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Capturing dependence among a large number of high dimensional random vectors is a very important and challenging problem. By arranging n random vectors of length p in the form of a matrix, we develop a linear spectral statistic of the constructed matrix to test whether the n random vectors are...
Persistent link: https://www.econbiz.de/10015236206
Capturing dependence among a large number of high dimensional random vectors is a very important and challenging problem. By arranging n random vectors of length p in the form of a matrix, we develop a linear spectral statistic of the constructed matrix to test whether the n random vectors are...
Persistent link: https://www.econbiz.de/10011259986
Statistical inferences for sample correlation matrices are important in high dimensional data analysis. Motivated by this, this paper establishes a new central limit theorem (CLT) for a linear spectral statistic (LSS) of high dimensional sample correlation matrices for the case where the...
Persistent link: https://www.econbiz.de/10011093869
Capturing dependence among a large number of high dimensional random vectors is a very important and challenging problem. By arranging n random vectors of length p in the form of a matrix, we develop a linear spectral statistic of the constructed matrix to test whether the n random vectors are...
Persistent link: https://www.econbiz.de/10010860404
Capturing dependence among a large number of high-dimensional random vectors is a very important and challenging problem. By arranging <italic>n</italic> random vectors of length <italic>p</italic> in the form of a matrix, we develop a linear spectral statistic of the constructed matrix to test whether the <italic>n</italic> random vectors are...
Persistent link: https://www.econbiz.de/10010971105
Capturing dependence among a large number of high dimensional random vectors is a very important and challenging problem. By arranging n random vectors of length p in the form of a matrix, we develop a linear spectral statistic of the constructed matrix to test whether the n random vectors are...
Persistent link: https://www.econbiz.de/10013085147
In this paper, we consider a class of time-varying panel data models with individual-specific regression coefficients and common factors where both the serial correlation and cross-sectional dependence among error terms can be present. Based on an initial estimator of factors, we propose a...
Persistent link: https://www.econbiz.de/10012898777
In this paper, we propose a localized neural network (LNN) model and then develop the LNN based estimation and inferential procedures for dependent data in both cases with quantitative/qualitative outcomes. We explore the use of identification restrictions from a nonparametric regression...
Persistent link: https://www.econbiz.de/10014347671
Persistent link: https://www.econbiz.de/10009724611
Persistent link: https://www.econbiz.de/10011781035