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Factor modeling for high-dimensional time series: Inference for the number of factors
Autocovariance matrices blessing of dimensionality eigenanalysis fast convergence rates multivariate time series
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2012/6/19
This paper deals with the factor modeling for high-dimensional time series based on a dimension-reduction viewpoint. Under stationary settings, the inference is simple in the sense that both the numbe...
Extended BIC for linear regression models with diverging number of relevant features and high or ultra-high feature spaces
Diverging number of parameters Feature selection Extended Bayes information criterion High dimensional feature space
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2011/9/5
Abstract: In many conventional scientific investigations with high or ultra-high dimensional feature spaces, the relevant features, though sparse, are large in number compared with classical statistic...
Bernstein von Mises Theorems for Gaussian Regression with increasing number of regressors
Bernstein von Mises Theorems Gaussian Regression increasing number of regressors
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2010/12/1
This paper brings a contribution to the Bayesian theory of nonparametric and semiparametric estimation.