The MSE is a measure of the quality of an estimator. As it is derived from the square of Euclidean distance
, it is always a positive value with the error decreasing as the error approaches zero.
The MSE is the second moment
(about the origin) of the error,[clarification needed]
and thus incorporates both the variance
of the estimator (how widely spread the estimates are from one data sample
to another) and its bias
(how far off the average estimated value is from the true value).
For an unbiased estimator
, the MSE is the variance of the estimator. Like the variance, MSE has the same units of measurement as the square of the quantity being estimated. In an analogy to standard deviation
, taking the square root of MSE yields the root-mean-square error or root-mean-square deviation
(RMSE or RMSD), which has the same units as the quantity being estimated; for an unbiased estimator, the RMSE is the square root of the variance
, known as the standard error
Definition and basic properties
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
mapping a sample
of data to an estimate of a parameter
of the population
from which the data is sampled). The definition of an MSE differs according to whether one is describing a predictor or an estimator.
If a vector of predictions is generated from a sample of n
data points on all variables, and
is the vector of observed values of the variable being predicted, with
being the predicted values (e.g. as from a least-squares fit), then the within-sample MSE of the predictor is computed as
In other words, the MSE is the mean
of the squares of the errors
. This is an easily computable quantity for a particular sample (and hence is sample-dependent).
and is the
The MSE can also be computed on q
data points that were not used in estimating the model, either because they were held back for this purpose, or because these data have been newly obtained. In this process (known as cross-validation
), the MSE is often called the mean squared prediction error
, and is computed as
The MSE of an estimator with respect to an unknown parameter is defined as
This definition depends on the unknown parameter, but the MSE is a priori a property of an estimator. The MSE could be a function of unknown parameters, in which case any estimator of the MSE based on estimates of these parameters would be a function of the data (and thus a random variable). If the estimator is derived as a sample statistic and is used to estimate some population parameter, then the expectation is with respect to the sampling distribution of the sample statistic.
The MSE can be written as the sum of the variance
of the estimator and the squared bias
of the estimator, providing a useful way to calculate the MSE and implying that in the case of unbiased estimators, the MSE and variance are equivalent.
Proof of variance and bias relationship
Alternatively, we have
But in real modeling case, MSE could be described as the addition of model variance, model bias, and irreducible uncertainty[clarification needed]
. According to the relationship, the MSE of the estimators could be simply used for the efficiency
comparison, which includes the information of estimator variance and bias. This is called MSE criterion.
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 calculated, and shown as the mean squared error. The squaring is critical to reduce the complexity with negative signs. To minimize MSE, the model could be more accurate, which would mean the model is closer to actual data. One example of a linear regression using this method is the least squares method
—which evaluates appropriateness of linear regression model to model bivariate dataset
but whose the limitation is related to known distribution of the data.
The term mean squared error
is sometimes used to refer to the unbiased estimate of error variance: the residual sum of squares
divided by the number of degrees of freedom
. This definition for a known, computed quantity differs from the above definition for the computed MSE of a predictor, in that a different denominator is used. The denominator is the sample size reduced by the number of model parameters estimated from the same data, (n-p)
for p regressors
if an intercept is used (see errors and residuals in statistics
for more details).
Although the MSE (as defined in this article) is not an unbiased estimator of the error variance, it is consistent
, given the consistency of the predictor.
In regression analysis, "mean squared error", often referred to as mean squared prediction error
or "out-of-sample mean squared error", can also refer to the mean value of the squared deviations
of the predictions from the true values, over an out-of-sample test space, generated by a model estimated over a particular sample space. This also is a known, computed quantity, and it varies by sample and by out-of-sample test space.
