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The International Indian Statistical Association (IISA, https://www.intindstat.org/) 2022 Conference (in-person) will be held at the Indian Institute of Science, Bangalore between December 26-30, 2022 (https://www.intindstat.org/conference2022/index). The KISS has 1 or 2 allotted invited sessions. Each invited session will be 80 minutes long and will have 3 speakers. Each speaker will have 25 minutes for their presentation, with 5 minutes dedicated to Q&A. As an organizer you can be a participant in the session you are organizing, and/or you may want to invite your Indian friends to your session since it will be in India. The detailed information regarding invited session submissions can be found at https://www.intindstat.org/conference2022/invitedSessionProposal. If you are interested in submitting an invited session proposal, or giving a talk in the KISS invited session, please let me know by June 10th, Friday (MinJae.Lee@UTSouthwestern.edu).
Date: 1-2pm (ET) on May 10 (Tue)
Zoom link: https://psu.zoom.us/j/94826638241
Speaker: Dr. Kyu Ha Lee (Harvard T.H. Chan School of Public Health)
Title: Outcome-Specific Variable Selection for High-Dimensional Multivariate Zero-Inflated Count Data
Abstract: With recent advances in next generation sequencing technologies, the Human Microbiome Project has provided outstanding documentation of the microorganisms present in both health and disease states. Moving beyond such a catalogue will require similar advances in biostatistical methods development to meet challenges arising from inherent features of these data, such as zero-inflated sequence counts and the complex interdependencies among organisms. In this project, we compare the oral microbiomes in HIV-infected children and HIV-uninfected children in a cohort who were all exposed to HIV perinatally (the ongoing Pediatric HIV/AIDS Cohort Study). Here, we are interested in bacterial counts as an outcome of interest. First, most investigators adopt a univariate approach, analyzing one taxon at a time. However, application of separate univariate analyses for each taxon ignores the dependent relationships among organisms. Even with Bonferroni corrections, univariate approaches generally inflate type I error. We address this challenge by developing multivariate zero-inflated models. The proposed multivariate variable selection procedure can select, simultaneously, a subset of covariates for each of multiple taxa. Through comprehensive numerical studies, we demonstrate that the proposed multivariate methods improve upon univariate approaches.
We are pleased to announce the 2022 The Caucus for Women in Statistics (CWS) Travel Awards. This year, we have three individual travel awards.
Since there still remains uncertainty as to the mode of JSM this year, we will either reimburse travel expenses or registration for the conference if travel is not an option.
The deadline for applications is April 30, 2022.
The detailed information is available below or at https://cwstat.org/2022-cws-annual-travel-award/
Looking forward to your participations!
Dong-Yun Kim, PhD
CWS Travel Award Committee Chair, 2022
Date: 5-6pm (ET) on April 29th (Friday)
Zoom link: https://psu.zoom.us/j/94826638241
Speaker: Dr. Hang Joon Kim (University of Cincinnati)
Title: Bayesian model calibration and sensitivity analysis with ordinary differential equation modeling
Abstract: Ordinary differential equations (ODE) are often used by biomathematicians to model the periodic behavior of organisms, such as circadian rhythm. Parameter estimation and sensitivity analysis with the ODE model often pose a significant statistical challenge due to its ill behavior in high dimension, identifiability, and numerical instability. This talk introduces a new Bayesian calibration strategy for oscillating biochemical modeling. The proposed methodology utilizes harmonic basis representation for the likelihood function description, and the posterior inference is made with an advanced Markov chain Monte Carlo called the generalized multiset sampler. The proposed framework is illustrated with circadian oscillations observed in a model filamentous fungus, Neurospora crassa.
