Adaptive Higher-order Spectral Estimators

Many applications involve estimation of a signal matrix from a noisy data matrix. In such cases, it has been observed that estimators that shrink or truncate the singular values of the data matrix perform well when the signal matrix has approximately low rank. In this article, we generalize this approach to the estimation of a tensor of parameters from noisy tensor data. We develop new classes of estimators that shrink or threshold the mode-specific singular values from the higher-order singular value decomposition. These classes of estimators are indexed by tuning parameters, which we adaptively choose from the data by minimizing Stein's unbiased risk estimate. In particular, this procedure provides a way to estimate the multilinear rank of the underlying signal tensor. Using simulation studies under a variety of conditions, we show that our estimators perform well when the mean tensor has approximately low multilinear rank, and perform competitively when the signal tensor does not have approximately low multilinear rank. We illustrate the use of these methods in an application to multivariate relational data.Comment: 29 pages, 3 figure

Bayesian dimensionality reduction with PCA using penalized semi-integrated likelihood

We discuss the problem of estimating the number of principal components in Principal Com- ponents Analysis (PCA). Despite of the importance of the problem and the multitude of solutions proposed in the literature, it comes as a surprise that there does not exist a coherent asymptotic framework which would justify different approaches depending on the actual size of the data set. In this paper we address this issue by presenting an approximate Bayesian approach based on Laplace approximation and introducing a general method for building the model selection criteria, called PEnalized SEmi-integrated Likelihood (PESEL). Our general framework encompasses a variety of existing approaches based on probabilistic models, like e.g. Bayesian Information Criterion for the Probabilistic PCA (PPCA), and allows for construction of new criteria, depending on the size of the data set at hand. Specifically, we define PESEL when the number of variables substantially exceeds the number of observations. We also report results of extensive simulation studies and real data analysis, which illustrate good properties of our proposed criteria as compared to the state-of- the-art methods and very recent proposals. Specifially, these simulations show that PESEL based criteria can be quite robust against deviations from the probabilistic model assumptions. Selected PESEL based criteria for the estimation of the number of principal components are implemented in R package varclust, which is available on github (https://github.com/psobczyk/varclust).Comment: 31 pages, 7 figure

The Effect of Walkthrough Observations on Teacher Perspectives in Christian Schools

This study investigated the effects on teacher perceptions of frequent, brief classroom observations in Christian schools. Teachers (N=111) responded to 13 belief and value statements prior to and after the term during which administrators conducted weekly, brief, unannounced observations in their classes. Teachers reported significant positive change regarding (a) analyzing reasons for selecting methods to assess learning, (b) being encouraged after class observations, and (c) being encouraged after receiving feedback related to the observations

Effect of Applied Orthorhombic Lattice Distortion on the Antiferromagnetic Phase of CeAuSb2_2

We study the response of the antiferromagnetism of CeAuSb$_2$ to orthorhombic lattice distortion applied through in-plane uniaxial pressure. The response to pressure applied along a $\langle 110 \rangle$ lattice direction shows a first-order transition at zero pressure, which shows that the magnetic order lifts the $(110)/(1\bar{1}0)$ symmetry of the unstressed lattice. Sufficient $\langle 100 \rangle$ pressure appears to rotate the principal axes of the order from $\langle 110 \rangle$ to $\langle 100 \rangle$. At low $\langle 100 \rangle$ pressure, the transition at $T_N$ is weakly first-order, however it becomes continuous above a threshold $\langle 100 \rangle$ pressure. We discuss the possibility that this behavior is driven by order parameter fluctuations, with the restoration of a continuous transition a result of reducing the point-group symmetry of the lattice.Comment: 6 pages, 7 figure

Panel Data Tests Of PPP: A Critical Overview

This paper reviews recent developments in the analysis of non-stationary panels, focusing on empirical applications of panel unit root and cointegration tests in the context of PPP. It highlights various drawbacks of existing methods. First, unit root tests suffer from severe size distortions in the presence of negative moving average errors. Second, the common demeaning procedure to correct for the bias resulting from homogeneous cross-sectional dependence is not effective; more worryingly, it introduces cross-correlation when it is not already present. Third, standard corrections for the case of heterogeneous cross-sectional dependence do not generally produce consistent estimators. Fourth, if there is between-group correlation in the innovations, the SURE estimator is affected by similar problems to FGLS methods, and does not necessarily outperform OLS. Finally, cointegration between different groups in the panel could also be a source of size distortions. We offer some empirical guidelines to deal with these problems, but conclude that panel methods are unlikely to solve the PPP puzzl