**Statistics Seminar**

**Dao Nguyen**

University of California-Berkeley

**Iterated Filtering and Iterated Smoothing Algorithms** (pdf)

**David Mason**

University of Delaware

**Bootstrapping the Student t‐Statistic** (pdf)

**Yichuan Zhao**

Georgia State University

**Jackknife Empirical Likelihood Methods for the Gini Index** (pdf)

A variety of statistical methods have been developed to the interval estimation of a Gini index, one of the most widely used measures of economic inequality. However there is still plenty of room for improvement in terms of coverage accuracy and interval length. In this paper, we propose interval estimators for the index and the difference of two Gini indices via jackknife empirical likelihood. Via expressing the estimating equations in the form of U-statistics, our method can be simply applied as the standard empirical likelihood for an univariate mean and avoid maximizing the profile empirical likelihood for the difference of two Gini indices. Simulation studies show that our method is comparable to existing empirical likelihood methods in terms of coverage accuracy, but yields shorter intervals. The proposed methods are illustrated using a real data set. This is joint work with Dongliang Wang and Dirk Gilmore.

**Junying Zhang**

Taiyuan University of Technology, Taiyuan, P. R. China

**Marginal Empirical Likelihood Independence Screening in Sparse Ultrahigh Dimensional Additive Models** (pdf)

Additive models have been proven to be very useful as they increase the flexibility of the standard linear model and allow a data-analytic transform of the covariates to enter into the linear model. In a high-dimensional setting where the dimensionality grows exponentially with the sample size, the urgent issue is to reduce dimensionality from high to a moderate scale. In this paper, we investigate the marginal empirical likelihood screening methods for ultrahigh dimensional additive models. The proposed nonparametric screening method selects variables by ranking a measure of the marginal empirical likelihood ratio evaluated at zero in order to differentiate the contributions of each covariate made to the response variable. We show that, under some mild technical conditions, the proposed marginal empirical likelihood screening method has a sure screening property. And the extent to which the dimensionality can be reduced is also explicitly quantified. We also propose a data-driven thresholding and an iterative marginal empirical likelihood method to enhance the finite sample performance for fitting sparse additive models. Simulation results and real data analysis demonstrate that the proposed methods work competitively and perform better than the competing methods in a heteroscedastic scenario.

**Yimin Xiao**

Michigan State University

**On the Excursion Probabilities of Gaussian Random Fields** (pdf)

Excursion probabilities of Gaussian random fields have many applications in statistics (e.g., scanning statistic and control of false discovery rate (FDR)) and in other areas. In this talk, we consider the excursion probabilities of two types of multivariate Gaussian random fields: those with smooth sample functions, and those with non-smooth (or fractal) sample functions. An important class of such random fields are those with Matérn cross-covariance functions studied by Gneiting, Kleiber, and Schlather (2010). For smooth Gaussian random fields, it is shown that the “Expected Euler Characteristic Heuristic” still holds; and non-smooth Gaussian random fields, we prove an asymptotic result which extends those of Pickands (1969), Piterbarg (1996) and Piterbarg and Stamatovich (2005). The methods for establish these two types of results are very different. This talk is based on joint works with Dan Cheng and Yuzhen Zhou.

**Charles Katholi**

University of Alabama at Birmingham

**Estimating Proportions by Group Testing: A Frequentist Approach** (pdf)

Group testing is a statistical method for estimating proportions of a factor in a population by testing pools of subjects rather than individuals. Although group testing has been in the literature since 1943, the use of the method has increased dramatically in the last two decades because of the development of screening methods based on DNA amplification by PCR. The probability model for this approach is presented and a short history of the method and a survey of known results are given. Two cases are distinguished; when the pool sizes are equal and when they are not equal. A new approach to finding point and interval estimates in the case of unequal pool sizes is described and the statistical properties of the estimators are explored.

**Cuilan Gao**

St. Jude Children’s Research Hospital

**Evaluate Agreement of Differential Expression for Translational Cross-Species Genomics** (pdf)

**Yang Cheng**

Mississippi State University

**Orbit Uncertainty Propagation Using Sparse Grid-Based Method** (pdf)

**Meng Zhao**

Mississippi State University

**Local Linear Regression with Censored Data** (pdf)

**Pradeep Singh**

Southeast Missouri State University

**A Modified Approach in Statistical Significance for Genome Wide Studies** (pdf)

**Ebenezer Olusegun George**

University of Memphis

**On the Exchangeable Multinomial Distribution** (pdf)

**Deo Kumar Srivastava**

St. Jude Children’s Research Hospital

**Robust Multiple Regression based on Winsorization and Bootstrap Methods** (pdf)

**Paul Schliekelman**

University of Georgia

**Integrating Genome-wide Expression Information into Genome Scans for Complex Traits** (pdf)

**Justin Shows**

Mississippi State University

**Sparse Estimation and Inference for Censored Median Regression** (pdf)

**Hanzhe Zheng**

Merck Research Laboratories

**Adaptive Design in Clinical Trials** (pdf)

**Stan Pounds**

St. Jude Children’s Research Hospital

**Reference Alignment of SNP Microarray Signals for Copy Number Analysis of Tumors** (pdf)

**Russell Stocker**

Mississippi State University

**Optimal Goodness-of-Fit Tests** (pdf)

**Gauri Sankar Datta**

University of Georgia

**Bayesian approach to survey sampling** (pdf)

**Dawn Wilkins**

University of Mississippi

**Supervised and Unsupervised Learning with Microarray Data** (pdf)

**Hemant K. Tiwari**

University of Alabama at Birmingham

**Issues & Challenges in Genetic Analysis of Complex Disorders** (pdf)

**Ajit Sadana**

University of Mississippi

**A Fractal Analysis of Binding and Dissociation Kinetics of Glucose and Related Analytes on Biosensor Surfaces** (pdf)

**Jane L. Harvill**

Mississippi State University

**Modeling and Prediction for Nonlinear Time Series** (pdf)

**Fenghai Duan**

Yale School of Public Healthy

**Probe-level Correction in Analysis of Affymetrix Data** (pdf)

**J. Sunil Rao**

Case Western Reserve University

**Spike and slab variable selection: frequentist and Bayesian strategies (in DNA microarray data analysis)** (pdf)

**Warren May**

University of Mississippi Medical Center

**On Being a Statistician in a Medical Center Environment** (pdf)

**Malay Ghosh**

University of Florida

**Hierarchical Bayesian Neural Networks: An Application to Prostate Cancer Study** (pdf)

**Pranab K. Sen**

University of North Carolina at Chapel Hill

**Constrained Inference in Statistical Practice** (pdf)

**Ebenezer Olusegun George**

University of Memphis

**Statistical Methods for Analyzing Clustered Discrete Data: Applications to Teratology Studies** (pdf)

**Haimeng Zhang**

Concordia College

**Estimating Survival Functions In Koziol-Green Models** (pdf)

**Deo Kumar Srivastava**

St. Jude Children’s Hospital

**Impact of Censoring in Survival Analysis** (pdf)

**Z. Govindarajulu**

University of Kentucky

** Robustness of Small Sample Size Re-estimation Procedures** (pdf)

**Xueqin Wang**

University of Mississippi

**Asymptotics of the Theil-Sen Estimator in Simple Linear Regression Model With a Random Covariate** (pdf)

**Xueqin Wang**

University of Mississippi

**Unbiasedness of the Theil-Sen Estimator** (pdf)

**Patrick D. Gerard**

Mississippi State University

**Estimating Polulation Density in Line Transect Sampling Using Kernel Methods** (pdf)