Principal Component Analysis of High-Frequency Data
نویسندگان
چکیده
منابع مشابه
High-dimensional Principal Component Analysis
High-dimensional Principal Component Analysis by Arash Ali Amini Doctor of Philosophy in Electrical Engineering University of California, Berkeley Associate Professor Martin Wainwright, Chair Advances in data acquisition and emergence of new sources of data, in recent years, have led to generation of massive datasets in many fields of science and engineering. These datasets are usually characte...
متن کاملThe Five Trolls under the Bridge: Principal Component Analysis with Asynchronous and Noisy High Frequency Data
We develop a principal component analysis (PCA) for high frequency data. As in Northern fairly tales, there are trolls waiting for the explorer. The first three trolls are market microstructure noise, asynchronous sampling times, and edge effects in estimators. To get around these, a robust estimator of the spot covariance matrix is developed based on the Smoothed TSRV (Mykland et al. (2017)). ...
متن کاملPrincipal Component Analysis for Sparse High-Dimensional Data
Principal component analysis (PCA) is a widely used technique for data analysis and dimensionality reduction. Eigenvalue decomposition is the standard algorithm for solving PCA, but a number of other algorithms have been proposed. For instance, the EM algorithm is much more efficient in case of high dimensionality and a small number of principal components. We study a case where the data are hi...
متن کاملMultilevel Functional Principal Component Analysis for High-Dimensional Data.
We propose fast and scalable statistical methods for the analysis of hundreds or thousands of high dimensional vectors observed at multiple visits. The proposed inferential methods do not require loading the entire data set at once in the computer memory and instead use only sequential access to data. This allows deployment of our methodology on low-resource computers where computations can be ...
متن کاملPrincipal Component Analysis with Contaminated Data: The High Dimensional Case
We consider the dimensionality-reduction problem (finding a subspace approximation of observed data) for contaminated data in the high dimensional regime, where the number of observations is of the same magnitude as the number of variables of each observation, and the data set contains some (arbitrarily) corrupted observations. We propose a High-dimensional Robust Principal Component Analysis (...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2018
ISSN: 0162-1459,1537-274X
DOI: 10.1080/01621459.2017.1401542