Probability & Statistics Seminar
Seminars for Fall 2024
10:00am – 10:50am
Na Zhang
Towson University
On stochastic processes
In a comprehensive study of feed-forward ReLU neural networks, Grigsby et al. (2022) explore the functional dimension of such networks, which measures a network’s expressiveness. One factor contributing to a functional dimension below the maximal level is the presence of stably inactivated neurons. In this work, we analyze a feed-forward neural network with input dimension n. We show that the probability of a neuron being stably inactivated in the second hidden layer is: (2n + 1)/(4n+1)when the first hidden layer has n+1 neurons, and is 1/(2^n1+1)when the first hidden layer has n1 neurons where n1≤ n. Moreover, a conjecture for more general case when n1≥ n+1 will be proposed, along with supporting experimental evidence presented at the end.
10:00am – 10:50am
Sahifa Siddiqua
University of Mississippi/ University of Wisconsin
Abstract posted when available
10:00am – 10:50am
Martial Longla
University of Mississippi
Idempotent copulas, m-dependence and Markov chains
New copula families are constructed following the ideas of Longla (2023). Subclasses of idempotent copulas with square integrable densities are derived. It is shown that these copulas generate exchangeable Markov chains that behave like independent and identically distributed random variables conditionally on the initiial variable. We prove that the extracted copula family is the only set of symmetric idempotent copulas with square integrable densities. We extend these copula families to asymmetric copulas with square integrable densities with special dependence properties. One of our extensions includes the Farlie-Gumbel-Morgenstern copula family (FGM). Mixing properties of Markov chains generated by these copulas follow from Longla (2022c) and Longla (2015). The Spearman’s correlation coefficient ρ is provided for each of these copula families. Some graphs are provided to illustrate the properties of the copula densities.
10:00am – 10:50am
Romuald Momeya
Canada
Local risk-minimization under partially observed Markov-modulated Exponential Levy model
The option hedging problem for a Markov-modulated exponential Lévy model is examined. This incomplete market model is based on a Markov additive process for which the Markov component plays a prominent role. In general, the full information about this component is not available to the investor in the market. Thus, we study optimal hedging strategies for European derivatives based on a model with partial or incomplete information. We employ the local risk-minimization approach to develop optimal hedging strategies under full information. Then, we project the hedging strategies on the observed information to obtain hedging strategies under partial information.
*In Hume 305
10:00am – 10:50am
Zamzam Qazi
University of Mississippi
On estimation problems based on Type III Longla copulas
We will explore parameter estimation for a new class of non-symmetric copulas. We provide asymptotic distributions for parameter estimators. We study a simple random sample from the bivariate copula, then extend the work to the stationary Markov chain framework. Asymptotic normality is established for proposed estimators and the MLE. The proof uses the unique properties of the eigenfunctions of the copula’s functional operator and the mixing structure of the Markov chains. A simulation study involving sine, cosine, and FGM copulas is presented. This simulation compares the performance of the proposed estimator with that of other existing estimators.
* In CSTI Conference Room D04 (4th floor east side)
10:00am – 10:50am
Yiyuan She
Florida State University
Range Penalization: Theoretical Insights with Applications in Federated Learning
This talk introduces range regularization to enhance statistical accuracy and transmission efficiency, essential for reducing communication and computational demands in federated learning without compromising performance. Our approach identifies features with shared weights across different clients and adaptively clusters the weights of personalized features at extreme values, a process we refer to as polar clustering.Theoretical analysis of the associated estimators poses significant challenges due to the seminorm nature and non-decomposability of the regularizer. We develope new proof techniques for the nonasymptotic analysis of statistical accuracy and faithful pattern recovery. Moreover, a fast optimization algorithm that leverages varying degrees of local strong convexity is proposed to reduce iteration complexity. Experiments support the efficacy and efficiency of our approach.
See Previous Seminars
Previous Statistics Seminars
9:00am – 9:50am
Barkat Mian
University of Mississippi
On planar Brownian motion singularly tilted through a point potential
We will discuss a special family of two-dimensional diffusions, defined over a finite time interval [0, T]. These diffusions have transition density functions that are given by the integral kernels of the semigroup corresponding to the two-dimensional Schrodinger operator with a point potential at the origin. Although, in a few ways, our processes of interest are closely related to two-dimensional Brownian motion, they have a singular drift pointing in the direction of the origin that is strong enough to enable the possibly of visiting there with positive probability. Our
main focus is on characterizing a local time process at the origin for these diffusions analogous to that for a one-dimensional Brownian motion.
