Yiming Ding

MIC as an Appropriate Method to Construct the Brain Functional Network

Using an effective method to measure the brain functional connectivity is an important step to study the brain functional network. The main methods for constructing an undirected brain functional network include correlation coefficient (CF), partial correlation coefficient (PCF), mutual information (MI), wavelet correlation coefficient (WCF), and coherence (CH). In this paper we demonstrate that the maximal information coefficient (MIC) proposed by Reshef et al. is relevant to constructing a brain functional network because it performs best in the comprehensive comparisons in consistency and robustness. Our work can be used to validate the possible new functional connection measures.

THE ASYMPTOTIC PERIODICITY OF LORENZ MAPS

A Lorenz map f : I --> I is a one dimensional piecewise monotone map with a single discontinuity c. Let

Wavelet-based estimator for the hurst parameters of fractional brownian sheet

It is proposed a class of statistical estimators

MODEL SELECTION METHOD BASED ON MAXIMAL INFORMATION COEFFICIENT OF RESIDUALS

\hat H =(\hat H_1, \ldots, \hat H_d)

NASR: an effective approach for simultaneous noise and artifact suppression in NMR spectroscopy.

for the Hurst parameters

Enhancement of tunability of MAPK cascade due to coexistence of processive and distributive phosphorylation mechanisms.

H=(H_1, \ldots, H_d)

Estimation of self-similar Gaussian fields using wavelet transform

of fractional Brownian field via multi-dimensional wavelet analysis and least squares, which are asymptotically normal. These estimators can be used to detect self-similarity and long-range dependence in multi-dimensional signals, which is important in texture classification and improvement of diffusion tensor imaging (DTI) of nuclear magnetic resonance (NMR). Some fractional Brownian sheets will be simulated and the simulated data are used to validate these estimators. We find that when

Reconstructing diffusion ordered NMR spectroscopy by simultaneous inversion of Laplace transform

H_i \geq 1/2

On Hausdorff Dimension of Invariant Sets for a Class of Piecewise Linear Maps

, the estimators are efficient, and when

Rate of convergence and expansion of rényi entropic central limit theorem

H_i < 1/2