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Vidakovic,
Brani
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Brani
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ItemSelf-similarity in NMR Spectra: An Application in Assessing the Level of Cysteine,(Georgia Institute of Technology, 2010-01) Jung, Yoon Young ; Park, Youngja ; Jones, Dean P. ; Ziegler, Thomas R. ; Vidakovic, BraniHigh resolution of NMR spectroscopic data of biosamples are a rich source of information on the metabolic response to physiological variation or pathological events. There are many advantages of NMR techniques such as the sample preparation is fast, simple and non-invasive. Statistical analysis of NMR spectra usually focuses on differential expression of large resonance intensity corresponding to abundant metabolites and involves several data preprocessing steps. In this paper we estimate functional components of spectra and test their significance using multiscale techniques. We also explore scaling in NMR spectra and use the systematic variability of scaling descriptors to predict the level of cysteine, an important precursor of glutathione, a control antioxidant in human body. This is motivated by high cost (in time and resources) of traditional methods for assessing cysteine level by high performance liquid chromatograph (HPLC).
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ItemSelf-similarity in NMR spectra: an application in assessing the level of cysteine(Georgia Institute of Technology, 2007-01-15) Jung, Yoon Young ; Park, Youngja ; Jones, Dean P. ; Ziegler, Thomas R. ; Vidakovic, BraniHigh resolution of NMR spectroscopic data of biosamples are a rich source of information on the metabolic response to physiological variation or pathological events. There are many advantages of NMR techniques such as the sample preparation is fast, simple and non-invasive. Statistical analysis of NMR spectra usually focuses on differential expression of large resonance intensity corresponding to abundant metabolites and involves several data preprocessing steps. In this paper we estimate functional components of spectra and test their significance using multiscale techniques. We also explore scaling in NMR spectra and use the systematic variability of scaling descriptors to predict the level of cysteine, an important precursor of glutathione, a control antioxidant in human body. This is motivated by high cost (in time and resources) of traditional methods for assessing cysteine level by high performance liquid chromatograph (HPLC).
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ItemTwo-Step Wavestrapping: Simulating Non-Stationary Acceleration Data in the Mobile Computing Context(Georgia Institute of Technology, 2005-12-16) Yi, Ji Soo ; Jung, Yoon Young ; Jacko, Julie A. ; Sainfort, François ; Vidakovic, BraniMeasurements of acceleration provide an important source of information in understanding and predicting the movements of mobile computer users. However, collecting acceleration data can require substantial time and resources because it involves both human participation and the construction of adequate contextual environments. Thus, simulating acceleration data would be helpful to circumvent these difficulties. However, using traditional resampling techniques turned out to be inadequate for simulation of these non-stationary time series data. The present study proposes a wavelet-based resampling approach, called the “two-step wavestrapping,” which consists of parallel wavestrapping and energy/trend adjustments. This approach was applied to the acceleration data we collected from mobile computing users under six different contextual settings. The results showed that two-step wavestrapping can successfully generate surrogate acceleration data from the collected acceleration data.
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ItemLarger Posterior Mode Wavelet Thresholding and Applications(Georgia Institute of Technology, 2005-07-01) Cutillo, Luisa ; Jung, Yoon Young ; Ruggeri, Fabrizio ; Vidakovic, BraniThis paper explores the thresholding rules induced by a variation of the Bayesian MAP principle. The MAP rules are Bayes actions that maximize the posterior. The proposed rule is thresholding and always picks the mode of the posterior larger in absolute value, thus the name LPM. We demonstrate that the introduced shrinkage performs comparably to several popular shrinkage techniques. The exact risk properties of the thresholding rule are explored, as well. We provide extensive simulational analysis and apply the proposed methodology to real-life experimental data coming from the field of Atomic Force Microscopy.
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ItemBayesian False Discovery Rate Wavelet Shrinkage: Theory and Applications(Georgia Institute of Technology, 2005) Lavrik, Ilya A. ; Jung, Yoon Young ; Ruggeri, Fabrizio ; Vidakovic, BraniStatistical inference in the wavelet domain remains vibrant area of contemporary statistical research because desirable properties of wavelet representations and the need of scientific community to process, explore, and summarize massive data sets. Prime examples are biomedical, geophysical, and internet related data. In this paper we develop wavelet shrinkage methodology based on testing multiple hypotheses in the wavelet domain. The shrinkage/thresholding approach by implicit or explicit simultaneous testing of many hypotheses had been considered by many researchers and goes back to the early 1990’s. Even the early proposal, the universal thresholding, could be interpreted as a test of multiple hypotheses in the wavelet domain. We propose two new approaches to wavelet shrinkage/thresholding. (i) In the spirit of Efron and Tibshirani’s recent work on local false discovery rate, we propose the theoretical counterpart Bayesian Local False Discovery Rate, BLFDR, where the underlying model assumes unknown variances. This approach to wavelet shrinkage can also be connected with shrinkage based on Bayes factors. (ii) The second proposal to wavelet shrinkage explored in this paper is Bayesian False Discovery Rate, BaFDR. This proposal is based on ordering of posterior probabilities of hypotheses in Bayesian testing of multiple hypotheses. We demonstrate that both approaches result in competitive shrinkage methods by contrasting them to some popular shrinkage techniques