9:00am – 9:50am
Dr Paramahansa Pramanik
University of South Alabama
On Estimation of Function-on-function Regression Kernels with Brownian Berkson Errors.
In this paper, we introduce a new methodology to determine an optimal kernel of function-on-function regression in the presence of a stochastic differential equation with Berkson error. We assume that the response variable, unobserved true predictor, the best available observed measure of the true predictor, and the regression kernels are functions of space and time, and the regressor dynamics follow a stochastic differential equation. First, we construct an objective function as a time-dependent Mean Square Error (MSE) and then minimize it with respect to regression coefficients subject to stochastic Berkson error dynamics. A Feynman-type path integral control approach is used to determine a Wick-rotated Schrodinger-type equation that has the complete information of the system. Using first-order conditions for these kernels give us a closed-form solution.
9:00am – 9:50am
Dr. Xiang Huang
Vanderbilt University
Testing and estimating treatment effect in the presence of delayed onset of the effect for cancer immunotherapies
The standard log-rank test has been extended by adopting various weight functions. Cancer vaccine or immunotherapy trials have shown a delayed onset of effect for the experimental therapy. This is manifested as a delayed separation of the survival curves. We propose new weighted log-rank tests to account for such delay. We implement a numerical evaluation of the Schoenfeld approximation (NESA) for the mean of the test statistic. The NESA enables us to assess the power and to calculate the sample size for detecting such delayed treatment effect and also for a more general specification of the non-proportional hazards in a trial. Extensive simulation studies are conducted to compare the performance of the proposed tests with the standard log-rank test and to assess their robustness to model mis-specifications. Our tests outperform the Gρ,γ class in general and have performance close to the optimal test. We demonstrate our methods on two cancer immunotherapy trials.
9:00am – 9:50am
Dr Huybrechts Bindele
University of South Alabama
Rank-based Estimating Equation with non-ignorable missing responses via empirical likelihood
In this talk, we will consider a general regression model with responses missing not at random. We will consider a rank-based estimating equation of the regression parameter from which a rank-based estimator will be derived. Based on its asymptotic normality property, a consistent sandwich estimator of the corresponding asymptotic covariance matrix is developed. In order to overcome the under coverage issue of the normal approximation procedure, the empirical likelihood based on the rank-based gradient function is defined, and its asymptotic distribution is established. Extensive simulation experiments under different settings of error distributions with different missing mechanisms will be considered, and the simulation results will show that the proposed empirical likelihood approach has better performance in terms of coverage probability and average length of confidence intervals for the regression parameters compared with the normal approximation approach and its least-squares counterpart. A real data example will be provided to illustrate our methods.
9:00am – 9:50am
Dr. Kalimuthu Krishnamoorthy
University of Louisiana
Fiducial Inference with Applications
Fiducial distribution for a parameter is essentially the posterior distribution with no a prior distribution on the parameter. In this talk, we shall describe Fisher’s method of finding a fiducial distribution for a parameter and fiducial inference through examples involving well-known distributions such as the normal and binomial. We then describe the approach for finding fiducial distributions for the parameters of a location-scale family. In particular, we shall see fiducial methods for finding confidence intervals, prediction intervals and prediction limits for the mean of a future sample. Application to analysis of zero-inflated lognormal data will also be discussed. All the methods will be illustrated using some practical examples.
9:00am – 9:50am
Dr. Ngartelbaye Guerngar (Serge)
University of North Alabama
Phase transition for Fractional Stochastic Partial Differential Equations in Bounded Domains
Stochastic partial differential equations (SPDEs) are partial differential equations (PDEs) with a random component. They have many applications in many areas of science and engineering. In this talk, I will discuss an interesting property of the solution of an SPDE driven by a Gaussian noise in a bounded domain. This is based on my joint work with E. Nane (Auburn University) and M. Foondun (University of Strathclyde).
