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Paris : Société d’éditions scientifiques, 1896.

Londres : H. Bailliere, 1868-1884.

Paris : Germer-Bailliere, 1856-1857.

London : Longmans, Green, Reader, and Dyer, 1877-1878.

London : Printed for John Anderson [and three others], 1825.

London : Printed for Longman, Orme, Brown, Green and Longman, 1838.

London : printed for Longman, Brown, Green, and Longmans, 1842.

London : printed for J. Callow, 1814.

London : Longmans, Green, 1895.

London : Macmillan, 1874-1876.

London : Cassell, 1894.

Madras : printed by C. Foster, 1876.

Glasgow : J. Maclehose, 1887.

Bruxelles : Etablissement Encyclographique, 1833-46.

London : G.P. Putnam's Sons, 1906.

London : printed for the author, 1832.

London : Bailliere, Tindall and Cox, 1884.

Londres : Paris, 1866.

Paris : Ancelle, 1812.

Paris : G. Steinheil, 1895.

Primary season is now upon us. Here are three ideas for teaching in the wake of the Iowa caucus fallout.

In a case implicating the use of schools as voting locations as well as free speech in education, the justices said Minnesota went too far.

Fast-moving bills in two state legislatures could dictate to their education departments what goes into the states' accountability plans under the Every Student Succeeds Act.

[Paris?] : Mac. S. [le tout dirigé & mis au jour, par les soins de Eugene Mac-Swiny], [1741?]

London : Printed by Mary Clark, and are to be sold by John Clark, at Mercers Chappel at the Lower End of Cheapside, MDCLXXVIII. [1678]

([Rome] : Raffaelle Jacomini impresse)

[London] : Waterlow Bros. & Layton, Limited, [1893]

A 24-team Stanley Cup Playoffs? Here's a look at the latest NHL buzz and a hypothetical Flyers matchup. By Jordan Hall

The state's Republican governor is in a pitched battle with the state's educators over the process he used to fire Missouri's commissioner of education.

Former Republican Missouri Gov. Eric Greitens appointed enough board members to have Commissioner Margie Vandeven fired last year, but now that he's gone, the state board decided to hire her back.

[QLD, Australia] : Hawkeye Publishing, 2018.

Leith, 2019

Rockville, Maryland : National Institute on Drug Abuse, 1982.

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

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

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

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Durham, North Carolina : [Duke Station, 1957-[197-?]

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.

Emanuel Ben-David, Bala Rajaratnam.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 580--604.

Abstract:
The Wishart distribution defined on the open cone of positive-definite matrices plays a central role in multivariate analysis and multivariate distribution theory. Its domain of parameters is often referred to as the Gindikin set. In recent years, varieties of useful extensions of the Wishart distribution have been proposed in the literature for the purposes of studying Markov random fields and graphical models. In particular, generalizations of the Wishart distribution, referred to as Type I and Type II (graphical) Wishart distributions introduced by Letac and Massam in Annals of Statistics (2007) play important roles in both frequentist and Bayesian inference for Gaussian graphical models. These distributions have been especially useful in high-dimensional settings due to the flexibility offered by their multiple-shape parameters. Concerning Type I and Type II Wishart distributions, a conjecture of Letac and Massam concerns the domain of multiple-shape parameters of these distributions. The conjecture also has implications for the existence of Bayes estimators corresponding to these high dimensional priors. The conjecture, which was first posed in the Annals of Statistics, has now been an open problem for about 10 years. In this paper, we give a necessary condition for the Letac and Massam conjecture to hold. More precisely, we prove that if the Letac and Massam conjecture holds on a decomposable graph, then no two separators of the graph can be nested within each other. For this, we analyze Type I and Type II Wishart distributions on appropriate Markov equivalent perfect DAG models and succeed in deriving the aforementioned necessary condition. This condition in particular identifies a class of counterexamples to the conjecture.

Matthias Löwe, Kristina Schubert.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1796--1815.

Abstract:
We show how to exactly reconstruct the block structure at the critical line in the so-called Ising block model. This model was recently re-introduced by Berthet, Rigollet and Srivastava in [2]. There the authors show how to exactly reconstruct blocks away from the critical line and they give an upper and a lower bound on the number of observations one needs; thereby they establish a minimax optimal rate (up to constants). Our technique relies on a combination of their methods with fluctuation results obtained in [20]. The latter are extended to the full critical regime. We find that the number of necessary observations depends on whether the interaction parameter between two blocks is positive or negative: In the first case, there are about $Nlog N$ observations required to exactly recover the block structure, while in the latter case $sqrt{N}log N$ observations suffice.

Christophe A. N. Biscio, Nicolas Chenavier, Christian Hirsch, Anne Marie Svane.

Source: Electronic Journal of Statistics, Volume 14, Number 1, 1024--1074.

Abstract:
We introduce tests for the goodness of fit of point patterns via methods from topological data analysis. More precisely, the persistent Betti numbers give rise to a bivariate functional summary statistic for observed point patterns that is asymptotically Gaussian in large observation windows. We analyze the power of tests derived from this statistic on simulated point patterns and compare its performance with global envelope tests. Finally, we apply the tests to a point pattern from an application context in neuroscience. As the main methodological contribution, we derive sufficient conditions for a functional central limit theorem on bounded persistent Betti numbers of point processes with exponential decay of correlations.

This paper studies the nonparametric modal regression problem systematically from a statistical learning viewpoint. Originally motivated by pursuing a theoretical understanding of the maximum correntropy criterion based regression (MCCR), our study reveals that MCCR with a tending-to-zero scale parameter is essentially modal regression. We show that the nonparametric modal regression problem can be approached via the classical empirical risk minimization. Some efforts are then made to develop a framework for analyzing and implementing modal regression. For instance, the modal regression function is described, the modal regression risk is defined explicitly and its Bayes rule is characterized; for the sake of computational tractability, the surrogate modal regression risk, which is termed as the generalization risk in our study, is introduced. On the theoretical side, the excess modal regression risk, the excess generalization risk, the function estimation error, and the relations among the above three quantities are studied rigorously. It turns out that under mild conditions, function estimation consistency and convergence may be pursued in modal regression as in vanilla regression protocols such as mean regression, median regression, and quantile regression. On the practical side, the implementation issues of modal regression including the computational algorithm and the selection of the tuning parameters are discussed. Numerical validations on modal regression are also conducted to verify our findings.