.

Washington, D.C. : Wynne Associates, 1974.

Washington, D.C. : Wynne Associates, [1974]

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

São Paulo : Governo do Estado de Sao Paulo, Secretaria da Segurança Pública, 1975.

São Paulo : Serviço Grafíco da Secretaria da Segurança Pública, 1976.

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

Hear how former Oregon State guard and current member of the WNBA's LA Sparks Sydney Wiese is recovering from a COVID-19 diagnosis, seeing friends and family show support and love during a trying time.

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.

Pac-12 Networks' Mike Yam interviews former Oregon State guard Sydney Wiese to hear how she's recovering from contracting COVID-19. Wiese recounts her recent travel and how she's been lifted up by steadfast support from friends, family and fellow WNBA players. See more from Wiese during "Pac-12 Playlist" on Monday, April 6 at 7 p.m. PT/ 8 p.m. MT on Pac-12 Network.

During Wednesday's "Pac-12 Perspective" podcast, Natalie Chou shared why she is using her platform to speak out against racism she sees in her community related to the novel coronavirus.

Ruofei Zhao, Yuanzhi Li, Yuekai Sun.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 632--660.

Abstract:
We study the convergence behavior of the Expectation Maximization (EM) algorithm on Gaussian mixture models with an arbitrary number of mixture components and mixing weights. We show that as long as the means of the components are separated by at least $Omega (sqrt{min {M,d}})$, where $M$ is the number of components and $d$ is the dimension, the EM algorithm converges locally to the global optimum of the log-likelihood. Further, we show that the convergence rate is linear and characterize the size of the basin of attraction to the global optimum.

Gregor Pasemann, Wilhelm Stannat.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 547--579.

Abstract:
A parameter estimation problem for a class of semilinear stochastic evolution equations is considered. Conditions for consistency and asymptotic normality are given in terms of growth and continuity properties of the nonlinear part. Emphasis is put on the case of stochastic reaction-diffusion systems. Robustness results for statistical inference under model uncertainty are provided.

François Bachoc, Baptiste Broto, Fabrice Gamboa, Jean-Michel Loubes.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 503--546.

Abstract:
In the framework of the supervised learning of a real function defined on an abstract space $mathcal{X}$, Gaussian processes are widely used. The Euclidean case for $mathcal{X}$ is well known and has been widely studied. In this paper, we explore the less classical case where $mathcal{X}$ is the non commutative finite group of permutations (namely the so-called symmetric group $S_{N}$). We provide an application to Gaussian process based optimization of Latin Hypercube Designs. We also extend our results to the case of partial rankings.

Ming Yu, Varun Gupta, Mladen Kolar.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 413--457.

Abstract:
We study the problem of recovery of matrices that are simultaneously low rank and row and/or column sparse. Such matrices appear in recent applications in cognitive neuroscience, imaging, computer vision, macroeconomics, and genetics. We propose a GDT (Gradient Descent with hard Thresholding) algorithm to efficiently recover matrices with such structure, by minimizing a bi-convex function over a nonconvex set of constraints. We show linear convergence of the iterates obtained by GDT to a region within statistical error of an optimal solution. As an application of our method, we consider multi-task learning problems and show that the statistical error rate obtained by GDT is near optimal compared to minimax rate. Experiments demonstrate competitive performance and much faster running speed compared to existing methods, on both simulations and real data sets.

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.

Daren Wang, Yi Yu, Alessandro Rinaldo.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1917--1961.

Abstract:
The problem of univariate mean change point detection and localization based on a sequence of $n$ independent observations with piecewise constant means has been intensively studied for more than half century, and serves as a blueprint for change point problems in more complex settings. We provide a complete characterization of this classical problem in a general framework in which the upper bound $sigma ^{2}$ on the noise variance, the minimal spacing $Delta $ between two consecutive change points and the minimal magnitude $kappa $ of the changes, are allowed to vary with $n$. We first show that consistent localization of the change points is impossible in the low signal-to-noise ratio regime $frac{kappa sqrt{Delta }}{sigma }preceq sqrt{log (n)}$. In contrast, when $frac{kappa sqrt{Delta }}{sigma }$ diverges with $n$ at the rate of at least $sqrt{log (n)}$, we demonstrate that two computationally-efficient change point estimators, one based on the solution to an $ell _{0}$-penalized least squares problem and the other on the popular wild binary segmentation algorithm, are both consistent and achieve a localization rate of the order $frac{sigma ^{2}}{kappa ^{2}}log (n)$. We further show that such rate is minimax optimal, up to a $log (n)$ term.