Conventional Vector Autoregressive (VAR) modelling methods applied to high dimensional neural time series data result in noisy solutions that are dense or have a large number of spurious coefficients. This reduces the speed and accuracy of auxiliary comp
(2013). Inference for high-dimensional sparse econometric models. Advances in Economics and Econometrics. 10th World Congress of Econometric Society. August 2010, III:245- 295. ArXiv, 2011.Belloni, A., V. Chernozhukov, and C. Hansen (2013): "Inference for high-dimensional sparse econometric ...
2021, Review of World Economics An improved modified cholesky decomposition approach for precision matrix estimation 2020, Journal of Statistical Computation and Simulation Scalable Bayesian variable selection for structured high-dimensional data 2018, Biometrics Cholesky-GARCH models with applications to finance...
Identification theory for high dimensional static and dynamic factor models J. Econometrics (2014) ContiG. et al. Bayesian exploratory factor analysis J. Econometrics (2014) NeudeckerH. On the identification of restricted factor loading matrices: An alternative condition J. Math. Psych. (1990) Shap...
Two-dimensional materials offer a promising platform for the next generation of (opto-) electronic devices and other high technology applications. One of the most exciting characteristics of 2D crystals is the ability to tune their properties via controllable introduction of defects. However, the searc...
High-dimensional feature selection methods, such as stability selection (SS), Model-X (MX) knockoff or bootstrap-enhanced Lasso (Bolasso), improve reliability by controlling for false discoveries in the selected feature set18,19,20. However, these methods often require a priori definition of the...
Y Matsushita,T Otsu - 《Lse Research Online Documents on Economics》 被引量: 0发表: 2020年 Jackknife empirical likelihood test for high-dimensional regression coefficients A novel way to test coefficients in high-dimensional linear regression model is presented. Under the 'large p small n' situati...
High dimensional T-type Estimator for robust covariance matrix estimation with applications to elliptical factor models Article 06 June 2024 Simple powerful robust tests based on sign depth Article Open access 30 July 2022 References Anderson, T. W.: An Introduction to Multivariate Statistical An...
To obtain the correct value of 𝝝Θ, supervised models use a training set (i.e., a dataset where both dependent and independent variables are known) and compute the set of parameters 𝝝Θ that, given the training set, is more likely to reproduce the data. In the case of decision ...
The non-zero elements are ordered by rows in a one-dimensional real array, say array ALU. If a non-zero element is located in position K of array ALU, then its column number must be stored in position K of a one-dimensional integer array CNLU. The length of these two arrays is NN...