9:00am – 9:50am
Mr. Thierry Taning Longla
PhD Student and Data analyst, Yildiz Technical University (Turkey)
Investigating the robustness of wireless sensor networks
Human designs are susceptible to being corrupted by errors or failures. Many social and natural systems have strange abilities to resist failures and maintain basic functions even when some of their components fail. Robustness is a key question in many disciplines, such as biology, economics, and security, just to mention a few. The evolution of telecommunications nowadays has made the world a small village. The arrival of 5G and 6G has boosted the digitization and proliferation of the Internet of Things, with sensors as key elements. Depending on the environment where sensors are deployed, these networks are subject to several constraints, necessitating the design of robust systems for them. In this project, a robustness analysis of complex networks, particularly sensor networks, was done. Sensor devices here are viewed as vertices, the wireless link between the nodes as edges, and the information exchange between the nodes as weights. We did an investigation of the effect of node removal in complex networks. To quantify or assess the level of robustness of wireless sensor networks, we used the inverse percolation and the Laplacian matrix fielder value. We interpret the results of our experiments. and proposed some topologies to be used in building robust sensor networks. Python programming language was used for simulations and computations. In addition, we use network software such as Pajek and yED for visualization and analysis.
9:00am – 9:50am
Dr. Qingyang Zhang
University of Arkansas
On relationships between Chatterjee’s and Spearman’s correlation coefficients
In his seminal work, Chatterjee (2021) introduced a novel correlation measure which is distribution-free, asymptotically normal, and consistent against all alternatives. In this talk, we will study the probabilistic relationships between Chatterjee’s correlation and the widely used Spearman’s correlation. We will show that, under independence, the two sample-based correlations are asymptotically joint normal and asymptotically independent. For dependent variables, we will establish some extremal cases featuring large differences between these two metrics. Motivated by these findings, a new independence test is proposed by combining Chatterjee’s and Spearman’s correlations into a maximal strength measure of variable association. We will use both simulated data and a real-world dataset to show the good sensitivity of the new test to different correlation patterns.
9:00am – 9:50am
Dr. Melik Masarifoglu
Senior Data Analyst at NMQ Digitals (Turkey)
Predictive Analytics for E-Commerce Sales
Consider a hotel chain that wants to predict how many customers will stay in a certain location this weekend so they can ensure they have enough staff and resources to handle . Steward Health Care, the largest for-profit private hospital operator in the United States (38 hospitals). They predicted hospital volume. Why? They found out that during peaks in patient volume, the hospital is often understaffed, and during valleys in patient volume, the hospital is often overstaffed. This is highly inefficient, and typically leads to hospitals incurring extremely high expenses for on-call staff and overtime pay . With predictive analytics, they saved 2 million dollars (just eight of the 38 hospitals in Steward’s network). Energy Companies predict energy consumption to manage capacity planning. This talk is about such applications of predictive analytics.
9:00am – 9:50am
Wilfried Youmbi
Department of Economics at the University of Western Ontario
Nonparametric Analysis of Random Coalitional Multi-Utility Models
In this paper, we study a method for testing the rational behavior of a population of consumers when observed choice data reveal a non-transitive preference relation. To this end, we develop a stochastic version of the coalitional multi-utility (CMU) model developed in Aguiar et al. (2022). The motivating application is to test the null hypothesis that a sample of cross-sectional demand distributions was generated by a population of rational consumers with complete, but not necessarily transitive, preference relations. We test a necessary and sufficient condition that does not rely on any restriction on unobserved heterogeneity or the number of goods. We provide an empirical characterization of this novel stochastic choice model and show that this characterization can be tested statistically. We also show how to evaluate the welfare implications of an observed price change. This work is a generalization of Kitamura & Stoye (2018)’s work on the nonparametric test of random utility models (RUM) for finite choice sets to situations where preferences are not transitive. We apply the new test to the UK Family Expenditure Survey (FES) and find evidence against RUM, while the random CMU model is not rejected in the dataset.
11:00am – 12:00pm
Dr. Theophile Bougna Lonla
World Bank Economist
Poverty and transport modeling: Perspectives offered by Big Data and Machine Learning
Data and good models are at the forefront of all efficient decision-making processes, especially for poverty alleviation and transport planning. Technological advancement and the recent developments in ‘Big Data’ and machine learning provide useful information and methods that are nice complements to data collected through conventional methods and traditional models. The identification of key challenges and the current knowledge gaps in poverty and transport modeling are explored. Practical examples of how machine learning and big data are combined with statistical and economic models to tackle poverty and transport challenges. Promising areas for future opportunities and research, including new data collection, data analytics, and application development to support and inform policymakers’ decisions are also discussed.
