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Fitting a Multiple Regression Function

A statistical problem which finds a wide range of applications is the estimation of a regression function. In this document an estimate is proposed when there are at least two independent regressors. This is not a direct generalization of the Priestley-Chao estimate. Also presented is a consistent e...

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Main Authors:	Ahmad,Ibrahim A, Lin,Pi-Erh
Format:	Report
Language:	English
Subjects:	Analysis of variance Asymptotic normality Confidence limits Consistency Convergence Errors Estimates Fitting functions(Mathematics) Kernels Probability density functions Random variables Regression analysis Statistics and Probability
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creator	Ahmad,Ibrahim A Lin,Pi-Erh
description	A statistical problem which finds a wide range of applications is the estimation of a regression function. In this document an estimate is proposed when there are at least two independent regressors. This is not a direct generalization of the Priestley-Chao estimate. Also presented is a consistent estimate for the error variance. With the aid of the variance estimate, an asymptotic confidence interval can be constructed.
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In this document an estimate is proposed when there are at least two independent regressors. This is not a direct generalization of the Priestley-Chao estimate. Also presented is a consistent estimate for the error variance. 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With the aid of the variance estimate, an asymptotic confidence interval can be constructed.</description><subject>Analysis of variance</subject><subject>Asymptotic normality</subject><subject>Confidence limits</subject><subject>Consistency</subject><subject>Convergence</subject><subject>Errors</subject><subject>Estimates</subject><subject>Fitting functions(Mathematics)</subject><subject>Kernels</subject><subject>Probability density functions</subject><subject>Random variables</subject><subject>Regression analysis</subject><subject>Statistics and Probability</subject><fulltext>true</fulltext><rsrctype>report</rsrctype><creationdate>1982</creationdate><recordtype>report</recordtype><sourceid>1RU</sourceid><recordid>eNrjZFBzyywpycxLV0hU8C3NKcksyElVCEpNL0otLs7Mz1NwK81LLgEyeBhY0xJzilN5oTQ3g4yba4izh25KSWZyfDHQhNSSeEcXR0NjYxMLI2MC0gDdBSSc</recordid><startdate>198212</startdate><enddate>198212</enddate><creator>Ahmad,Ibrahim A</creator><creator>Lin,Pi-Erh</creator><scope>1RU</scope><scope>BHM</scope></search><sort><creationdate>198212</creationdate><title>Fitting a Multiple Regression Function</title><author>Ahmad,Ibrahim A ; Lin,Pi-Erh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-dtic_stinet_ADA1334823</frbrgroupid><rsrctype>reports</rsrctype><prefilter>reports</prefilter><language>eng</language><creationdate>1982</creationdate><topic>Analysis of variance</topic><topic>Asymptotic normality</topic><topic>Confidence limits</topic><topic>Consistency</topic><topic>Convergence</topic><topic>Errors</topic><topic>Estimates</topic><topic>Fitting functions(Mathematics)</topic><topic>Kernels</topic><topic>Probability density functions</topic><topic>Random variables</topic><topic>Regression analysis</topic><topic>Statistics and Probability</topic><toplevel>online_resources</toplevel><creatorcontrib>Ahmad,Ibrahim A</creatorcontrib><creatorcontrib>Lin,Pi-Erh</creatorcontrib><creatorcontrib>FLORIDA STATE UNIV TALLAHASSEE DEPT OF STATISTICS</creatorcontrib><collection>DTIC Technical Reports</collection><collection>DTIC STINET</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ahmad,Ibrahim A</au><au>Lin,Pi-Erh</au><aucorp>FLORIDA STATE UNIV TALLAHASSEE DEPT OF STATISTICS</aucorp><format>book</format><genre>unknown</genre><ristype>RPRT</ristype><btitle>Fitting a Multiple Regression Function</btitle><date>1982-12</date><risdate>1982</risdate><abstract>A statistical problem which finds a wide range of applications is the estimation of a regression function. 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source	DTIC Technical Reports
subjects	Analysis of variance Asymptotic normality Confidence limits Consistency Convergence Errors Estimates Fitting functions(Mathematics) Kernels Probability density functions Random variables Regression analysis Statistics and Probability
title	Fitting a Multiple Regression Function
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