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A weight function is a mathematical device used when performing a sum, integral, or average to give some elements more "weight" or influence on the result than other elements in the same set. The result of this application of a weight function is a weighted sum or weighted average.
Weighted least squares (WLS), also known as weighted linear regression, [1] [2] is a generalization of ordinary least squares and linear regression in which knowledge of the unequal variance of observations (heteroscedasticity) is incorporated into the regression.
The weighted mean in this case is: ¯ = ¯ (=), (where the order of the matrix–vector product is not commutative), in terms of the covariance of the weighted mean: ¯ = (=), For example, consider the weighted mean of the point [1 0] with high variance in the second component and [0 1] with high variance in the first component.
A kernel smoother is a statistical technique to estimate a real valued function: as the weighted average of neighboring observed data. The weight is defined by the kernel, such that closer points are given higher weights. The estimated function is smooth, and the level of smoothness is set by a single parameter.
The proof for positive p and q is as follows: Define the following function: f : R + → R + =. f is a power function, so it does have a second derivative: f ″ ( x ) = ( q p ) ( q p − 1 ) x q p − 2 {\displaystyle f''(x)=\left({\frac {q}{p}}\right)\left({\frac {q}{p}}-1\right)x^{{\frac {q}{p}}-2}} which is strictly positive within the ...
In weighted least squares, the definition is often written in matrix notation as =, where r is the vector of residuals, and W is the weight matrix, the inverse of the input (diagonal) covariance matrix of observations.
The method of mean weighted residuals solves (,,, …,) = by imposing that the degrees of freedom are such that: ((,,, …,),) =is satisfied. Where the inner product (,) is the standard function inner product with respect to some weighting function () which is determined usually by the basis function set or arbitrarily according to whichever weighting function is most convenient.
Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals.