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In statistics, an additive model (AM) is a nonparametric regression method. It was suggested by Jerome H. Friedman and Werner Stuetzle (1981) [ 1 ] and is an essential part of the ACE algorithm. The AM uses a one-dimensional smoother to build a restricted class of nonparametric regression models.
The additive model has been suggested to be a better fit for predicting disease risk in a population while a multiplicative model is more appropriate for disease etiology. [ 2 ] Epigenetics is an example of an underlying mechanism of gene–environment effects, however, it does not conclude whether environment effects are additive ...
Nonparametric regression is a category of regression analysis in which the predictor does not take a predetermined form but is constructed according to information derived from the data.
Some common models for how the phenotypes of independent genes combine include the min, additive, and multiplicative models. [1] [3] In the min model, the expected fitness resulting from the mutation of two independent genes is the same as the fitness of the least-fit single mutant. [3]
An additive model would be used when the variations around the trend do not vary with the level of the time series whereas a multiplicative model would be appropriate if the trend is proportional to the level of the time series. [3] Sometimes the trend and cyclical components are grouped into one, called the trend-cycle component.
Interaction effect of education and ideology on concern about sea level rise. In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the effect of one causal variable on an outcome depends on the state of a second causal variable (that is, when effects of the two causes are not additive).
If this was just five years ago, let alone 10 or 20, the prospect of 72-year-old Bill Belichick as a college football coach would have been more about a splashy hire than the promise of great success.
GAMs were originally developed by Trevor Hastie and Robert Tibshirani [1] to blend properties of generalized linear models with additive models. They can be interpreted as the discriminative generalization of the naive Bayes generative model. [2] The model relates a univariate response variable, Y, to some predictor variables, x i.