11:00am – 12:00pm
Chathurika Abeykoon
University of Mississippi
The Double Descent Behavior In Two Layer Neural Network For Binary Classification
Recent studies observed a surprising concept about test error called the double descent phenomenon where the increasing model complexity decreases the test error first and then the error increases and decreases again. To observe this, we worked on a two-layer neural network model with a ReLU activation function designed for binary classification under supervised learning. Our aim was to observe and find the mathematical concept behind the double descent behavior of the test error in the model for varying over-parameterization and under-parameterization ratios. We have been able to derive a closed-form solution for the test error of the model and a theorem to find the parameters with optimal empirical loss when model complexity increases. We proved the existence of the double descent phenomenon in our model for square loss function using the theorems derived.
11:00am – 12:00pm
Dr. Jeremy Clark
University of Mississippi
On two-dimensional Brownian motion singularly tilted through a point potential
A well-known but interesting characteristic of two-dimensional Brownian motion is that it will (almost surely) never return exactly to the origin even though it will reenter any given small neighborhood of the origin infinitely many times. I will discuss a two-dimensional diffusion process closely connected to Brownian motion
that has just enough drift towards the origin to enable it to return there. This opens up the possibility of formulating a theory of its local time, a characterization of the time spent in the vicinity of the origin. The transition probabilities for this diffusion process are defined through an integration kernel that has arisen in recent articles on the two-dimensional stochastic heat equation. The work that I will present is in collaboration with Barkat Mian.
11:00am – 12:00pm
Dr. Xin Dang
University of Mississippi
Feature screening for ultrahigh-dimensional classification via Gini distance correlation
Gini distance correlation (GDC) was recently proposed to measure dependence between a categorical variable and numerical random vector. In this talk, we utilize the GDC to establish a feature screening for ultrahigh-dimensional classification where the response variable is categorical. It can be used for screening individual features as well as grouped features. The proposed procedure possesses several appealing properties. It is model-free. No model specification is needed. It holds the sure independence screening property and the ranking consistency property. The proposed screening method can deal with the case that the response has divergent number of categories. Simulation and real data applications are presented to compare performance of the proposed screening procedure.
11:00am – 12:00pm
Mathias Muia Nthiani & Mous-Abou
University of Mississippi
A point on discrete vs continuous state-space markov chains/A comparison of estimation techniques for copula-based Markov chains
In this talk a Bernoulli Markov chain based on the Mardia copula family is considered. We obtain estimators for the parameters in the structure of the Markov chain and provide their confidence intervals. Moreover, for Markov chains generated by symmetric copulas with uniform marginals we provide new estimators and confidence intervals for copula parameters by considering several families of copulas introduced in Longla(2023). A simulation study is provided with a comparison to other known estimators such as the MLE and that of Longla and Peligrad (2021). We then make a comparison of discrete versus continuous state-space Markov chains.
11:00am – 12:00pm
Dr. Olivier Menoukeu Pamen
University of Liverpool, UK
A uniqueness and smoothness result for multidimensional SDE’s on the plane with nondecreasing coefficient
In this talk, we discuss the path by path uniqueness for multidimensional stochastic differentialequations driven by the Brownian sheet. We assume that the drift coefficient is unbounded, ver-ifies a spacial linear growth condition and is componentwise nondeacreasing. We first show theresult for bounded and measurable drift. Our proofs rely on a local time-space representation ofBrownian sheet and a type of law of the iterated logarithm for the Brownian sheet. The result inthe unbounded case then follows by using the Gronwall’s lemma on the plane. Under boundednessof the solution, we also prove that the obtained solution is Malliavin smooth.This talk is based on a joint work with A. M. Bogso and M. Dieye.
11:00am – 12:00pm
Dr. Martial Longla
University of Mississippi
Exchangeable copulas and m-dependent copulas
I will talk about a new set of copulas that I have been dealing with. I obtained these copulas while searching for conditions to have a Markov chain that is exchangeable. In the process, I was dragged into m-dependent Markov chains, and ended up providing a characterization of some families of copulas that I call m-dependent copulas, idempotent copulas and exchangeable copulas. Exchangeable copulas remind me of De Finetti’s theorem. The large sample theory of parameter estimators has been done for these families under The assumption that the Data has uniform marginal distribution.
