High Dimensional Data Test
A Two-Sample Test for Mean Vectors in High-Dimensional Data
... of high-dimensional data by researchers and statisticians and classical statistical inferences, such as the renowned Hotelling’s T 2 test, are no longer valid when the dimension of the ...
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An Efficient Association Test for High Dimensional Data, with Application in Genetic Studies
... This paper proposes a novel spline regression method for interpreting results from single hypothesis tests that capitalises on the correlation between explanatory variables to reliably identify true associations between ...
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A Two Sample Test for High Dimensional Data with Applications to Gene set Testing
... proposed test was much more powerful than Bai-Saranadasa test for all cases considered in the simulation, while maintaining a reasonable size approximation to the nominal 5% ...proposed test and ...
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A computationally fast variable importance test for random forests for high-dimensional data
... The hold-out variable importance, in contrast, is not affected in the same manner. Here the data is partitioned into the sets S 1 and S 2 . Each set – and correspondingly each observation within the set – is used ...
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Diagonal Likelihood Ratio Test for Equality of Mean Vectors in High-Dimensional Data
... 2 dimensional data under two common scenarios: the one-sample test and the two-sample test with equal covariance ...the test statistics under the assumption that the covariance matrices ...
Independence Test for High Dimensional Random Vectors
... panel data model whose dependent structure is different from that investigated in Section 3 and develops the asymptotic distribution of the proposed statistic under the null hypothesis for this ...proposed ...
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A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise
... gave test statistics for ...the test statistics when p/n → c > 0. Since the conventional test statistics do not work for HDLSS data, Srivastava et ...a test statistic under (A-iii). ...
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A global homogeneity test for high-dimensional linear regression
... By contraposition, it suffices to reject at least one of the H 0,S hypotheses to reject the global null hypothesis. This fundamental observation motivates our testing procedure. As summarized in Algorithm 1, the idea is ...
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A Classification Algorithm for High-dimensional Data
... ranked features and estimates its error rate, then top three and so on. It then selects a set of feature combinations that has the lowest error rates and builds more accurate hypersphere classifiers with those feature ...
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Knowledge discovery in high dimensional data
... § 5.3 Which cluster to split? As explained in Section 4.1.5, the second question that any divisive algo- rithm needs to face is which cluster to select from the pool of already re- trieved partitions, to forego with the ...
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Booster in High Dimensional Data Classification
... : Data Mining is a technique used in various domains to give mean- ing to the available ...the data is classified to make predictions about new ...old data to predict new data has the danger ...
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Analysis Challenges for High Dimensional Data
... hypothesis test- ings to all the predictors, and eventually refit the linear model with these significant ...a data-driven method. Most data-driven methods have their roots in two ideas: penalized ...
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Supporter in High Dimensional Data Classification
... a high exactness that is practically unfeeling to a creating number of unessential ...logarithmical test desire quality with respect to the quantity of ...
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Statistical inference for high-dimensional data
... Introduction High-dimensional data, where the number of variables p is large compared to the sample size n, are widely available from microarray studies, finance and many other ...of high ...
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Identification of influential observations in high-dimensional cancer survival data through the rank product test
... inherent data have a high number of covariates, dimensionality reduction becomes a key challenge, usually addressed through regularized optimization, ...very high number of associated covariates ...
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Test for Bandedness of High Dimensional Covariance Matrices with Bandwidth Estimation
... proposed test is targeted on the covariance matrix Σ. A test for the correlation matrix can be developed by modifying the test statistic by first standardizing each data dimension via its ...
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Bayesian Model Selection for High-dimensional High-throughput Data
... Co-Chairs of Advisory Committee: Dr. Valen E. Johnson Dr. David B. Dahl Bayesian methods are often criticized on the grounds of subjectivity. Furthermore, mis- specified priors can have a deleterious effect on Bayesian ...
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Cluster based boosting for high dimensional data
... Although its success, boosting had some issues. Keywords - Feature Selection, Boosting, Clustering. I. INTRODUCTION Classifiers in the data mining can be divided by their learning process or representation of ...
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High-Dimensional Data Clustering
... 2.3 Subspace clustering Subspace clustering methods involve two kinds of approaches. On the one hand, projection pursuit clustering assumes that the class centers are located on a same unknown subspace [9, 14]. On the ...
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High Dimensional Data Visualization
... more than three dimensions, multiple line graphs can become confusing, depending on the scale and whether or not an offset is used to separate the dimensions. If different colored lines are used to identify each ...
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