[Ml-stat-talks] Wilks Statistics Seminar on Friday: Norden Huang (National Central University)
jqfan at princeton.edu
Wed Oct 23 15:43:03 EDT 2013
The details of Norden Huang can be found here:
He was elected as a member of the National Academy of Engineering, 2000 and has an interesting and effective method for forecasting. But its theoretical properties are largely unknown.
On Oct 23, 2013, at 11:03 AM, Lucy Xia <lxia at princeton.edu> wrote:
> Hi All,
> This friday's wilks seminar will be given by prof. Norden Huang from Taiwan. He is well known for his contribution in the field of nonstationary and nonlinear data analysis. Detailed info. for the talk please see below, will be a very interesting talk!
> === Wilks Statistics Seminar ===
> DATE: Friday, October 25
> TIME: 12:30pm
> LOCATION: Sherrerd Hall 101
> SPEAKER: Norden Huang, National Central University
> TITLE: A Plea for Adaptive Data Analysis
> ABSTRACT: Data analysis is indispensable to every scientific endeavors. The existing data analysis methods are all developed by mathematicians based on their rigorous rules. In pursue of the rigor, we are forced to make idealized assumptions and live in a pseudo-real linear and stationary world, in which data analysis is relegated to data processing. But the world we live in is neither stationary nor linear. As scientific research getting increasingly sophistic, the inadequacy of mere processing data becomes glaringly obvious. To get the truth containing in the data, we have to break away from these limitations; we should let data speak for themselves so that the results could reveal the full range of consequences of nonlinearity and nonstationarity. To do so, we need new paradigm of data analysis methodology without a priori basis to fully accommodating the variations of the underlying driving mechanisms. The solution lies in adaptive data analysis approach. One example is the Empirical Mode Decomposition method and the associated extensions of time-frequency representation. We will show that, with the adaptive method, we can also determine trend objectively. In fact, we can only define true frequency with adaptive method, which would lead to quantify nonstationarity and nonlinearity. Examples from classic nonlinear system and recent climate change data will be used to illustrate the prowess of the new approach.
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