Louis Aimé FONO
Research Group in Applied Mathematics for Social Science
University of Douala-Cameroon
On Some Probability Distributions of Customer Sensitivity for Premium Renewal in Non-life Insurance
Every year, non- life insurers face the recursing problem of adjusting premium. This problem comes from the trade-off between the need of increasing the global revenue of the company and the need of retention of the existing customers of the portfolio. Traditional pricing methods (General Linear Model or Credibility Theory) solve this problem by a static approach and they do not take into account the customer sensitivity and/or the prices offered by competing companies. Elena et al. [1] formalized and solved the pricing renewal problem of a non-life insurance company by using a dynamic approach based on reinforcement learning (Markov Decision Problem). The insurer has a portfolio of costumers and therefore a total turnover (initial state). At the time of contract renewal, the insurer (agent) offers a renewal premium to the first insured (we say that the agent takes action). Whether or not the insured accepts the renewal premium, his decision leads the company to a new state (new income and new retention). Then, taking into account the new situation of the company, the insurer repeats sequentially the same action to all the others insureds in the portfolio.
This paper extends and improves the model of Elena et al. in various circumstances. More precisely, we propose some families of probability distributions that take into consideration sensitivity of insurers to the new premiums. We rewrite the Elena et al.’s model by replacing regression probability by the obtained probability distributions and we obtain our new pricing models. We find the best strategy for insurer to set renewal price through reinforcement learning algorithms. The implementation of the newly obtained reinforcement models on a portfolio of contracts by using backward SARSA( ) learning agent yields better results than those obtained by Elena and al. [1]. Keywords: Pricing renewal in Non-life insurance; Reinforcement learning; Customer sensitivity; Customer renewal probabilities.
References
[1] Elena K. and Garcia J., Maestre R. and Fernandez F. (2019) Reinforcement learning for pricing strategy optimization in the insurance industry, Engineering Applications of Artificial Intelligence, 80 (C) 8-19. https://doi.org/10.1016/j.engappai.2019.01.010
[2] Ngnié F.C. Mbama E.B., Fotso S. and Fono L.A. (2021) On the study of premium renewal problem in non-life insurance based on two families of customer renewal probability through reinforcement learning. Online Astin Colloquia.
Xinyuan Chen
Assistant Professor of Statistics
Mississippi State University
A Bayesian Machine Learning Approach for Estimating Heterogeneous Survivor Causal Effects: Applications to a Critical Care Trial
Assessing heterogeneity in the effects of treatments has become increasingly popular in the field of causal inference and carries important implications for clinical decision-making. While extensive literature exists for studying treatment effect heterogeneity when outcomes are fully observed, there has been limited development of tools for estimating heterogeneous causal effects when patient-centered outcomes are truncated by a terminal event, such as death. Due to mortality occurring during study follow-up, the outcomes of interest are unobservable, undefined, or not fully observed for specific subgroups of participants, therefore requiring the principal stratification framework to draw valid causal conclusions. Motivated by the Acute Respiratory Distress Syndrome Network (ARDSNetwork) ARDS respiratory management (ARMA) trial, we developed a flexible Bayesian machine learning approach to estimate the average causal effect and heterogeneous causal effects among the always-survivors stratum when clinical outcomes are subject to truncation. We adopted Bayesian additive regression trees (BART) to flexibly specify separate models for the potential outcomes and latent strata membership. In the analysis of the ARMA trial, we found that the low tidal volume treatment had an overall benefit for participants sustaining acute lung injuries on the outcome of time to returning home, but substantial heterogeneity in treatment effects among the always-survivors, driven most strongly by sex and the alveolar-arterial oxygen gradient at baseline (a physiologic measure of lung function and source of hypoxemia). These findings illustrate how the proposed methodology could guide the prognostic enrichment of future trials in the field. We also demonstrated through a simulation study that our proposed Bayesian machine learning approach outperforms other parametric methods in reducing the estimation bias in both the average causal effect and heterogeneous causal effects for always-survivors.
Ngongo Isidore Seraphin
ENS, Universite de Yaounde 1, Cameroun.
