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Found 4 papers, 0 papers with code
A Unified Framework for Fast Large-Scale Portfolio Optimization
We introduce a unified framework for rapid, large-scale portfolio optimization that incorporates both shrinkage and regularization techniques.
Theoretical analysis of deep neural networks for temporally dependent observations
Despite the widespread use of neural networks in such settings, most theoretical developments of deep neural networks are under the assumption of independent observations, and theoretical results for temporally dependent observations are scarce.
Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm
To overcome these challenges, in this paper we propose an approximate EM algorithm for Markov-switching VAR models that leads to efficient computation and also facilitates the investigation of asymptotic properties of the resulting parameter estimates.
Inference on the change point in high dimensional time series models via plug in least squares
We study a plug in least squares estimator for the change point parameter where change is in the mean of a high dimensional random vector under subgaussian or subexponential distributions.