The seminar video was recorded and it is now publicly available in the following link:
Date: 3-4pm (ET) on March 24th (Thursday)
Zoom link: https://psu.zoom.us/j/94826638241
Speaker: Dr. Won Chang (University of Cincinnati)
Title: Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression
Abstract: Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiments is input parameter uncertainty. Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. The existing standard calibration framework suffers from inferential issues when the model output and observational data are high-dimensional dependent data, such as large time series, due to the difficulty in building an emulator and the nonidentifiability between effects from input parameters and data-model discrepancy. To overcome these challenges, we propose a new calibration framework based on a deep neural network (DNN) with long short-term memory layers that directly emulates the inverse relationship between the model output and input parameters. Adopting the “learning with noise” idea, we train our DNN model to filter out the effects from data-model discrepancy on input parameter inference. We also formulate a new way to construct interval predictions for DNN using quantile regression to quantify the uncertainty in input parameter estimates. Through a simulation study and real data application with the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro), we show our approach can yield accurate point estimates and well-calibrated interval estimates for input parameters.
The seminar video was recorded and it is now publicly available in the following link:
Date: 3-4pm (ET) on February 18th, 2022 (Fri)
Zoom link: https://psu.zoom.us/j/94826638241
Speaker: Dr. Yeonhee Park (University of Wisconsin-Madison)
Title: Envelope-Based Application To Real Data
Abstract: The envelope model is first introduced by Cook et al. (2010) as an efficient method to estimate the regression coefficients under the context of multivariate linear regression. It uses sufficient dimension reduction techniques to identify the part of the data that is immaterial to the estimation goal. The subsequent estimation is only based on the material part and is thus more efficient. After that, the envelope model has been adapted to many areas. Among the envelope models, the groupwise envelope model (Park et al. 2017) and partial predictor envelope model (Park et al. 2022+) are discussed in this talk with the application to Imaging Genetic Analysis for Alzheimer's Disease Neuroimaging Initiative (ADNI) study and Cytokine-based Biomarker Analysis for COVID-19. Motivated to search for how the associations between genetic variants and brain imaging phenotypes differ across male and female groups, a groupwise envelope model is developed for multivariate linear regression to establish the association between both multivariate responses and covariates. The groupwise envelope model allows for both distinct regression coefficients and distinct error structures for different groups. For the identification of cytokine-based biomarkers for COVID-19 patients, which reveals the association between the cytokine-based biomarkers and patients' clinical information including disease status at admission and demographical characteristics, a partial predictor envelope model is developed to achieve estimation efficiency when both continuous and categorical predictors are present. Using a connection between the envelope model and partial least squares (PLS), the partial predictor envelope model considers the PLS regression on the conditional distributions of the response(s) and continuous predictors on the categorical predictors. The envelope-based application shows the effectiveness of the models in estimation, which leads to a clear scientific interpretation of the results.
The seminar video was recorded and it is now publicly available in the following link:
Congratulations, Professor Jong-Min Kim, on his new appointment as Journal of Applied Statistics (JAS) Book Review Editor. Professor Kim teaches at the University of Minnesota-Morris. He served as the first treasurer of the KISS, and gave a presentation, Copula Directional Dependence and Its Applications, in October at the KISS Monthly Webinar Series. Congratulations and best wishes for your next adventure, Jong-Min!
Wednesday, December 8, 2021
8:00 – 9:00 pm (ET)
5:00 – 6:00 pm (PT)
Via ZOOM
Please R.S.V.P. by December 1st to get Zoom link.
Click here to register.
We encourage you to participate in a fun activity during our party by sending a picture from your vacation (last summer or past vacations) showing what you have been up to or where you have been to (Where was I?): jungwha-lee@northwestern.edu
Attachment: 2021 KISS Virtual Holiday Party Flyer
Date: 1-2pm EDT on November 23rd (Tue), 2021
Zoom link: https://psu.zoom.us/j/94826638241
Speaker: Dr. Jaeil Ahn (Georgetown University)
Title: Bayesian analysis of longitudinal dyadic/multiple outcome data with informative missing data
Abstract: Analysis of longitudinal dyadic/multiple outcomes with missing data is challenging due to the complicated correlations within and between dyads/multiple outcomes, as well as non-ignorable missing data. In the first part of the talk, I will introduce a Bayesian mixed effects hybrid model to analyze longitudinal dyadic data with non-ignorable dropouts/intermittent missingness. To address, I factorize the joint distribution of the measurement, random effects, and dropout processes into three components. The proposed model accounts for the dyadic interplay using the concept of actor and partner effects as well as dyad-specific random effects. I evaluate the performance of the proposed methods using a simulation study, and apply our method to longitudinal dyadic datasets that arose from a prostate cancer trial. In the second part of the talk, I will introduce a Bayesian mixed effects selection model to analyze multivariate quality of life data with non-ignorable missing data. Compared to the first model, I first describe the overall/marginal effects of predictors on outcomes and then incorporate a variable selection feature in the missing data mechanism to evaluate the impact of potentially moderate to high dimensional outcomes on missing data mechanisms. I will illustrate how the proposed model works using a longitudinal study of quality of life in gastric cancer patients who underwent distal gastrectomy.
