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- W2341792980 abstract "Prediction is an important uspect of the scientific study of any phenomenon. In the traditional framework of regression analysis, the predictions are obtained either for the average value of the study variable or for its actual value. This has limited utility in the sense that either of the two predictions are not all that enough in general. Situations are not uncommon where we need to consider both these predictions simultaneously. For example. in a consumer market the life guarantee of an item on sale is of paramount importance. The promoter of the item concerned claims the guarantee on the basis of its average life prediction while the consumer takes decisio~ regarding its purchac;e Qn the basis of its actual life prediction.The classicaltheoryof predictioncan safeguardthe interest of either the promoter or the consumer at a time but ~an not vousafe for both simultaneously.. This can be achieved by defining a target function of the average and the actuat value predictions of the study variable with possibly different weights assigned to them. This composite function w(!JUldtake care of the two predictions simultaneously and would allow us to assign desired weightage to them according to their respective importance in any given application; see, for example, Zellner (1994). Another major limitation of the classical least squares theory of prediction I is that it is based on the assumption that the observations on the variables I involvedinthemodelarefreefrommeasurement elTors. In practice,bowever," @default.
- W2341792980 created "2016-06-24" @default.
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- W2341792980 date "1995-01-01" @default.
- W2341792980 modified "2023-09-27" @default.
- W2341792980 title "PREDICTIONS IN LINEAR REGRESSIONMODEL WITH MEASUREMENTERRORS" @default.
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