Search results
Results from the WOW.Com Content Network
In statistics, Deming regression, named after W. Edwards Deming, is an errors-in-variables model that tries to find the line of best fit for a two-dimensional data set. It differs from the simple linear regression in that it accounts for errors in observations on both the x - and the y - axis.
It is a generalization of Deming regression and also of orthogonal regression, and can be applied to both linear and non-linear models. The total least squares approximation of the data is generically equivalent to the best, in the Frobenius norm, low-rank approximation of the data matrix. [1]
In Deming regression, a type of linear curve fitting, if the dependent and independent variables have equal variance this results in orthogonal regression in which the degree of imperfection of the fit is measured for each data point as the perpendicular distance of the point from the regression line.
Vertical distance: Simple linear regression; Resistance to outliers: Robust simple linear regression; Perpendicular distance: Orthogonal regression (this is not scale-invariant i.e. changing the measurement units leads to a different line.) Weighted geometric distance: Deming regression
It may be considered a robust version of reduced major axis regression. The slope estimator b {\displaystyle b} is the median of the absolute values of all pairwise slopes. The original algorithm is rather slow for larger data sets as its computational complexity is O ( n 2 ) {\displaystyle O(n^{2})} .
Linear errors-in-variables models were studied first, probably because linear models were so widely used and they are easier than non-linear ones. Unlike standard least squares regression (OLS), extending errors in variables regression (EiV) from the simple to the multivariable case is not straightforward, unless one treats all variables in the same way i.e. assume equal reliability.
DeFries–Fulker regression; de Moivre's law; De Moivre–Laplace theorem; Decision boundary; Decision theory; Decomposition of time series; Degenerate distribution; Degrees of freedom (statistics) Delaporte distribution; Delphi method; Delta method; Demand forecasting; Deming regression; Demographics; Demography. Demographic statistics ...
Pages in category "Regression analysis" The following 95 pages are in this category, out of 95 total. ... Deming regression; Dependent and independent variables;