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Colloquia & Seminars
Colloquia & seminar talks are scheduled from 1:30pm  2:45pm on Thursday each week and usually take place in DMSC 104, unless otherwise noted below. Speakers give 50minute presentations on various mathematical and statistical topics.
If you would like to meet with a speaker, please contact math@unr.edu to schedule a meeting. To receive email announcements about future talks and events, please subscribe to our email list by sending an email to sympa@lists.unr.edu with a blank subject line and the main body 'subscribe mathstatannounce EmailAddress FirstName LastName'.
We look forward to your participation in our upcoming colloquia!
Date  Speaker  Institution  Title  Room 

Sep. 13, 2018  Agnieszka Wylomanska  Wroclaw University of Science and Technology  How to Model Data with Anomalous Diffusion Behavior? Click for Abstract... The classical financial models are based on the standard Brownian diffusiontype processes. However, in the exhibition of some real market data (like interest or exchange rates) we observe characteristic periods of constant values. Moreover, in the case of financial data, the assumption of normality is often unsatisfied. In such cases the popular Vasiček model, that is a mathematical system describing the evolution of interest rates based on the OrnsteinUhlenbeck process, seems not to be applicable. Therefore, we propose an alternative approach based on a combination of the popular OrnsteinUhlenbeck process with a stable distribution and subdiffusion systems that demonstrate such a characteristic behavior. The probability density function of the proposed process can be described by a FokkerPlanck type equation and therefore it can be examined as an extension of the basic OrnsteinUhlenbeck model. We propose the parameter's estimation method and calibrate the subordinated Vasiček model to the interest rate data. 
DMSC 104 
Sep. 20, 2018  Wei Yang  UNR  Overview of UNR School of Community Health Sciences Biostatistics Program: Faculty, Research Interests and Projects Click for Abstract... TBA 
DMSC 104 
Sep. 27, 2018  Daniel Lautzenheiser  UNLV  Generalized Apollonian Packings and Hausdorff Dimension Click for Abstract... In this talk, we discuss counting methods which admit rigorous upper and lower bounds on the Hausdorff (or Besikovitch) dimension of two generalized Apollonian circle packings. We find that the Hausdorff dimension of each packing is strictly greater than that of the Apollonian packing, supporting the unsolved conjecture that, among the many possible disk tilings of the plane, the Apollonian packing has the smallest possible residual set dimension. The obtained bounds are also consistent with calculated heuristic estimates. 
DMSC 104 
Oct. 4, 2018  Mark Colarusso  University of South Alabama  GelfandZeitlin Integrable Systems: Where Linear Algebra, Geometry, & Representation Theory meet Click for Abstract... In the 19th century, physicists were interested in determining the conditions under which the equations of motion for a classical mechanical system could be found by integrating a finite number of times. Such a system was said to be completely integrable. Using symplectic geometry, we can generalize the notion of an integrable system beyond the realm of physics and into Lie theory and representation theory. Such "abstract" integrable systems can be used to geometrically construct infinite dimensional representations of Lie algebras. 
DMSC 104 
Oct. 11, 2018  Allison Moore  UC Davis  Band surgery along knots and sitespecific recombination on circular DNA Click for Abstract... Band surgery is a topological operation that transforms a 
DMSC 104 
Oct. 18, 2018  Edward J Bedrick  University of Arizona  Data Reduction Prior to Interface: What are the Consequences of Using Principal Component Scores to Make Group Comparisons in a Student's TTest or ANOVA? Click for Abstract... There has been a significant recent development of statistical methods for inference with highdimensional data. Despite these developments, biomedical researchers and computational scientists often use a simple twostep step process to analyze high dimensional data. First, the dimensionality is reduced using a standard technique such as principal component analysis, followed by a group comparison using a ttest or analysis of variance. In this talk, I will try to untangle a number of issues associated with this approach, starting with the simplest but most vexing question (since this is left unstated)  what hypothesis is being tested? I will use a combination of approaches, including asymptotics, analytical construction of worst case scenarios, and simulation based on actual data to address whether this approach is sensible. Although asymptotics will consider a nonsparse setting, some discussion of implications in sparse problems will be given. 
DMSC 104 
Oct. 25, 2018  Brandon Koch  School of Community Health Sciences, UNR  A New Approach for Variable Selection in Causal Inference Click for Abstract... Estimating the causal effect of a binary intervention or action (referred to as a "treatment") on a continuous outcome is often an investigator's primary goal. Randomized trials are ideal for estimating causal effects because randomization eliminates selection bias in treatment assignment. However, randomized trials are not always ethically or practically possible, and observational data must be used to estimate the causal effect of treatment. Unbiased estimation of causal effects with observational data requires adjustment for confounding variables that are related to both the outcome and treatment assignment. Adjusting for all measured covariates in a study protects against bias, but including covariates unrelated to outcome may increase the variability of the estimated causal effect. Standard variable selection techniques aim to maximize predictive ability of a model for the outcome and are used to decrease variability of the estimated causal effect, but they ignore covariate associations with treatment and may not adjust for important confounders weakly associated to outcome. In this talk, I will discuss GLiDeR (Group Lasso and Doubly Robust Estimation), a novel variable selection technique for identifying confounders and predictors of outcome using an adaptive group lasso approach that simultaneously performs coefficient selection, regularization, and estimation across the treatment and outcome models. The selected variables and corresponding coefficient estimates are used in a standard doubly robust average causal effect estimator. I provide asymptotic results that show, for a broad class of data generating mechanisms, GLiDeR yields a consistent estimator of the average causal effect when either the outcome or treatment model is correctly specified. A simulation study shows that GLiDeR is more efficient than doubly robust methods using standard variable selection techniques and has substantial computational advantages over a recently proposed doubly robust Bayesian model averaging method. We illustrate our method by estimating the average causal treatment effect of bilateral versus singlelung transplant on forced expiratory volume in one year after transplant using an observational registry. 
DMSC 104 
Nov. 1, 2018  Huixi Li  UNR  Why is 1 + 1 greater than 1 + 2 in number theory? Click for Abstract...

DMSC 104 
Nov. 8, 2018  Noah Forman  University of Washington  TBA Click for Abstract... TBA 
DMSC 104 
Nov. 15, 2018  Dan Nakano  University of Georgia  TBA Click for Abstract... TBA 
DMSC 104 
Nov. 29, 2018  Swatee Naik  NSF  TBA Click for Abstract... TBA 
DMSC 104 
Dec. 6, 2018  TBA  TBA  TBA Click for Abstract... TBA 
DMSC 104 
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