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Discrimination of Nonstationary Time Series using the SLEX Model

Huang, Hsiao-Yun (2003) Discrimination of Nonstationary Time Series using the SLEX Model. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Statistical discrimination for nonstationary random processes have developed into a widely practiced field with various applications. In some applications, such as signal processing and geophysical data analysis, the generated processes are usually long series. In such cases, a discriminant scheme with computational efficiency and optimal property is of great interest.In this dissertation, a discriminant scheme for nonstationary time series based on the SLEX model (Ombao, Raz, von Sachs and Guo, 2002) is presented. The SLEX model is based on the Smooth Localized complex EXponential (SLEX)[Wickerhauser, 1994] basis functions. SLEX basis functions generalize directly to a library of SLEX basis vectors that are complex-valued, orthonormal, and simultaneously localized in time and frequency domains (Wickerhauser, 1994). Thus, it provides an explicit segmentation of the time-frequency plane and hence is able to represent discrete random processes whose spectral properties change with time. Since the SLEX basis functions can also be considered a generalization of the tapered Fourier vectors, the calculation from SLEX basis functions to a library of SLEX basis vectors (called the SLEX transform) can use the Fast Fourier Transform. That is, the SLEX transform has computational efficiency. Moreover, the SLEX model, with a structure for asymptotic theory, allows the derivation of the optimal properties of the discriminant statistic in this dissertation. A statistical time series classification scheme can be considered a formulation with two steps: extracting features from the data and developing a decision function. For feature extraction, a fast algorithm associated with the SLEX model is formed to extract the features. For developing a decision function, an optimal discriminant statistic based on the Kullback-Leibler divergence (Kullback and Leibler, 1951) of the SLEX model is proposed. The entire scheme will be organized as an algorithm. That is, a computationally efficient and statistically optimal discriminant scheme for nonstationary time series is proposed in this dissertation.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Huang, Hsiao-Yunhshst12@pitt.eduHSHST12
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairStoffer, David Sstoffer@stat.pitt.eduSTOFFER
Committee MemberOmbao,
Committee MemberRosen,
Committee MemberAnderson, Stewartsja@pitt.eduSJA
Date: 28 May 2003
Date Type: Completion
Defense Date: 21 April 2006
Approval Date: 28 May 2003
Submission Date: 1 May 2003
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Statistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: SLEX; nonstationary;classification;discrimination
Other ID:, etd-05012003-154859
Date Deposited: 10 Nov 2011 19:43
Last Modified: 15 Nov 2016 13:43


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