Sparse Modelling And Estimation For Nonstationary Time Series And High Dimensional Data
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Sparse Modelling And Estimation For Nonstationary Time Series And High Dimensional Data
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Author : Haeran Cho
language : en
Publisher:
Release Date : 2010
Sparse Modelling And Estimation For Nonstationary Time Series And High Dimensional Data written by Haeran Cho and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Academic theses categories.
High Dimensional Nonstationary Time Series Modelling With Generalized Dynamic Semiparametric Factor Model
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Author : Song Song
language : en
Publisher:
Release Date : 2017
High Dimensional Nonstationary Time Series Modelling With Generalized Dynamic Semiparametric Factor Model written by Song Song and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
(High dimensional) time series which reveal nonstationary and possibly periodic behavior occur frequently in many fields of science. In this article, we separate the modeling of high dimensional time series to time propagation of low dimensional time series and high dimensional time invariant functions via functional factor analysis. We propose a two-step estimation procedure. At the first step, we detect the deterministic trends of the time series by incorporating time basis selected by the group Lasso-type technique and choose the space basis based on smoothed functional principal component analysis. We show properties of this estimator under various situations extending current variable selection studies. At the second step, we obtain the detrended low dimensional stochastic process, but it also poses an important question: is it justified, from an inferential point of view, to base further statistical inference on the estimated stochastic time series? We show that the difference of the inference based on the estimated time series and "true" unobserved time series is asymptotically negligible, which finally allows one to study the dynamics of the whole high-dimensional system with a low dimensional representation together with the deterministic trend. We apply the method to our motivating empirical problems: studies of the dynamic behavior of temperatures (further used for pricing weather derivatives), implied volatilities and risk patterns and correlated brain activities (neuro-economics related) using fMRI data, where a panel version model is also presented.
High Dimensional Covariance Estimation
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Author : Mohsen Pourahmadi
language : en
Publisher: John Wiley & Sons
Release Date : 2013-05-28
High Dimensional Covariance Estimation written by Mohsen Pourahmadi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-05-28 with Mathematics categories.
Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning. Recently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. High-Dimensional Covariance Estimation focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task. High-Dimensional Covariance Estimation features chapters on: Data, Sparsity, and Regularization Regularizing the Eigenstructure Banding, Tapering, and Thresholding Covariance Matrices Sparse Gaussian Graphical Models Multivariate Regression The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.
Partial Identification In Econometrics And Related Topics
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Author : Nguyen Ngoc Thach
language : en
Publisher: Springer Nature
Release Date :
Partial Identification In Econometrics And Related Topics written by Nguyen Ngoc Thach and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Estimation And Inference For High Dimensional Time Series
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Author : Danna Zhang
language : en
Publisher:
Release Date : 2017
Estimation And Inference For High Dimensional Time Series written by Danna Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
There is a well-developed asymptotic theory for sample means and sample second-order statistics of low dimensional stationary processes. However, many important problems on their asymptotic behaviors are still unanswered for time series which can be high-dimensional, nonstationary and non-Gaussian.
Change Point Analysis For Time Series
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Author : Lajos Horváth
language : en
Publisher: Springer Nature
Release Date :
Change Point Analysis For Time Series written by Lajos Horváth and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Neural Information Processing
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Author : Tom Gedeon
language : en
Publisher: Springer Nature
Release Date : 2019-12-05
Neural Information Processing written by Tom Gedeon and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-05 with Computers categories.
The two-volume set CCIS 1142 and 1143 constitutes thoroughly refereed contributions presented at the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019. For ICONIP 2019 a total of 345 papers was carefully reviewed and selected for publication out of 645 submissions. The 168 papers included in this volume set were organized in topical sections as follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred computing; human centred computing and medicine; human centred computing for emotion; hybrid models; image processing by neural techniques; learning from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based approaches; social network computing; spiking neuron and related models; text computing using neural techniques; time-series and related models; and unsupervised neural models.
Artificial Neural Networks And Machine Learning Icann 2019 Deep Learning
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Author : Igor V. Tetko
language : en
Publisher: Springer Nature
Release Date : 2019-09-09
Artificial Neural Networks And Machine Learning Icann 2019 Deep Learning written by Igor V. Tetko and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-09 with Computers categories.
The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.
Advances In Time Series Methods And Applications
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Author : Wai Keung Li
language : en
Publisher: Springer
Release Date : 2016-12-02
Advances In Time Series Methods And Applications written by Wai Keung Li and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-02 with Business & Economics categories.
This volume reviews and summarizes some of A. I. McLeod's significant contributions to time series analysis. It also contains original contributions to the field and to related areas by participants of the festschrift held in June 2014 and friends of Dr. McLeod. Covering a diverse range of state-of-the-art topics, this volume well balances applied and theoretical research across fourteen contributions by experts in the field. It will be of interest to researchers and practitioners in time series, econometricians, and graduate students in time series or econometrics, as well as environmental statisticians, data scientists, statisticians interested in graphical models, and researchers in quantitative risk management.
Journal Of The American Statistical Association
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Author :
language : en
Publisher:
Release Date : 2005
Journal Of The American Statistical Association written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Electronic journals categories.