Excerpt from Minimax Linear Predictor Under Lipschitz' Type Conditions for the Regression Function Regression analysis is the most (or at least one of the most) popular and most often used techniques in various fields of statistical data analysis. In some cases, how ever, regression analysis is very dangerous, and sometimes gives awkward results. Such dangers, which are inherent in regression techniques, are well known, at least well perceived by experienced applied statisticians. But theoretical analysis of such a ...
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Excerpt from Minimax Linear Predictor Under Lipschitz' Type Conditions for the Regression Function Regression analysis is the most (or at least one of the most) popular and most often used techniques in various fields of statistical data analysis. In some cases, how ever, regression analysis is very dangerous, and sometimes gives awkward results. Such dangers, which are inherent in regression techniques, are well known, at least well perceived by experienced applied statisticians. But theoretical analysis of such a situation that yields some pitfalls to the careless application of regression analysis is far from satisfactory. Though well trained statisticians can evade such a danger by their good judgment, there is no formal well established technique that may be applied. The purpose of this paper is to derive some method to treat one such difficulty, i.e. The problem of the functional form of the regression. Suppose that we have a quantity or response Y, which is influenced by some quantity or explanatory variable x. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at ... This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
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