We develop statistical tools for time series analysis of high-dimensional multivariate datasets, when a few core series are of principal interest and there are many potential ancillary predictive variables. Themethodology, based on Vector Autoregressions (VAR), handles the case where unrestricted fitting is precluded by the large number of serie...
Creator:
McElroy, Tucker (U.S. Bureau of the Census)
Created:
2018-02-21
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
The Vector AutoRegressive (VAR) Model is a popular model for the analysis of a multivariate time series. It allows to investigate the impact changes in one time series have on other ones. A drawback of the VAR is the risk of overparametrization because the number of parameters increases quadratically with the number of included time series. This...
Creator:
Croux, Christophe (EDHEC Business School)
Created:
2018-02-21
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
This paper presents a novel nonlinear framework for the construction of flexible multivariate dependence structure~(i.e., copula) from existing copulas based on a straightforward "pairwise max" rule. The newly constructed max-copula has a closed form and has strong interpretability. Compared to the classical "linear symmetric" mixture copula, th...
Creator:
Zhang, Zhengjun (University of Wisconsin, Madison)
Created:
2018-02-21
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
The financial crisis of 2007-2008 has caused severe economic andpolitical consequences over the world. An interesting question from this crisis is whether or to what extent such sharp changes or structuralbreaks in the market can be explained by economic and market fundamentals.To address this issue, we consider a model that extracts the informa...
Creator:
Xing, Haipeng (State University of New York, Stony Brook (SUNY))
Created:
2018-02-21
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
High-dimensional point processes have become ubiquitous in many scientific fields. For instance, neuroscientists use calcium florescent imaging to monitor the firing of thousands of neurons in live animals. In this talk, I will discuss new methodological, computational and theoretical developments for learning neuronal connectivity networks from...
Creator:
Shojaie, Ali (University of Washington)
Created:
2018-02-21
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Possessing such remarkable visual capabilities, humans believe they can always perceive and understand the shape of objects correctly. Under the phenomenon of visual illusion, however, what humans see differs from reality. "Impossible objects" are a type of 3D visual illusion that are 2D pictures which give the impression of having inconsistent ...
Creator:
Sugihara, Kokichi (Meiji University)
Created:
2018-02-21
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
We seek statistical methods to study the occurrence of multiple rain types observed by satellite on a global scale. The main scientific interests are to relate rainfall occurrence with various atmospheric state variables and to study the dependence between the occurrences of multiple types of rainfall (e.g. short-lived and intense versus long-li...
Creator:
Jun, Mikyoung (Texas A & M University)
Created:
2018-02-21
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In kin-cohort studies, clinicians and genetic counselors are interested in providing their patients the most current cumulative risk of a disease arising from a rare deleterious mutation. Estimating the cumulative risk is difficult, however, when the genetic mutation status in patients is unknown and, instead, only estimated probabilities of a p...
Creator:
Garcia, Tanya (Texas A & M University)
Created:
2018-02-21
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In this talk, we will give an overview of statistical methodologies for spectral analysis of time series data. We will briefly discuss the common approaches for spectral analysis, and discuss their limitations for analyzing data whenever the study has a longitudinal experimental design. To address the limitations, we propose a Bayesian model for...
Creator:
Fiecas, Mark (University of Minnesota, Twin Cities)
Created:
2018-02-21
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.