Next Meetup

🏁 Annual Kick-Off 🗓️
NO COST & PARKING FREE PLUS Lunch will be provided with a vegetarian option Stick around for the business meeting to learn about next year's initiatives! Please share this event with a friend: meetup.com/sc_asa Keynote by Dr. Shujie Ma, Department of Statistics, UCR: "A Robust and Efficient Approach to Causal Inference Based on Sparse Sufficient Dimension Reduction" Students Presenters: Ms. Mingming Liu, graduate student, UCR (Title: Subgroup analysis in longitudinal data) Mr. Gordon David Mosher, undergraduate student, UCR (Title: Prior Weights for Iterative Hard Thresholding: Using Julia to analyze the human genome) Schedule: 9am-10:15am : Keynote Presentation (Dr. Shujie Ma) 10:15am-10:30am : Coffee Break 10:30am-11am : Presentation by Mingming Liu 11am-11:30am : Presentation by Gordon David Mosher 11:30am-12:30pm : Lunch 12:30pm-2pm : SCASA Business meeting About: KEYNOTE: A Robust and Efficient Approach to Causal Inference Based on Sparse Sufficient Dimension Reduction (by Dr. Shujie Ma) A fundamental assumption used in causal inference with observational data is that treatment assignment is ignorable given measured confounding variables. This assumption of no missing onfounders is plausi-ble if a large number of baseline covariates are included in the analysis, as we often have no prior knowledge of which variables can be important confounders. Thus, estimation of treatment effects with a large number of covariates has received considerable attention in recent years. Most of the existing meth-ods require specifying certain parametric models involving the outcome, treatment and confounding vari-ables, and employ a variable selection procedure to identify confounders. However, selection of the right set of confounders depends on correct specification of the working models. The bias due to model mis-specification and incorrect selection of confounders can yield misleading results. Weproposes a new ro-bust and efficient approach for inference about the average treatment effect via a flexible modeling strate-gy incorporating penalized variable selection. Specifically, weconsider an estimator constructed based on an efficient influence function which involves a propensity score function and an outcome regression func-tion. We then propose a new sparse sufficient dimension reduction approach to estimating these two functions, without making restrictive parametric modeling assumptions. We show that the proposed esti-mator of the average treatment effect is asymptotically normal and semiparametric efficient. STUDENT TOPIC: Subgroup analysis in longitudinal data (by Ms. Mingming Liu) Understanding treatment heterogeneity in longitudinal studies is very important to the development of precision medicine, which seeks to tailor medical treatments to subgroups of patients with similar charac-teristics. One of the challenges is that we usually do not have a priori knowledge of the grouping infor-mation of patients with respect to treatment effect. To solve this problem, we propose a penalized ap-proach for subgroup analysis based on either a GEE model or a nonparametric mixed-effects model. STUDENT TOPIC: Prior Weights for Iterative Hard Thresholding: Using Julia to analyze the human genome (by Mr. Gordon David Mosher) This summer I was privileged to participate the “Bruins-In-Genomics” Summer Undergraduate Re-search Program at UCLA where I was selected to work in the Human Genetics Department in the Geffen School of Medicine. I learned to program in Julia, which is designed to be extremely fast and to handle the large data sets common in modern genomics analyses. I used my new-found Julia skills on a project in support of the multivariate genome-wide association studies component in OpenMendel, an open source statistical ge-netics package for analysis of qualitative and quantitative traits. Read more @

Azusa Pacific University

701 East Foothill Avenue · Azusa, Ca

    Past Meetups (4)

    What we're about

    The Southern California Chapter of the American Statistical Association (SCASA, affectionately pronounced skah- zah) is a non-profit organization that exists for the purpose of promoting unity within the community of statisticians around Southern California area, and of contributing to statistical education within the professional community and the general public. By its efforts, the Chapter hopes to increase the contribution of Statistics to human welfare everywhere.

    SCASA organizes four standing events per year:
    Kick-off in October-November,
    Career Day in February-March,
    Applied Statistics Workshop in March-April,
    and AP Stat Poster Competition in May-June.

    The membership in SCASA is open to all individuals in all fields related to Statistics. The SCASA members have the benefit of being actively involved in organizing local events and activities, and, if also members of the national American Statistical Association, the SCASA members will have the right to vote and hold a SCASA office. Regular membership dues are $12 per year, and $6 for full-time students and retirees. More details on the SCASA membership benefits can be found in Article III of the SCASA Constitution.

    The SCASA members are eternally grateful to organizations that sponsor some of the events or contribute books and goodies to give out as door prizes. Some of the main and most faithful supporters in the previous years include Amgen, SAS, JMP, Salford Systems, and CRC Press.

    Members (239)

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