Suppose we have a random sample of size from a population,
. Suppose the sample units were chosen with replacement. That is, the units are selected one at a time, and previously selected units are still eligible for selection for all draws. The usual estimator for the is the sample average
which has an expected value equal to the true mean (so it is unbiased) and a mean squared error of
This is unbiased (its expected value is
), hence also called the unbiased sample variance,
and its MSE is
However, one can use other estimators for
which are proportional to
, and an appropriate choice can always give a lower mean squared error. If we define
then we calculate:
This is minimized when
For a Gaussian distribution
, this means that the MSE is minimized when dividing the sum by
. The minimum excess kurtosis is
which is achieved by a Bernoulli distribution
= 1/2 (a coin flip), and the MSE is minimized for
Hence regardless of the kurtosis, we get a "better" estimate (in the sense of having a lower MSE) by scaling down the unbiased estimator a little bit; this is a simple example of a shrinkage estimator
: one "shrinks" the estimator towards zero (scales down the unbiased estimator).
Further, while the corrected sample variance is the best unbiased estimator
(minimum mean squared error among unbiased estimators) of variance for Gaussian distributions, if the distribution is not Gaussian, then even among unbiased estimators, the best unbiased estimator of the variance may not be
The following table gives several estimators of the true parameters of the population, μ and σ2
, for the Gaussian case.
An MSE of zero, meaning that the estimator predicts observations of the parameter with perfect accuracy, is ideal (but typically not possible).
Values of MSE may be used for comparative purposes. Two or more statistical models
may be compared using their MSEs—as a measure of how well they explain a given set of observations: An unbiased estimator (estimated from a statistical model) with the smallest variance among all unbiased estimators is the best unbiased estimator
or MVUE (Minimum Variance Unbiased Estimator).
Both linear regression
techniques such as analysis of variance
estimate the MSE as part of the analysis and use the estimated MSE to determine the statistical significance
of the factors or predictors under study. The goal of experimental design
is to construct experiments in such a way that when the observations are analyzed, the MSE is close to zero relative to the magnitude of at least one of the estimated treatment effects.
In one-way analysis of variance
, MSE can be calculated by the division of the sum of squared errors and the degree of freedom. Also, the f-value is the ratio of the mean squared treatment and the MSE.
MSE is also used in several stepwise regression
techniques as part of the determination as to how many predictors from a candidate set to include in a model for a given set of observations.
- Minimizing MSE is a key criterion in selecting estimators: see minimum mean-square error. Among unbiased estimators, minimizing the MSE is equivalent to minimizing the variance, and the estimator that does this is the minimum variance unbiased estimator. However, a biased estimator may have lower MSE; see estimator bias.
- In statistical modelling the MSE can represent the difference between the actual observations and the observation values predicted by the model. In this context, it is used to determine the extent to which the model fits the data as well as whether removing some explanatory variables is possible without significantly harming the model's predictive ability.
- In forecasting and prediction, the Brier score is a measure of forecast skill based on MSE.
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 actual loss in applications. Carl Friedrich Gauss
, who introduced the use of mean squared error, was aware of its arbitrariness and was in agreement with objections to it on these grounds.
The mathematical benefits of mean squared error are particularly evident in its use at analyzing the performance of linear regression
, as it allows one to partition the variation in a dataset into variation explained by the model and variation explained by randomness.
The use of mean squared error without question has been criticized by the decision theoristJames Berger
. Mean squared error is the negative of the expected value of one specific utility function
, the quadratic utility function, which may not be the appropriate utility function to use under a given set of circumstances. There are, however, some scenarios where mean squared error can serve as a good approximation to a loss function occurring naturally in an application.
, mean squared error has the disadvantage of heavily weighting outliers
This is a result of the squaring of each term, which effectively weights large errors more heavily than small ones. This property, undesirable in many applications, has led researchers to use alternatives such as the mean absolute error
, or those based on the median
This can be proved by Jensen's inequality
as follows. The fourth central moment
is an upper bound for the square of variance, so that the least value for their ratio is one, therefore, the least value for the excess kurtosis
is −2, achieved, for instance, by a Bernoulli with p
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Last edited on 7 June 2021, at 21:18
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