Yong Zeng

National Science Foundation, University of Missouri – Kansas City

Bayesian Inference via Filtering Equations for Financial Ultra-High Frequency Data

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.

Thursday, October 12, 2017 - 3:30pm
Room 306, Statistics Building 1130

Tharuvai Sriram

University of Georgia

Leverage-based Sequential Sampling Method for Streaming Time Series Data

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.

Thursday, September 21, 2017 - 3:30pm
Statistics Building Room 306

David Jones


Detecting planets: jointly modeling radial velocity and stellar activity time series

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.

Thursday, October 19, 2017 - 3:30pm
Room 306, Statistics Building 1130

Liang Peng

Georgia State University

New Robust Econometric Tests for a Dynamic Predictive Regression

Testing for predictability of asset returns has been a long history in economics and finance.

Thursday, November 2, 2017 - 3:30pm
Room 306, Statistics Building 1130

Anuj Srivastava

Florida State University

Recent Advances in Elastic Functional Data Analysis

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.

Thursday, August 31, 2017 - 3:30pm
Room 306, Statistics Building 1130


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