[gov3009-l] 9/20 Applied Stats: Mohammad Jalali

Hi everyone! This week at the Applied Statistics Workshop we will be welcoming *Mohammad Jalali*, research faculty at MIT Sloan School of Management. He will be presenting work entitled *A Flexible Method for Aggregation of Prior Statistical Findings*. Please find the abstract below and the full text here <https://urldefense.proofpoint.com/v2/url?u=http-3A__journals.plos.org_plosone_article-3Fid-3D10.1371_journal.pone.0175111&d=DwMFaQ&c=WO-RGvefibhHBZq3fL85hQ&r=7GHlh3PoEL0ovJoykMGjMxtroiHX2uem_TzqWQ3PJXo&m=HBvHf4Ex_XuwbE1xR5fMA0w3X4BWfAeB-P6ZQ5c2RHA&s=VDt2XvEJTmQxpgw1hpYT8drz_CQlk5K67nTU40HtQuo&e=> . As usual, we will meet at noon in CGIS Knafel Room 354 and lunch will be provided. See you all there! -- Dana Higgins *Title:* *A Flexible Method for Aggregation of Prior Statistical Findings* *Abstract:* Rapid growth in scientific output requires methods for quantitative synthesis of prior research, yet current meta-analysis methods limit aggregation to studies with similar designs. Here we describe and validate Generalized Model Aggregation (GMA), which allows researchers to combine prior estimated models of a phenomenon into a quantitative meta-model, while imposing few restrictions on the structure of prior models or on the meta-model. In an empirical validation, building on 27 published equations from 16 studies, GMA provides a predictive equation for Basal Metabolic Rate that outperforms existing models, identifies novel nonlinearities, and estimates biases in various measurement methods. Additional numerical examples demonstrate the ability of GMA to obtain unbiased estimates from potentially mis-specified prior studies. Thus, in various domains, GMA can leverage previous findings to compare alternative theories, advance new models, and assess the reliability of prior studies, extending meta-analysis toolbox to many new problems.