Date: 3-4pm EST on October 20th (Wed), 2021
Zoom link: https://psu.zoom.us/j/94826638241
Speaker: Dr. Jong-Min Kim (University of Minnesota-Morris)
Title: Copula Directional Dependence and Its Applications
Abstract: By a theorem due to Sklar in 1959, a multivariate distribution can be represented in terms of its underlying margins by binding them together a copula function. Copulas are useful devices to explain the dependence structure between variables by eliminating the influence of marginals. A copula method for understanding multivariate distributions has a relatively short history in statistics literature; most of the statistical applications have arisen in the last twenty years. In this talk, copula history will be briefly introduced. The main of this talk is as follows: first, copula directional dependence capturing the direct interactions among data will be introduced. Second, for the applications of the copula direction dependence, diverse examples such as earthquakes in East Asia countries (China, Japan, Korea), China air pollution, Gene interaction, Finance will be introduced.
Date: 3-4pm EST on September 24th (Fri), 2021
Zoom link: https://psu.zoom.us/j/94826638241
Speaker: Dr. Hyungsuk Tak (Pennsylvania State University)
Title: Data Augmentation for Multimodality, Outliers, and Heteroscedasticity
Abstract: Data augmentation is a versatile strategy of augmenting the observed data by auxiliary variables (either latent or missing data) not only to improve the convergence speed of iterative algorithms such as EM algorithms and Gibbs-type samplers, but also to solve various problems in statistics. In this talk, I present three novel data augmentation schemes that are practically motivated to tackle multimodality, outliers, and heteroscedasticity in astronomical data analyses. For example, multimodality is a well known issue of an MCMC implementation in estimating the current expansion rate of the Universe, called the Hubble constant; outlying observations are common in measuring pulsar timing for detecting gravitational waves; and large-scale astronomical surveys always accompany with heteroscedastic measurement errors. The proposed data augmentation methods will be illustrated in the context of these astronomical problems.
Dr. Kim (our KISS Board Member) has been elected to the leadership position of the Caucus for Women in Statistics (CWS: https://cwstat.org/). CWS has just celebrated its 50th anniversary this year and is one of the societies which sponsor the Joint Statistical Meetings (JSM) along with the KISS. She will serve CWS as the President Elect in 2022 and as President in 2023.
Invited Session #335 (Virtual)
Thu, 8/12/2021, 10:00 AM - 11:50 AM
Title: COVID-19 Stories: Voices from the Fields
Organizer: Dong-Yun Kim, National Institutes of Health)
https://ww2.amstat.org/meetings/jsm/2021/onlineprogram/ActivityDetails.cfm?SessionID=220366
Topic-contributed session #76 (Virtual)
Mon, 8/9/2021, 10:00 AM - 11:50 AM
Title: Recent Advances in Multivariate Analysis for Modern Scientific Studies and Application (organized by Yeonhee Park, University of Wisconsin)
https://ww2.amstat.org/meetings/jsm/2021/onlineprogram/ActivityDetails.cfm?SessionID=220217
Topic-contributed session #154 (Virtual)
Tue, 8/10/2021, 10:00 AM - 11:50 AM
Title: Some New Innovations in Survey Sampling and Missing Data Problems
Organizer: Sixia Chen, University of Oklahoma Health Sciences Center
https://ww2.amstat.org/meetings/jsm/2021/onlineprogram/ActivityDetails.cfm?SessionID=220450
Topic-contributed session #309 (Virtual)
Wed, 8/11/2021, 3:30 PM - 5:20 PM
Title: Statistical Advances in Source Apportionment of Air Pollutants and Source-Specific Health Effects Evaluation
Organizer: Eun Sug Park, Texas A&M Transportation Institute
https://ww2.amstat.org/meetings/jsm/2021/onlineprogram/ActivityDetails.cfm?SessionID=220380
Congratulations, Professor Mikyoung Jun on being elected as ASA Fellow this year!
