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   Montiel Olea, José Luis; Ortoleva, Pietro; Pai, Mallesh M; Prat, Andrea (April 2022, The Quarterly Journal of Economics)

   Abstract Different agents need to make a prediction. They observe identical data, but have different models: they predict using different explanatory variables. We study which agent believes they have the best predictive ability—as measured by the smallest subjective posterior mean squared prediction error—and show how it depends on the sample size. With small samples, we present results suggesting it is an agent using a low-dimensional model. With large samples, it is generally an agent with a high-dimensional model, possibly including irrelevant variables, but never excluding relevant ones. We apply our results to characterize the winning model in an auction of productive assets, to argue that entrepreneurs and investors with simple models will be overrepresented in new sectors, and to understand the proliferation of “factors” that explain the cross-sectional variation of expected stock returns in the asset-pricing literature. 

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2. Data Models

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   Pilgrim, Robert (May 2024, University of Kentucky Libraries)

   This talk addresses the essential role of data models in analytics, especially for lean teams. It caters to a broad audience, from beginners creating reports to those integrating diverse datasets for advanced analytics and KPI development. A robust data model is crucial for rapidly scaling analytics efforts, allowing for the inclusion of varied data sources such as research awards and proposals, HR data (Gender, ranks, titles, ethnicity etc.), teaching loads, and external datasets like the HERD Survey. We will cover data modeling basics, then explore advanced analytics with the Microsoft Analytics Stack, focusing on Power BI Desktop, emphasizing its accessibility and capability for comprehensive insights. Including an introduction to Microsoft Data Analysis Expressions (DAX) and Time Intelligence Functions. Discover how effective data models enhance analytics capabilities, enabling teams to achieve significant research outcomes. 

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3. Linear Models are Most Favorable among Generalized Linear Models

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4. Authorless Topic Models: Biasing Models Away from Known Structure

   Thompson, Laure; Mimno, David (January 2018, COLING)

   Most previous work in unsupervised semantic modeling in the presence of metadata has assumed that our goal is to make latent dimensions more correlated with metadata, but in practice the exact opposite is often true. Some users want topic models that highlight differences between, for example, authors, but others seek more subtle connections across authors. We introduce three metrics for identifying topics that are highly correlated with metadata, and demonstrate that this problem affects between 30 and 50% of the topics in models trained on two real-world collections, regardless of the size of the model. We find that we can predict which words cause this phenomenon and that by selectively subsampling these words we dramatically reduce topic-metadata correlation, improve topic stability, and maintain or even improve model quality 

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5. Better null models for assessing predictive accuracy of disease models

   https://doi.org/10.1371/journal.pone.0285215  

   Keyel, Alexander C.; Kilpatrick, A. Marm (May 2023, PLOS ONE)

   Ansari, Ali R. (Ed.)

   Null models provide a critical baseline for the evaluation of predictive disease models. Many studies consider only the grand mean null model (i.e. R 2 ) when evaluating the predictive ability of a model, which is insufficient to convey the predictive power of a model. We evaluated ten null models for human cases of West Nile virus (WNV), a zoonotic mosquito-borne disease introduced to the United States in 1999. The Negative Binomial, Historical (i.e. using previous cases to predict future cases) and Always Absent null models were the strongest overall, and the majority of null models significantly outperformed the grand mean. The length of the training timeseries increased the performance of most null models in US counties where WNV cases were frequent, but improvements were similar for most null models, so relative scores remained unchanged. We argue that a combination of null models is needed to evaluate the forecasting performance of predictive models for infectious diseases and the grand mean is the lowest bar. 

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