PREDICTIVE INFERENCE

By Richard L. Smith. 2004

Subfields: Probabilistic & Bayesian Methods

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Probabilistic & Bayesian Methods

1. Belén Diezma
2. L. Lleó
3. M. Ruiz-Altisent
4. A. Herrero‐Langreo
5. Jing Gao
6. Florian Hartig
7. Bin Yu
8. Qi He

Citations in the corpus

• Veridical data science (2019, background): “Seymour Geisser advocated that statistical analyses should focus on prediction rather than parametric inference, particularly in cases where the statistical model is an inappropriate description of reality (26).”
• Managing computational complexity using surrogate models: a critical review (2020, methodology): “In this method the data is divided into test and training data sets and the process is repeated several times (Geisser 1993).”
• A deep learning approach for detecting traffic accidents from social media data (2018, methodology): “Besides the computing time, we further implement 5-fold cross validation to overcome the over-fitting problem (Geisser, 1993) in the process of model training.”
• Three principles of data science: predictability, computability, and stability (PCS) (2018, background): “Proceedings of the National Academy of Sciences 112(25):7629–7634.”
• Examination of the quality of spinach leaves using hyperspectral imaging (2013, methodology): “PLS-DA / CovSel3 A total of 6 latent variables were chosen for the PLS-DA by means of a cross-validation4 procedure (Geisser, 1993).”
• Model averaging in ecology: a review of Bayesian, information-theoretic, and tactical approaches for predictive inference (2018, background): “Instead, our focus is on prediction, and predictive inference (sensu Geisser 1993), as exemplified by model-averaged predictions of species potential occurence for reserve-site selection (Meller et al. 2014) or the effect of roads on occupancy of ponds by frogs (Dai and Wang 2011).”