Professor Jun has been serving as a KISS Board member, and she recently presented her work at the 2021 KISS Webinar Series. The link to her homepage is: https://sites.google.com/view/mikyoung-jun/home
KISS Career Development Award recognizes statisticians in the early stages of their careers who have demonstrated outstanding productivity and the potential to make significant contributions to the field of statistics.
We are pleased to announce 2021 KISS Career Development Award winners.
The award winners will be recognized at our annual KISS meeting scheduled Monday August 9th 8PM EST. Please register (click link) if you haven't already.
Congratulations to 2021 KISS Career Development Award Recipients!
Yei Eun Shin, PhD
Winner, 2021 KISS Early Career Development Award (Prize $500)
Tenure-track Investigator
Biostatistics Branch
Division of Cancer Epidemiology & Genetics
National Cancer Institute
Junho Yang, PhD
Honorable Mention, 2021 KISS Early Career Development Award (Prize $200)
Assistant Research Fellow
Institute of Statistical Science at Academia Sinica
We would like to thank our committee members for reviewing this year's applications.
Career Development Awards Committee
Julia (Jungwha) Lee, Northwestern University Feinberg School of Medicine, Chair
Ryung Kim, Albert Einstein College of Medicine
Hee-Choon Shin, Center for Diseases and Control and Prevention
Kyunghee Song, Food and Drug Administration
Sincerely,
Korean International Statistical Society
Dear KISS members,
We are happy to hold our 2021 KISS Annual Meeting Virtually this year.
Please register below to join us on Monday August 9th, 8pm EST.
We will have several prizes for those who participate.
2021 KISS Annual (Virtual) Meeting
When: Aug 9, 2021 08:00 PM Eastern Time (US and Canada)
Register in advance for this meeting:
https://northwestern.zoom.us/meeting/register/tJwsdO6prj8vGNFCj8fwcglDA8tzvnF3Jhph
After registering, you will receive a confirmation email containing information about joining the meeting.
If you have any questions regarding the annual meeting, please email Julia Lee, 2021-22 KISS Executive Director, at jungwha-lee@northwestern.edu.
Date: 3-4pm EST on June 16th (Wed), 2021
Zoom link: https://psu.zoom.us/j/96312135796
Speaker: Dr. Mikyoung Jun (University of Houston)
Title: Multivariate spatio-temporal Hawkes process models of terrorism
Abstract: We develop a flexible bivariate spatio-temporal Hawkes process model to analyze pat-terns of terrorism. Previous applications of point process methods to political violence data have mainly utilized temporal Hawkes process models, neglecting spatial variation in these attack pat-terns. This limits what can be learned from these models, as any effective counter-terrorism strategy requires knowledge on both when and where attacks are likely to occur. Even the existing work that does exist on spatio-temporal Hawkes processes impose restrictions on the triggering function that are not well-suited for terrorism data. Therefore, we generalize the structure of the spatio-temporal triggering function considerably, allowing for nonseparability, nonstationarity, and cross-triggering(i.e., across the groups). To demonstrate the utility of our model, we analyze two samples of real-world terrorism data: Afghanistan (2002-2013) and Nigeria (2010-2017). Jointly, these two studies demonstrate that our model dramatically outperforms standard Hawkes process models, besting widely-used alternatives in overall model fit and revealing spatio-temporal patterns that are, by construction, masked in these models (e.g., increasing dispersion in cross-triggering over time). This is a joint work with Scott Cook.
