Absolute Error Of A Sum
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linear model Generalized linear model Discrete choice Logistic regression Multinomial logit Mixed logit Probit Multinomial probit Ordered sum of absolute deviation logit Ordered probit Poisson Multilevel model Fixed effects Random absolute error formula effects Mixed model Nonlinear regression Nonparametric Semiparametric Robust Quantile Isotonic Principal components Least angle absolute error calculator Local Segmented Errors-in-variables Estimation Least squares Ordinary least squares Linear (math) Partial Total Generalized Weighted Non-linear Non-negative Iteratively reweighted Ridge regression Least absolute deviations absolute error example Bayesian Bayesian multivariate Background Regression model validation Mean and predicted response Errors and residuals Goodness of fit Studentized residual Gauss–Markov theorem Statistics portal v t e Least absolute deviations (LAD), also known as least absolute errors (LAE), least absolute value (LAV), least absolute residual (LAR), sum of
How To Find Absolute Error
absolute deviations, or the L1 norm condition, is a statistical optimality criterion and the statistical optimization technique that relies on it. Similar to the popular least squares technique, it attempts to find a function which closely approximates a set of data. In the simple case of a set of (x,y) data, the approximation function is a simple "trend line" in two-dimensional Cartesian coordinates. The method minimizes the sum of absolute errors (SAE) (the sum of the absolute values of the vertical "residuals" between points generated by the function and corresponding points in the data). The least absolute deviations estimate also arises as the maximum likelihood estimate if the errors have a Laplace distribution. Contents 1 Formulation of the problem 2 Contrasting least squares with least absolute deviations 3 Other properties 4 Variations, extensions, specializations 5 Solving methods 5.1 So
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Absolute Error Physics
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Phone: +1 can absolute error be negative (888) 427-9486+1 (312) 257-3777 Contact Us Home >> Support >> Documentation >> NumXL >> Reference Manual >> Descriptive mean absolute error Stats >> SAE SAE Calculates the sum of absolute errors (SAEi) between the forecast and the eventual outcomes. Syntax SAE(X, Y) X is the original (eventual outcomes) time series sample https://en.wikipedia.org/wiki/Least_absolute_deviations data (a one dimensional array of cells (e.g. rows or columns)). Y is the forecast time series data (a one dimensional array of cells (e.g. rows or columns)). Remarks The time series is homogeneous or equally spaced. The two time series must be identical in size. A missing value (say or ) in either time series will exclude the data http://www.spiderfinancial.com/support/documentation/numxl/reference-manual/descriptive-stats/sae point from the SSEi. The sum of absolute errors (SAE) or deviations (SAD), is defined as follows: Where: is the actual observations time series is the estimated or forecasted time series Examples Example 1: A B C 1 Date Series1 Series2 2 1/1/2008 #N/A -2.61 3 1/2/2008 -2.83 -0.28 4 1/3/2008 -0.95 -0.90 5 1/4/2008 -0.88 -1.72 6 1/5/2008 1.21 1.92 7 1/6/2008 -1.67 -0.17 8 1/7/2008 0.83 -0.04 9 1/8/2008 -0.27 1.63 10 1/9/2008 1.36 -0.12 11 1/10/2008 -0.34 0.14 12 1/11/2008 0.48 -1.96 13 1/12/2008 -2.83 1.30 14 1/13/2008 -0.95 -2.51 15 1/14/2008 -0.88 -0.93 16 1/15/2008 1.21 0.39 17 1/16/2008 -1.67 -0.06 18 1/17/2008 -2.99 -1.29 19 1/18/2008 1.24 1.41 20 1/19/2008 0.64 2.37 Formula Description (Result) =SAE($B$1:$B$19,$C$1:$C$19) SAE (24.59) Files Examples References Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0-691-04289-6 Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0-471-690740 Related Links Wikipedia - Least absolute deviations‹ RMSEupSSE › Reference SSE MAPE RMSE RMSD Download Sites - NumXL Try our full-featured product freeTour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us http://stats.stackexchange.com/questions/43470/mean-absolute-error-vs-sum-absolute-error Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question absolute error Anybody can answer The best answers are voted up and rise to the top Mean absolute error vs sum absolute error up vote 1 down vote favorite I'm using the Matlab Neural Network toolbox for a regression problem (twelve inputs, one target). My target has a high dynamic range and is strongly non-gaussian. So far I've solved this by a log-transformation, but as an alternative I'm exploring if absolute error of a different error function than the standard mean square error would be of use. The Matlab Neural Network toolbox comes with four built-in "performance" functions: >> help nnperformance Neural Network Toolbox Performance Functions. mae - Mean absolute error performance function. mse - Mean squared error performance function. sae - Sum absolute error performance function. sse - Sum squared error performance function. In my understanding, such a performance function is a cost function. But as a cost function is simply something to be minimised, what is the difference between Mean absolute error and Sum absolute error in practice? Similarly, what is the difference between Mean squared error and Sum squared error? Shouldn't those be the same? The documentation for the different functions is of no use. In fact, the documentation for mse and sse is identical apart except for one word. matlab neural-networks share|improve this question asked Nov 13 '12 at 9:12 gerrit 870619 add a comment| 2 Answers 2 active oldest votes up vote 1 down vote You are correct, the minimizers are the same. However, looking at mean values can be easier to interpret, while the sum is related to the complete log likelihood and can thus serve for comparison w
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