NDLI: Posters to be presented at the CONFERENCE IN HONOR OF MURRAY ROSENBLATT UC San Diego , October 21-23 , 2016

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Posters to be presented at the CONFERENCE IN HONOR OF MURRAY ROSENBLATT UC San Diego , October 21-23 , 2016

Content Provider	Semantic Scholar
Author	Alimohammadi, Shahrouz Bai, Shuyang
Abstract	Statistical inference of long-memory time series faces two challenges due to the special behavior of the sample sum 1) a non-standard fluctuation rate which is typically unknown 2) a family of non-Gaussian scaling limits arise and it is difficult to determine which one is involved statistically. We introduce a procedure which combines two strategies: self-normalization and subsampling. Such a combination successfully bypasses the aforementioned challenges. Presenter: Srinjoy Das, PhD student, Department of Electrical and Computer Engineering, UCSD, srinjoyd@gmail.com Title: Predictive inference for locally stationary time series Abstract: A large class of time-series encountered in real-life which include examples such as key economic data and certain weather pattern measurements have a slowly changing stochastic structure where it is possible to analyze the series by assuming stationarity over short time windows. For such cases of locally stationary time series (LSTS) the Model-Free (MF) Prediction Principle developed by Politis (2013) can be used for predictive inference. The MF principle permits analysis of such series in very general cases which may include a trend and time-varying mean, variance, higher-order moments or nonstationarity in the mth order marginal distribution. Based on various nonstationarity properties of such LSTS several one-step ahead point prediction and prediction interval generation methods are developed and their performance compared using finite sample simulations.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.math.ucsd.edu/~rosenblattconf/PosterROSENBLATTtex.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Poster

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