Variable selectionThis paper is concerned with identifying important features in high dimensional data analysis, especially when there are complex relationships among predictors. Without any specication of an a
We attribute the excellent modelling capabilities, especially the very high coefficients of determination and cross- validation correlations to the models' ability to avoid the curse of dimensionality while retaining great flexibility in the regression function9. In addition,12 concurred w ith...
On the other hand, as a direct application, based on the new communication-efficient distributed CQR and the smooth-threshold estimating equations (Ueki 2009), we further propose a variable selection procedure for the massive data, which can also be realized on the first machine. Theoretically, ...
The number of iterations of the Sequential MCMC sampler at each time \(m_{t}\) is determined based on the cross-chain correlation (see Guhaniyogi et al., 2018). Specifically, we set the number of iterations at time t, denoted as \(m_{t}\), to be the smallest integer s such that...
(2010) propose a correlation-based weighted complex network to uncover the interactional mechanism of financial markets. The empirical findings of Patro et al. (2013) show that the correlation network, built on stock return correlations among the US 22 largest financial institutions, is a useful ...
3, while the greatest correlation coefficient is about 0.87 for τ=0.4 and τ=0.9. If anything, there is weak evidence of a relative stronger correlation between In-degree and Out-degree for higher quantiles than for lower quantiles. 15 A firm might purchase allowances even when it ...
It produces a graphical map based on the forgoing statistical metrics that present the distance between the forecasting models and the observed river flow data set. From the Taylor diagrams, all the methods have similar correlation levels for Mary River and Lockyer Valley. On the other hand, ...
Şentürk, D., and H.-G. Müller. 2005. Covariate adjusted correlation analysis via varying coefficient models.Scandinavian Journal of Statistics32:365–83. Web of Science ®Google Scholar Şentürk, D., and H.-G. Müller. 2006. Inference for covariate adjusted regression via varying coeff...
Intuitively, the monotonicity of quantiles allows one to avoid modeling individuals’ utility function. This is because the maximization problem is invariant to monotonic transformations of the distribution of portfolio returns. Our model can deal with short sale, but we leave this to future work. ...
In particular, we argue that, in both plots, there is a higher positive correlation between the lower percentiles of both variables. For the sake of completeness, in Appendix A D-vine for the, Appendix B D-vine for the, Fig. A.2, Fig. B.2 show the conditional copula densities between...