In Galit Shmueli's "To Explain or Predict," https://www.researchgate.net/publication/48178170_To_Explain_or_to_Predict, on pages 5 and 6, there is a reference to Hastie, Tibshirani, and Freedman(2009), for statistical learning, which breaks expected square of the prediction error into the two parts of a variance of a prediction error and the square of the bias due to model misspecification. (Variance - bias tradeoff is discussed in Hastie, et.al. and other sources.)
An example of another kind of variance bias tradeoff that comes to mind would be the use of cutoff or quasi-cutoff sampling for highly skewed establishment surveys using model-based estimation (i.e., prediction from regression in such a cross-sectional survey of a finite population). The much smaller variance obtained is partially traded for a higher bias applied to small members of the population that should not be very much of the population totals (as may be studied by cross validation and other means). Thus some model misspecification will often not be crucial, especially if applied to carefully grouped (stratified) data.
[Note that if a BLUE (best linear unbiased estimator) is considered desirable, it is the estimator with the best variance, so bias must be considered under control, or you have to do something about it.]
Other means to tradeoff variance and bias seem apparent: General examples include various small area estimation (SAE) methods. -
Shrinkage estimators tradeoff increased bias for lower variance.
Are there other general categories of applications that come to mind?
Do you have any specific applications that you might share?
Perhaps you may have a paper on ResearchGate that relates to this.
Any example of any kind of bias variance tradeoff would be of possible interest.
Thank you.
Article To Explain or to Predict?
here i attached a link is found on your query
http://scott.fortmann-roe.com/docs/BiasVariance.html
Good link Manoj. It helps to compare notation with other sources. Thank you.
Here are three papers that might be of interest
1 : this discusses a multilevel model as a measurement model and how shrinkage is applied to reduce the Mean Square Error
https://www.researchgate.net/publication/252146040_Do_multilevel_models_ever_give_different_results
2 These show how to reduce MSE in the analysis of a large table with random coefficient modelling
https://www.researchgate.net/publication/269409538_Modelling_Interactions_A_Random-Effects_Approach_to_the_Analysis_of_Large_Tables_of_Counts_illustrated_by_two_Welsh_Referenda
https://www.researchgate.net/publication/266383638_A_model-_based_approach_to_the_analysis_of_a_large_table_of_counts_occupational_class_patterns_in_among_Australians_by_ancestry_generation_and_age_group
Article Do multilevel models ever give different results?.
Article Modelling Interactions: A Random-Effects Approach to the Ana...
Article A model- based approach to the analysis of a large table of ...
This might be helpful
http://scott.fortmann-roe.com/docs/BiasVariance.html
Yes, the Fortmann-Roe document is a good one, as noted above by Manoj Kuppusamy, and now by Desmond Bala Bisandu. Good to be reminded.
It is referenced several times in https://www.researchgate.net/publication/301285520_Prediction_for_Finite_Populations_Cutoff_Sampling_and_Related_Issues
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