The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function See more

In regression analysis, plotting is a more natural way to view the overall trend of the whole data. The mean of the distance from each point to the predicted regression model can be … See more

An MSE of zero, meaning that the estimator $${\displaystyle {\hat {\theta }}}$$ predicts observations of the parameter $${\displaystyle \theta }$$ with perfect accuracy, is ideal (but typically not possible).
Values of MSE may … See more

Squared error loss is one of the most widely used loss functions in statistics, though its widespread use stems more from mathematical convenience than considerations of … See more

Mean
Suppose we have a random sample of size $${\displaystyle n}$$ from a population, $${\displaystyle X_{1},\dots ,X_{n}}$$. Suppose the sample units were chosen with replacement. That is, the $${\displaystyle n}$$ See more

• Minimizing MSE is a key criterion in selecting estimators: see minimum mean-square error. Among unbiased estimators, minimizing the MSE … See more

• Bias–variance tradeoff
• Hodges' estimator
• James–Stein estimator
• Mean percentage error
• Mean square quantization error See more