
Sufficient principal component regression for pattern discovery in transcriptomic data
Methods for global measurement of transcript abundance such as microarra...
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Markovswitching State Space Models for Uncovering Musical Interpretation
For concertgoers, musical interpretation is the most important factor in...
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Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression
Motivation: The discovery of relationships between gene expression measu...
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Modeling trend in temperature volatility using generalized LASSO
In this paper, we present methodology for estimating trends in spatiote...
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Risk estimation for highdimensional lasso regression
In highdimensional estimation, analysts are faced with more parameters ...
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On the Nyström and ColumnSampling Methods for the Approximate Principal Components Analysis of Large Data Sets
In this paper we analyze approximate methods for undertaking a principal...
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Riskconsistency of crossvalidation with lassotype procedures
The lasso and related sparsity inducing algorithms have been the target ...
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Nonparametric risk bounds for timeseries forecasting
We derive generalization error bounds for traditional timeseries foreca...
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Estimated VC dimension for risk bounds
VapnikChervonenkis (VC) dimension is a fundamental measure of the gener...
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Spectral approximations in machine learning
In many areas of machine learning, it becomes necessary to find the eige...
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Rademacher complexity of stationary sequences
We show how to control the generalization error of time series models wh...
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Generalization error bounds for stationary autoregressive models
We derive generalization error bounds for stationary univariate autoregr...
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Estimating βmixing coefficients
The literature on statistical learning for time series assumes the asymp...
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Daniel J. McDonald
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