Analysis of observations on sequential events over time is common in real life. Sequential measurements over time describing the behavior of systems are usually called time series data, which have been collected in a wide range of disciplines. Over the years there have been multiple research areas in studying stochastic...
Agent-based models (ABM) are widely used in network data analysis, and due to their simple structures and sophisticated outcomes, they serve as good tools in understanding the dynamics in networks. In this thesis, we develop an agent-based dynamic network model, and show that it can replicate the expected degree distribution...
Analysis of stochastic models of networks is quite important in light of
the huge influx of network data in social, information and bio sciences, but
a proper statistical analysis of features of different stochastic models of networks
is still underway.We propose bootstrap subsampling methods for finding
empirical distribution of count...
Full Text:
BOOTSTRAP OF
COUNT FEATURES OF NETWORKS
By SharmodeepBhattacharyya∗,† and Peter J. Bickel∗
University of
Analysis of stochastic models of networks is quite important in light of
the huge influx of network data in social, information and bio sciences, but
a proper statistical analysis of features of different stochastic models of networks
is still underway.We propose bootstrap subsampling methods for finding
empirical distribution of count...
Full Text:
BY SHARMODEEPBHATTACHARYYA∗,† AND PETER J. BICKEL†
University of California, Berkeley∗ and Oregon
Analysis of stochastic models of networks is quite important in light of the huge influx of network data in social, information and bio sciences, but a proper statistical analysis of features of different stochastic models of networks is still underway.We propose bootstrap subsampling methods for finding empirical distribution of count...
Due to recent advances in computer technology, the cost of collecting and storing data has dropped drastically. This makes it feasible to collect large amounts of information for each data point. This increasing trend in feature dimensionality justifies the need for research on variable selection. Random forest (RF) has demonstrated...
Diverse scientific fields collect multiple time series data to investigate the dynamical behavior of complex systems: atmospheric and climate science, geophysics, neuroscience, epidemiology, ecology, and environmental science. Identifying patterns of mutual dependence among such data generates valuable knowledge that can be applied either for inferential or forecasting purposes. Vector autoregressive...
Moving mixed-model assembly lines are used by many companies to assemble multiple model types of a particular product. A moving assembly line consists of a material movement system that moves jobs at a constant velocity across a series of workstations. The variation in the work content of jobs at workstations...
Advancements in neurological data capturing and mapping have provided scientists with exponentially more information, allowing for more detailed analysis of neurological behaviors and their translations to cognitive, emotional, and physical function. Inspired by the diffculty in diagnosing brain damage, this project seeks to leverage statistical modeling with EEG data to...
This work presents an inductor less switched capacitor based energy harvester, which can simultaneously harvest from 2 energy sources (Solar + Piezo). The proposed harvester employs 2D maximum power point tracking algorithm, by changing the conversion ratios of the charge pumps for piezo and solar sources, and 1D voltage regulation...