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.
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