Inference for nonstationary time series of counts with application to change-point problems
We consider an integer-valued time series Y = (Yt)t∈Z where the model after a time k∗ is Poisson
autoregressive with the conditional mean that depends on a parameter θ∗ ∈ Θ ⊂ Rd. The structure of the
process before k∗ is unknown; it could be any other integer-valued time series, that is, the process Y could
be nonstationary. It is established that the maximum likelihood estimator of θ∗ computed on the nonstationary
observations is consistent and asymptotically normal. Subsequently, we carry out the sequential
change-point detection in a large class of Poisson autoregressive models. We propose a monitoring scheme
for detecting change in the model. The procedure is based on an updated estimator, which is computed
without the historical observations. The asymptotic behavior of the detector is studied, in particular, the
above results of inference in a nonstationary setting are applied to prove the consistency of the proposed
procedure. A simulation study as well as a real data application are provided.
Keywords: Time series of counts, Poisson autoregression, likelihood estimation, change-point, sequential
detection, weak convergence.
Huybrechts Bindele
University of South Alabama
Robust estimation and selection for single-index regression model
In this talk, we will consider a single-index regression model, from which we will discuss a robust estimation procedure for the model parameters and an efficient variable selection of relevant predictors. The proposed approach known as the penalized generalized signed-rank procedure will be introduced. Asymptotic properties of the resulting estimators will be discussed under mild regularity conditions. Extensive Monte Carlo simulation experiments will be carried out to study the finite sample performance of the proposed approach. The simulation results will demonstrate that the proposed approach dominates many of the existing ones in terms of robustness in estimation and efficiency of variable selection. Finally, a real data example will be discussed to illustrate the method.
Magda Peligrad
University of Cincinnati
The CLT for stationary Markov chains with trivial tail sigma field
In this talk we consider stationary Markov chains with trivial two-sided tail sigma field and present the tools leading to the following result: Any additive functional of such a Markov chain satisfies the central limit theorem provided the variance of partial sums divided by n is bounded.
The method is based on martingale decomposition using a new idea involving conditioning with respect to both the past and the future of the chain. No assumption of irreducibility or aperiodicity is needed.
Hume 321 at 4:30 PM
Jialin Zhang
Mississippi State University
Unfolding Entropic Statistics
This talk is organized into three parts.
1) Entropy estimation in Turing’s perspective is described. Given an iid sample from a countable alphabet under a probability distribution, Turing’s formula (introduced by Good (1953), hence also known as the Good-Turing formula) is a mind-bending non-parametric estimator of total probability associated with letters of the alphabet that are NOT represented in the sample. Some interesting facts and thoughts about entropy estimators are introduced.
2) Turing’s formula brought about a new characterization of probability distributions on general countable alphabets that provides a new way to do statistics on alphabets, where the usual statistical concepts associated with random variables (on the real line) no longer exist. The new perspective, in turn, inspires some thoughts on the characterization of probability distribution when the underlying sample space is unclear. An application example of authorship attribution is provided at the end.
3) Shannon’s entropy is only finitely defined for distributions with fast decaying tails on a countable alphabet. The unboundedness of Shannon’s entropy over thick-tailed distributions on an alphabet prevents its potential utility from being fully realized. Zhang (2020) proposed generalized Shannon’s entropy (GSE), which is finitely defined everywhere. Some interesting results about GSE and a new test of independence inspired by GSE are introduced. The new test does not require the knowledge of cardinality, and it is consistent and would detect any form of dependence structure in the general alternative space given a sufficiently large sample.
Martial Longla
University of Mississippi
Sometimes, Disorder Helps (pdf)
Timothy Fortune
University of Mississippi
Local Limit Theorem for Linear Random Fields (pdf)
Dongsheng Wu
University of Alabama-Huntsville
Weak Convergence of Martingales and its Application to Nonlinear Cointegrating Model (pdf)
Xin Dang
University of Mississippi
Gini Distance Correlation and Feature Selection (pdf)
Qian Zhou
Mississippi State University
Model Misspecification in Statistical Analysis (pdf)
Tung-Lung Wu
Mississippi State University
Tests for High-Dimensional Covariance Matrices Using Random Matrix Projection (pdf)
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)
Junying Zhang
Taiyuan University of Technology, Taiyuan, P. R. China
Marginal Empirical Likelihood Independence Screening in Sparse Ultrahigh Dimensional Additive Models (pdf)
Yimin Xiao
Michigan State University
On the Excursion Probabilities of Gaussian Random Fields (pdf)
Charles Katholi
University of Alabama at Birmingham
Estimating Proportions by Group Testing: A Frequentist Approach (pdf)
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)