Department of Mathematical Sciences

University of Nevada, Las Vegas

Statistics Colloquium/Seminar Series



[2006-2008] [2008-2009] [2009-2010] [2010-2011] [2011-2012] [2012-2013]

[2013-2014] [2014-2015]


For more information, contact the Colloquium/Seminar Coordinator, Dr. Hokwon Cho

(To see Math Dept Colloquia/Seminars, click next: Math Dept Seminar)


Fall 2015

Friday, October 2
CBC-C214, 11:30 am

(refreshments at 11:15 am)

Prof. Tapabrata Maiti
Department of Statistics & Probability

Michigan State University

Title: Variable Selection for High-dimensional Spatial Regression

[Abstract] Spatial regression is an important predictive tool in many scientific applications. When there are many available predictors, it is natural to select meaningful predictors. Penalized approach is proven to be effective for both variable selection and estimation simultaneously. We will talk some recent developments in this aspects of handling large spatial data.

Friday, October 23
CBC-C214, 11:30 am

(refreshments at 11:15 am)

Prof. Zuoheng A. Wang
Department of Biostatistics

Yale School of Public Health

Title: Mathematical Modeling of Dendritic Cell Population Dynamics

[Abstract] Dendritic cells (DCs) are crucial to shape body response to invaders including bacteria and viruses, thus serving as promising targets in immunotherapies for cancer and other diseases. Knowledge of DC dynamics is limited due to experimental difficulties. Combining both mathematical and experimental approaches, we characterized DC population dynamics at the steady state as well as under immune perturbation. Furthermore, using a computer-based algorithm, we quantified the intricate relationships among key parameters, such as cell death, proliferation and replenishment, many of which cannot be directly measured, for both steady-state (normal immune system) and cytokine-stimulated (representing immune perturbation) DC populations in the spleen. We found, in comparison to the steady state, DC proliferation increased 11-fold and surpassed cell death through shortening cell cycle duration by 80%, resulting in dramatic expansion of the splenic DC population. This study enables numerical evaluation of DC dynamics, a significant implication on design of DC-targeted treatments.

Friday, November 6
CBC-C214, 11:30 am

(refreshments at 11:15 am)

Prof. S. Rao Jammalamadaka
Department of Statistics & Applied Probability

University of California, Santa Barbara

Title: On the Robustness of Bayes Predictions in Linear Models

[Abstract] We consider the prediction problem for linear regression models with elliptical or spherically symmetric errors, a special case of which is the multivariate t-distribution with heavy tails. It is shown that the Bayes prediction density under the elliptical errors assumption is exactly the same as that for normally distributed errors when the prior information is objective or in the conjugate family. Thus assuming that the errors have a normal distribution when the true distribution is indeed elliptical, will not lead to incorrect predictive inferences. This extends some earlier work of Arnold Zellner and others.

Friday, November 20
CBC-C214, 11:30 am

(refreshments at 11:15 am)

Prof. Duchwan Ryu
Department of Mathematics & Statistics

Northern Illinois University

Title: Bayesian Multiple Inflated Poisson Regression for Infection Data

[Abstract] We propose a multiple inflated Poisson regression to model count responses containing excessive frequencies at more than one non-negative integer values in the presence of covariates. The well-known zero-inflated Poisson regression combines binary regression and Poisson regression for an excess of zero responses. To handle multiple excessive count responses we generalize the zero-inflated Poisson regression by replacing the binary regression to the multinomial regression. We discuss the properties of multiple inflated Poisson model along with regression models when some covariates are available, and use Bayesian computations for the complicated model estimations. As an application, in the study of infectious diseases which remain one of the greatest threats to human health, we observe excessive zeros and ones in the number of changes of infections and utilize the multiple inflated Poisson regressions.


Spring 2016

Friday, March 11, 2016
CBC-C138, 11:30 am

(refreshments at 11:15 am)

Prof. Hon Keung Tony Ng
Department of Statistical Sciences

Southern Methodist University

Title: Recent Advances in Precedence-type Tests and Applications

[Abstract] In this talk, we will provide a comprehensive overview of theoretical and applied approaches to a variety of problems in which precedence-type test procedures can be used. Then, some recent advances on precedence-type tests, including the extension to progressively censored data, a two-stage test for stochastic ordering in two samples, and a sequential procedure for two-sample problem will be discussed.

Friday, March 18, 2016
CBC-C138, 11:30 am

(refreshments at 11:15 am)

Prof. Jongphil Kim
Department of Biostatistics & Bioinformatics

H. Lee Moffitt Cancer Center & Research Institute


Department of Oncologic Sciences

University of South Florida

Title: Computation of Bivariate Normal and t probabilities, with Application to Comparisons of Normal Means in Cancer Studies


(Changed to: A Novel Method for Comparisons of Three Normal Means)

[Abstract] A novel method for the computation of the bivariate normal and t probability is presented. With suitable transformations, the probability over sets can be easily computed using exact one-dimensional numerical integration. An important application includes computing the exact critical points for the comparisons of three normal means for either the known or unknown variance problem. The critical points by one-dimensional integration can be computed using elementary numerical methods and are more accurate than those by the approximation methods and two-dimensional integration methods. The comparisons of reliability measurements from three populations are presented as an example of a known variance case.


Friday, April 22, 2015
CBC-C138, 11:30 am

(refreshments at 11:15 am)

Prof. Madhuri Mulekar
Department of Mathematics & Statistics

University of Southern Alabama

Title: Population Selection based on Count Data

[Abstract] The count data are popularly described using binomial, Poisson and negative binomial distributions in managerial applications to study consumer behavior, in entomological and ecological applications to study species' behavior, and in quality control applications to study systems' behavior. For each of these distributions, several solutions under different configurations are available for the problem of selecting a best population among the k available populations. Fixed-sample-size procedures with the indifference zone approach will be discussed. Interesting differences and similarities exhibited by these three distributions in the existence of selection procedures and the consistency of selection procedures will be explored.

Friday, May 6, 2015
CBC-C138, 11:30 am

(refreshments at 11:15 am)

Prof. Pankaj K. Choudhary
Department of Mathematical Sciences

University of Texas, Dallas

Title: Tolerance Bands for Functional Data

[Abstract] Often the object of inference in biomedical applications is a range that brackets a given fraction of individual observations in a population. A classical estimate of this range for univariate measurements is a `tolerance interval. In this talk, we discuss its natural extension for functional measurements, a `tolerance band,' and describe a methodology for constructing its pointwise and simultaneous versions that incorporates both sparse and dense functional data. Assuming that the measurements are observed with noise, the methodology uses functional principal component analysis in a mixed model framework to represent the measurements and employs bootstrapping to approximate the tolerance factors needed for the bands. The proposed bands also account for uncertainty in the principal components decomposition. Simulations show that the methodology has generally acceptable performance unless the data are quite sparse and unbalanced, in which case the bands may be somewhat liberal. The methodology is illustrated using two real datasets, a sparse dataset involving CD4 cell counts and a dense dataset involving core body temperatures.



Statistics Colloquium/Seminar Series