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Empirical Frequency Band Analysis of Nonstationary Time Series
Abstract The time-varying power spectrum of a time series process is a bivariate function that quantifies the magnitude of oscillations at different frequencies and times. To obtain low-dimensional,Expand
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A scalable framework for NBA player and team comparisons using player tracking data
  • Scott Bruce
  • Computer Science, Mathematics
  • 13 November 2015
In this paper, Principal Component Analysis is used to identify four principal components that account for 68% of the variation in player tracking data from the 2013-2014 regular season and intuitive interpretations of these new dimensions are developed by examining the statistics that influence them the most. Expand
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Nonparametric Distributed Learning Architecture for Big Data: Algorithm and Applications
We present MetaLP, a flexible, distributed statistical modeling framework suitable for large-scale data analysis, where statistical inference meets big data computing. Expand
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Conditional adaptive Bayesian spectral analysis of nonstationary biomedical time series.
Many studies of biomedical time series signals aim to measure the association between frequency-domain properties of time series and clinical and behavioral covariates. However, the time-varyingExpand
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Measuring Variability in Rest-Activity Rhythms from Actigraphy with Application to Characterizing Symptoms of Depression
The twenty-four hour sleep-wake pattern known as the rest-activity rhythm (RAR) is associated with many aspects of health and well-being. Researchers have utilized a number of interpretable,Expand
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Nonparametric Distributed Learning Framework: Algorithm and Application to Variable Selection
The big data era is here but where are the tools to analyze them? Dramatic increases in the size of datasets have made traditional "centralized" statistical inference techniques prohibitive.Expand
Evaluating Statistical Diversity in the NBA Using Player Tracking Data
Classification of Categorical Time Series Using the Spectral Envelope and Optimal Scalings
This article introduces a novel approach to the classification of categorical time series under the supervised learning paradigm. To construct meaningful features for categorical time seriesExpand
Conditional adaptive Bayesian spectral analysis of replicated multivariate time series.
This article introduces a flexible nonparametric approach for analyzing the association between covariates and power spectra of multivariate time series observed across multiple subjects, which weExpand
Adaptive Frequency Band Analysis for Functional Time Series
The frequency-domain properties of nonstationary functional time series often contain valuable information. These properties are characterized through its time-varying power spectrum. PractitionersExpand