Date: 4-5pm EST on May 14th, 2021
Zoom link: https://psu.zoom.us/j/96312135796
Speaker: Dr. Hyebin Song (Pennsylvania State University)
Title: Statistical inference for high-dimensional and large-scale data with noisy labels
Abstract: In many classification applications, we are presented with data with partially observed or contaminated labels. One example of such an application is in the analysis of datasets from deep mutational scanning (DMS) experiments in proteomics, which typically do not contain non-functional sequences. In many of these settings, the problem of interest is high-dimensional where the number of features is substantially larger than the sample size. Moreover, the rate of contamination is often unavailable depending on the experimental protocols, which further complicates the downstream analysis. In this talk, I will present both parametric and semi-parametric approaches for analyzing noisy, high-dimensional binary data. I will first demonstrate that when the rate of contamination is available, the noisy label model belongs to a generalized linear model with a non-canonical link, and optimal inference is possible despite the non-convex objective. I will then present a new semi-parametric approach based on hard-thresholding for the analysis of high-dimensional noisy labels data when prior knowledge of the contamination rate is unavailable. Finally, I will present an application of our methodology to inferring sequence-function relationships and designing highly stabilized enzymes based on large-scale DMS data.
Date: 4-5pm EST on April 16th, 2021 (Fri)
Zoom link: https://psu.zoom.us/j/96312135796
Speaker: Dr. Kiseop Lee (Purdue University)
Title: Data Science and Modern Financial Markets
Abstract: Since the celebrated Black-Scholes model emerged in 1973, quantitative approaches in finance have been state-of-the-art tools in financial markets. Recently, development of high frequency markets based on automated and algorithmic trading together with the boom of data science in other areas also led financial industries to adapt to this new trend. In this talk, we discuss briefly the history of quantitative finance, various machine learning tools currently used in financial markets, challenges and promises.
It is disheartening to see the rise of xenophobia and racial intolerance in America in recent years. The difficulties from the pandemic and, more notably, the misinformation and false narratives spread about the COVID-19 virus certainly played a role in exacerbating anti-Asian sentiment and violence in the States. However, we recognize that racism has always been deeply rooted within the US society. The recent tragedy in Atlanta finally brought it to the attention of mainstream media; it is a tragedy and a disgrace to see such hatred on display. Our heartfelt condolences go out to victims' families, relatives and friends as they grieve the loss of their loved ones.
The Korean International Statistical Society (KISS) unequivocally condemns all the violence and discrimination perpetrated against the Asian Americans and Pacific Islanders (AAPI) communities. We remain committed to supporting the AAPI communities, and to working with other professional and scientific societies to cultivate a diverse, inclusive, equitable, and productive environment. We will continue to support our KISS members, our AAPI colleagues, and AAPI students to meet the challenges facing the AAPI communities and our nation.
MoonJung Cho, KISS President
Jae-Kwang Kim, KISS President Elect
Don Jang, KISS Past President
On behalf of KISS Officers
Our sister societies have also made statements on anti-Asian racism, which can be accessed from the following link:
International Chinese Statistical Association (ICSA) Statement on Anti-Asian Racism
International Indian Statistical Association (IISA) Statement on Anti-Asian Racism
The first KISS webinar was held with great success. Thank you very much again for your participation! The seminar video was recorded and it is now publicly available in the following link:
Speaker: Dr. Jae-Kwang Kim (Iowa State University)
Title: Statistical Inference after Kernel Ridge Regression Imputation under Item Nonresponse
Abstract: Kernel Ridge Regression (KRR) is a modern regression technique based on the theory of Reproducing Kernel Hilbert Space. We use KRR to develop imputation for handling item nonresponse. While the KRR is potentially promising for imputation, its statistical properties are not fully investigated in the literature. We first establish the root-n consistency of the KRR imputation estimators and show that it is optimal in the sense that it achieves the lower bound of the semiparametric asymptotic variance. A consistent variance estimator is also proposed by a novel application of the KRR estimator of the density ratio function. Results from a limited simulation study are also presented to confirm our theory.
