Asymmetric Statistical Errors
نویسنده
چکیده
Asymmetric statistical errors arise for experimental results obtained by Maximum Likelihood estimation, in cases where the number of results is finite and the log likelihood function is not a symmetric parabola. This note discusses how separate asymmetric errors on a single result should be combined, and how several results with asymmetric errors should be combined to give an overall measurement. In the process it considers several methods for parametrising curves that are approximately parabolic.
منابع مشابه
Asymmetric Errors
Errors quoted on results are often given in asymmetric form. An account is given of the two ways these can arise in an analysis, and the combination of asymmetric errors is discussed. It is shown that the usual method has no basis and is indeed wrong. For asymmetric systematic errors, a consistent method is given, with detailed examples. For asymmetric statistical errors a general approach is o...
متن کاملUnskilled, unaware, or both? The better-than-average heuristic and statistical regression predict errors in estimates of own performance.
People who score low on a performance test overestimate their own performance relative to others, whereas high scorers slightly underestimate their own performance. J. Kruger and D. Dunning (1999) attributed these asymmetric errors to differences in metacognitive skill. A replication study showed no evidence for mediation effects for any of several candidate variables. Asymmetric errors were ex...
متن کاملSingle Facility Goal Location Problems with Symmetric and Asymmetric Penalty Functions
Location theory is an interstice field of optimization and operations research. In the classic location models, the goal is finding the location of one or more facilities such that some criteria such as transportation cost, the sum of distances passed by clients, total service time, and cost of servicing are minimized. The goal Weber location problem is a special case of location mode...
متن کاملArtificial Neural Networks for Time Series Prediction - A Novel Approach to Inventory Management Using Asymmetric Cost Functions
Artificial neural networks in time series prediction generally minimize a symmetric statistical error, such as the sum of squared errors, to model least squares predictors. However, applications in business elucidate that real forecasting problems contain non-symmetric errors. In inventory management the costs arising from overversus underprediction are dissimilar for errors of identical magnit...
متن کاملTraining Artificial Nexjral Networks for Time Series Prediction Using Asymmetric Cost Functions
Artificial neural network theory generally minimises a standard statistical error, such as the sum of squared errors, to learn relationships fiom the presented data. However, applications in business have shown that real forecasting problems require alternative error measures. Errors, identical in magnitude, cause different costs. To reflect this, a set of asymmetric cost functions is proposed ...
متن کامل