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]

[2014-2015] [Current Year]


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

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


Fall 2013

Friday, September 13
CBC-C116, 11:00 am

(refreshments at 10:45am)

Prof. Daniel Jeske
Department of Statistics

University of California, Riverside

Title: Neutral Zone Classifiers and their Applications

[Abstract]  Neutral zone classifiers allow for a region of neutrality when there is inadequate information to assign a predicted class with suitable confidence.  A neutral zone classifier is defined by classification regions that trade off the cost of an incorrect classification against the cost of remaining neutral.  In this talk, Bayes neutral zone classifiers will be discussed and it will be demonstrated that they outperform previous neutral zone classifiers with respect to the expected cost of misclassifications and also with respect to computational complexity.  A neutral zone classifier is illustrated with a microbial community profiling application in which no training data is available.  Previous applications of neutral zone classifiers have only dealt with the scenario where training data exists.  Extensions to sequential neutral zone classification will also be discussed.

Friday, November 8
CBC-C116, 11:00 am

(refreshments at 10:45am)

Prof. Bimal Sinha
Department of Mathematics & Statistics

University of Maryland, Baltimore County

Title: Data Analysis under Confidentiality Protection


[Abstract]  Statisticians working in most federal agencies are often faced with two conflicting objectives: (1) collect and publish useful data sets for designing public policies and building scientific theories, and (2) protect confidentiality of data respondents which is essential to uphold public trust, leading to better response rates and data accuracy. In this talk I will provide a survey of a few statistical methods currently used at the Census Bureau.

Friday, November 22
CBC-C116, 11:00 am

(refreshments at 10:45am)

Prof. T. N. Sriram
Department of Statistics

University of Georgia

Title: Sequential Fixed-Width Confidence Interval Based on

Bhattacharyya-Hellinger Distance Estimator


[Abstract]  We consider the construction of a sequential fixed-width confidence interval for the Bhattacharyya Hellinger (B-H) functional, T(g), when the random sample comes from an unknown probability mass function g. Using the empirical mass function, ^fn, to estimate g, we consider the minimum B-H distance estimator, T(^fn), introduced by Simpson (1987, JASA) to construct a sequential fixed-width confidence interval for T(g) of the form [T(^fNd) − d, T(^fNd) + d] based on a fully-sequential sampling scheme, Nd. The sequential procedure is shown to be asymptotically consistent and efficient, as d −> 0. When g = gθ,α,L = (1−α)fθ + αδL, which models an experiment where observations distributed according to a parametric model fθ are mixed with 100α% gross errors located at a large value L, we investigate the theoretical coverage probability of our sequential fixed-width confidence interval based on B-H estimator of θ and the expected sample size, as d -> 0. Here, we re-parametrize L = Ld such that Ld −> 1 as d −> 0. Some interesting contrasts are made with earlier studies involving the robustness of sequential fixed-width confidence intervals. Monte Carlo results are presented when fθ  is Poisson with mean θ.


Spring 2014

Friday, February 7, 2014
CBC-C114, 11:30 am

(refreshments at 11:15am)

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

University of California, Santa Barbara

Title: Middle-Censoring

[Abstract]  In connection with survival analysis and related problem, there is considerable literature which treats data that is censored from the left, the right or both.  In this talk, we consider situations where the data becomes unobservable if it falls inside a random interval in the middle, which we call middle-censoring. This happens in clinical trials and lifetime studies where a subject is temporarily absent or withdrawn from the study and the event of interest occurs during this period, so that the exact time of occurrence cannot be observed. This is also applicable in cases where some of the observations are so imprecise that they are stated as intervals, as for example the situation where peoples’ willingness to pay for a natural resource.  In this talk, we present a ”self-consistent estimator” and the Nonparametric MLE for the survival function, study its large-sample properties, and demonstrate how well it works even in the presence of heavy censoring. Presence of covariates can also be dealt with both in a parametric and semi-parametric set-up.

Friday, March 7, 2014
CBC-C114, 11:30 am

(refreshments at 11:15 am)

Dr. Chandrasekaran Kandhasamy
National Institute for Research in Tuberculosis

Indian Council of Medical Research (ICMR)

Ministry of Health, India

Title: Spatial count data modeling with CAR priors in Bayesian approach

[Abstract]  In spatial analysis, Poisson models are typically used to analyze count data such as disease incidence. One approach to minimize the unexplained variation in such models is to assume prior distributions for the linear or non-linear predictor term to include an extra random effect in Bayesian framework. The random effects in the above linear predictor model may have two components: correlated heterogeneity due to spatial aspect and uncorrelated heterogeneity due to unobserved covariates. Conditional autoregressive (CAR) models are assumed for the correlated heterogeneity and zero mean Gaussian distributions are assumed for the uncorrelated heterogeneity. In this talk, we will discuss the various currently available CAR models for spatial count data in the presence of covariate information. After an appropriate model choice using the Deviance Information Criterion (DIC), a map of the random effects from the resulting model is then prepared. We illustrate this procedure using Tuberculosis and HIV data from India for the year 2011.

Thursday, April 3, 2014
CBC-C116, 1:00 pm-2:15pm

(refreshments at 12:45 am)

Prof. Nitis Mukhopadhyay
Department of Statistics

University of Connecticut, Storrs

Title: Can We Make Teaching Probability and Statistical

Inference Exciting? You Decide

[Abstract]  Innovative teaching leads to serious research and I find this process very exciting. Lately, I have published extensively on various topics in probability, statistical inference, and linear models. Related research topics and publications originated directly from teaching graduate and undergraduate level courses in statistics. In this presentation, I will touch upon some selected topics from: independence, Rao-Blackwell and Lehmann-Scheffé theorems, multivariate normality, and invariant tests. I will highlight their origin and resolution.

Friday, April 4, 2014
CBC-C114, 11:30 am

(refreshments at 11:15 am)

Prof. Nitis Mukhopadhyay
Department of Statistics

University of Connecticut, Storrs

Title: On Sufficiency, Minimal Sufficiency, Information and Ancillarity:

Examples and Counterexamples

[Abstract]  Sufficiency, minimal sufficiency, information, and ancillarity are some of the deepest notions forming the very core of all statistical science laid down by R. A. Fisher nearly 100 years ago. Yet, intricacies are abound and many so-called “obvious results” often turn out false due to conceptual mix-up. In this presentation, I will happily explore some of the basic issues with the help of examples and counterexamples.



è Statistics Colloquium/Seminar Series