Fall 2021 Louisiana ASA Chapter Meeting

Fall 2021 Chapter Meeting

Friday, 22 October 2021 (On Zoom)

Please contact Calvin Berry with any questions or to obtain the zoom meeting information.

Schedule

Titles and Abstracts

A mixture binary randomized response technique model with a unified measure of privacy and efficiency
Maxwell Lovig
University of Louisiana at Lafayette
Lafayette, Louisiana
Abstract: In this talk, I will introduce a mixture binary Randomized Response Technique (RRT) model by combining the elements of the Greenberg Unrelated Question model and the Warner Indirect Question model. This model will also account for untruthful responses. A unified measure of model efficiency and respondent privacy will be discussed. I will also provide the results of a simulation study to validate the theoretical findings.
This talk is based on joint work with Sadia Khalil, Sumaita Rahman, Pujita Sapra, and Sat Gupta.

Investigation of adolescent antisocial behaviors in the U.S. under item response theory models with Add Health data
Jiayun Ling
Tulane University
New Orleans, Louisiana
Abstract: The item response theory (IRT) refers to a group of statistical models aiming to explore the relationship between latent traits and their manifestations. IRT has become a popular methodological framework for modeling response data from assessments in many areas such as cognitive abilities, personality traits, and patient satisfaction. Yet, the application of IRT in the study of children's abnormal behaviors is rare. The Add Health data are collected from a longitudinal study of nationally representative samples of over 20,000 adolescents who were in grades 7-12 during the 1994-95 school year in the United States. We apply IRT models on the first 2 waves of Add Health data to study abnormal behaviors among adolescents. We aim to find and test important factors associated with abnormal behaviors such as violence. We will discuss our findings and future directions.

Impacts of COVID-19 local spread and Google search trend on the US stock market
Kumer Das
University of Louisiana at Lafayette
Lafayette, Louisiana
Abstract: We develop a novel temporal complex network approach to quantify the US county level spread dynamics of COVID-19. We use both conventional econometric and Machine Learning (ML) models that incorporate the local spread dynamics, COVID-19 cases and death, and Google search activities to assess if incorporating information about local spreads improves the predictive accuracy of models for the US stock market. The results suggest that COVID-19 cases and deaths, its local spread, and Google searches have impacts on abnormal stock prices between January 2020 to May 2020. Furthermore, incorporating information about local spread significantly improves the performance of forecasting models of the abnormal stock prices at longer forecasting horizons

Depth-based weighted jackknife empirical likelihood for non-smooth U-structure equations
Yongli Sang
University of Louisiana at Lafayette
Lafayette, Louisiana
Abstract: In many applications, parameters of interest are estimated by solving some non-smooth estimating equations with U-statistic structure. Jackknife empirical likelihood (JEL) approach can solve this problem efficiently by reducing the computation complexity of the empirical likelihood (EL) method. However, as EL, JEL suffers the sensitivity problem to outliers. In this paper, we propose a weighted jackknife empirical likelihood (WJEL) to tackle the above limitation of JEL. The proposed WJEL tilts the JEL function by assigning smaller weights to outliers. The asymptotic of the WJEL ratio statistic is derived. It converges in distribution to a multiple of a chi-square random variable. The multiplying constant depends on the weighting scheme. The self-normalized version of WJEL ratio does not require to know the constant and hence yields the standard chi-square distribution in the limit. Robustness of the proposed method is illustrated by simulation studies and one real data application.

Bayesian mediation analysis with survival outcomes: With an application to explore racial disparity in breast cancer survival
Wentao Cao
LSU Health Science Center, New Orleans
New Orleans, Louisiana
Abstract: Mediation analysis is widely used to identify significant mediators and estimate the mediation effects in causal pathways between an exposure variable and a response variable. In mediation analysis, the mediation effect refers to the effect transmitted by mediator intervening the relationship between an exposure variable and a response variable. Bayesian method is a statistical method that allows researchers to incorporate prior information from previous knowledge into the analysis and estimate the quantities of interest from the posterior distributions. In this research, we apply the Bayesian method to the mediation analysis to make inferences on the mediation effects. We propose three Bayesian mediation methods to estimate the mediation effects and show how to use software, such as JAGS and Stan, for Bayesian mediation analysis. Through a series of simulations, we compare the accuracy and efficiency of the estimates of mediation effects using three Bayesian mediation methods. We extend three Bayesian mediation methods for time-to-event outcomes and apply these methods to explore the racial disparity in breast cancer survivals and age of patient diagnosed with breast cancer in Louisiana.
This talk is based on joint work with Qingzhao Yu.

Digital Ag, applications of data analysis in Agriculture
Thanos Gentimis
Louisiana State University
Baton Rouge, Louisiana
Abstract: In this talk we will give a brief overview of the developing field of Digital Agriculture, and the role of modern data science in decision making for anything Ag related. We will discuss the new multistate project and conference and the opportunities for Statisticians and Data analysts in general. We will attempt to give an overview of the most interesting problems/topics and describe the role our future generation of statisticians can play in it.