Minimax Linear Predictor Under Lipschitz' Type Conditions for the Regression Function (Classic Reprint)

Minimax Linear Predictor Under Lipschitz' Type Conditions for the Regression Function (Classic Reprint) PDF Author: Kei Takeuchi
Publisher: Forgotten Books
ISBN: 9780332434308
Category : Mathematics
Languages : en
Pages : 52

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Book Description
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 www.forgottenbooks.com 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.

Minimax Linear Predictor Under Lipschitz' Type Conditions for the Regression Function (Classic Reprint)

Minimax Linear Predictor Under Lipschitz' Type Conditions for the Regression Function (Classic Reprint) PDF Author: Kei Takeuchi
Publisher: Forgotten Books
ISBN: 9780332434308
Category : Mathematics
Languages : en
Pages : 52

Get Book Here

Book Description
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 www.forgottenbooks.com 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.

Minimax Linear Predictor Under Lipschitz' Type Conditions for the Regression Function

Minimax Linear Predictor Under Lipschitz' Type Conditions for the Regression Function PDF Author: Kei Takeuchi
Publisher: Palala Press
ISBN: 9781379102793
Category :
Languages : en
Pages : 50

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Book Description
This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.

Minimax Linear Predictor Under Lipschitz' Type Conditions for the Regression Function

Minimax Linear Predictor Under Lipschitz' Type Conditions for the Regression Function PDF Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
Regression analysis is one of the most popular and most often used techniques in various fields of statistical data analysis. In some cases, however, 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.

Minimax Linear Predictor Under Lipscitz' Type Conditions for the Regression Function

Minimax Linear Predictor Under Lipscitz' Type Conditions for the Regression Function PDF Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 43

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Book Description


Scientific and Technical Aerospace Reports

Scientific and Technical Aerospace Reports PDF Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 292

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Naval Research Reviews

Naval Research Reviews PDF Author:
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Category : Naval research
Languages : en
Pages : 468

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A Survey of Statistical Design and Linear Models

A Survey of Statistical Design and Linear Models PDF Author: Jagdish Narain Srivastava
Publisher: North-Holland
ISBN:
Category : Science
Languages : en
Pages : 716

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Book Description
This book comprises of papers presented at an International Symposium on Statistical Design and Linear Models, held in Colorado, 1973.

U.S. Government Research and Development Reports Index

U.S. Government Research and Development Reports Index PDF Author:
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ISBN:
Category : Nuclear science abstracts
Languages : en
Pages : 1492

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Minimax Linear Estimation at a Boundary Point

Minimax Linear Estimation at a Boundary Point PDF Author: Wayne Yuan Gao
Publisher:
ISBN:
Category :
Languages : en
Pages : 16

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Book Description
This paper characterizes the minimax linear estimator of the value of an unknown function at a boundary point of its domain in a Gaussian white noise model under the restriction that the first-order derivative of the unknown function is Lipschitz continuous (the second-order Holder class). The result is then applied to construct the minimax optimal estimator for the regression discontinuity design model, where the parameter of interest involves function values at boundary points.

Annual Report - Iowa State University, Statistical Laboratory

Annual Report - Iowa State University, Statistical Laboratory PDF Author: Iowa State University. Statistical Laboratory
Publisher:
ISBN:
Category :
Languages : en
Pages : 44

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Book Description