[PDF] Top 20 Two Sample Tests for High Dimensional Covariance Matrices

... In addition to detecting difference among the population means, there is a strong mo- tivation for comparing dependence among components of random vectors under different treatments, as high data dimensions can ... See full document

... when high collinearity exists, for example, in factor ...the sample marginal correlation and the true regression coeffi- cients due to collinearity, as mentioned in Section ...(ultra) ... See full document

... Our specification of the BSFG model in (4) differs from earlier methods such as the Bayesian genetic factor model of de los Campos and Gianola (2007) in two key respects. First, in classic factor models, the total ... See full document

... the sample size is less than the number of genes, we omit the inverse sample covariance matrix as it is not well defined and instead include the naive Bayes’ and support vector machine ...the ... See full document

... containing high-frequency components. However, by letting d to increase with sample sizes we allow for different asymptotics than Neyman’s fix d large sample ...present two-sample ... See full document

... Irvine Machine Learning Repository. The data set collects the so-called sonar returns which are the amplitudes of bouncing signals off an object, essentially the return signal strength over time. The sonar returns were ... See full document

... residual matrices for some moderate choice of regulariza- tion parameters γ = 20 and λ = ...are two main observations in the learned model in Figure ...some high degree nodes such as “South Korea” ... See full document

... in high dimensional generalized linear models, which is of important interest to practitioners, for instance in the context of discovering significant ...both high dimensionality and multiplicity, as ... See full document

... for covariance and inverse covariance estimation and for risk consistency based on an estimated sparse graph as we mentioned ...the sample complexity is one of the key focus point, which builds upon ... See full document

... of tests for hypotheses on the coefficient vector in linear regression models with dependent errors is highly practically relevant and has received lots of attention in the statistics and econometrics ...obtain ... See full document

... In this section, we demonstrate the performance of the multi-level thresholding test defined in (4.9) on a human breast cancer dataset, available at http://www.ncbi.nlm. nih.gov. The data have been analyzed by Richardson ... See full document

... the covariance matrix Σ by expanding the process X into a dictionary of basis ...to covariance estimation is well adapted to the case of low-dimensional covariance estimation when the num- ber ... See full document

... ting. We observe from Table 1 that the test has reasonably empirical sizes, around 5%, and that the test is not sensitive to the dimensionality indicated by its robust performance. There is some size inflation, which is ... See full document

... We remark that the oracle inequality given in Theorem 1 is of probabilistic nature and non- asymptotic. However, (4) holds with overwhelming probability as we are interested in the case when p is very large. The ... See full document

... This paper contributes to the literature on time-series modeling of RCOV. There are several existing approaches. One is to use some form of a decomposition to the matrix of realized variances and covariances and then use ... See full document

... formalize two-dimensional ...of two-dimensional semantics, although it can be used for this with certain ...formalize two-dimensional ...with two-dimensional ... See full document

... Principle Component Analysis (PCA, also called Karhunen-Loeve transform) is one of the most widely used dimension reduction techniques for data analysis and compression in practice. Its many application areas include ... See full document

... A high precision – O(10%) – measurement of a change in beam emittance, which isn’t possible with conventional bulk measurement techniques, requires MICE to operate as a single particle experiment, ... See full document

... a two-step process which consists of computing upper bounds for suitable functions on intervals contain- ing the spectrum of T and then applying the standard operational calculus to T (see ...or matrices ... See full document

... on eigenvalues presented larger power estimates than the generalized variance test (except for case 5, n = 10, where they were similar) in some of the cases and they were able to detect the differences from 0 to 1 even ... See full document