Functional near-infrared spectroscopy (fNIRS) is a relatively new neuroimaging technique. It is a low cost, portable, and non-invasive method to measure brain activity via the blood oxygen level dependent signal. Similar to fMRI, it measures changes in the level of blood oxygen in the brain. Its time resolution is much finer than fMRI, however its spatial resolution is much courser--similar to EEG or MEG.
The Statistics Department hosts weekly colloquia on a variety of statistcal subjects, bringing in speakers from around the world.
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We propose a new self-normalized method for testing change points in the time series setting. Self-normalization has been celebrated for its ability to avoid direct estimation of the nuisance asymptotic variance and its flexibility of being generalized to handle quantities other than the mean. However, it was developed and mainly studied for constructing confidence intervals for quantities associated with a stationary time series, and its adaptation to change-point testing can be nontrivial as direct implementation can lead to tests with nonmonotonic power.
The rapid growth of geospatial-temporal data sources from satellites, drones, weather modeling, IoT sensors etc., accumulating at a pace of petabytes to exabytes annually, opens unprecedented opportunities for industrial applications. However, the sheer size and complexity of the data also raises considerable challenges for conventional tools, relational geospatial databases, and cloud geospatial data services based on file systems (manifested as object stores or “cold” tape storages).
We propose a general partially-observed framework of Markov processes with marked point process observations for ultra-high frequency (UHF) transaction price data, allowing other observable economic or market factors. We develop the corresponding Bayesian inference via filtering equations to quantify parameter and model uncertainty. Specifically, we derive filtering equations to characterize the evolution of the statistical foundation such as likelihoods, posteriors, Bayes factors and posterior model probabilities.
We consider a streaming time series data, which is assumed to come from a non-explosive p-th order autoregressive (AR(p)) model with p ≥ 1. Our goal is to estimate the parameters of this model using a subsample of random size drawn sequentially from the streaming data based on a stopping rule. Traditionally, sequential sampling is carried out after observing an initial sample of fixed size. However, our sampling starting point is chosen according to statistical leverage scores of the data and the subsample size is decided by a sequential sampling rule.
The radial velocity technique is one of the two main approaches for detecting planets outside our solar system, or exoplanets as they are known in astronomy. When a planet orbits a star it causes the star to move and this induces a Doppler shift (i.e. the star light appears redder or bluer than expected), and it is this effect that the radial velocity method attempts to detect. Unfortunately, these Doppler signals are typically contaminated by various "stellar activity" phenomena, such as dark spots on the star surface.
Testing for predictability of asset returns has been a long history in economics and finance.
Functional data analysis (FDA) is fast becoming an important research area, due to its broad applications in many branches of science. An essential component in FDA is the registration of points across functional objects. Without proper registration the results are often inferior and difficult to interpret. The current practice in FDA community is to treat registration as a pre-processing step, using off-the-shelf alignment procedures, and follow it up with statistical analysis of the resulting data.