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LINEAR REGRESSION ANALYSIS
, 2014
"... Determining if a dataset has one or more outliers is a fundamental and challenging problem in statistical analysis. This dissertation introduces a statistical framework that addresses two wellknown problems in the outlier analysis. The first problem (Problem 1) is to detect outliers in independent ..."
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and identically distributed univariate samples, which is the basic setting of outlier problem. The second problem (Problem 2) is to detect outliers and influential observations in the linear regression analysis, which is a major topic in linear regression model diagnostics and represents a more complete setting
OPTIMAL PREDICTION IN LINEAR REGRESSION ANALYSIS
, 1987
"... Expressions are derived for generalized ridge (GR), ordinary ridge (OR) and shrunken least squares (SLS) predictors that are optimal for predicting the response at a single or at multiple future observations. As in the case of biased estimation, these predictors depend on the true (population) regre ..."
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) regression coefficient values and the true variance of the underlying linear regression model. Hence, we propose operational predictors where the unknown parameters in the biased predictors are estimated from the data. Using the Mean Squared Error of Prediction (MSEP) criterion, we compare the proposed
Respiratory resistive impedance in obstructive patients: linear regression analysis vs
, 903
"... Respiratory resistive impedance in obstructive patients: linear regression analysis vs viscoelastic modelling ..."
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Respiratory resistive impedance in obstructive patients: linear regression analysis vs viscoelastic modelling
Functional linear regression analysis for longitudinal data
 Ann. of Statist
, 2005
"... We propose nonparametric methods for functional linear regression which are designed for sparse longitudinal data, where both the predictor and response are functions of a covariate such as time. Predictor and response processes have smooth random trajectories, and the data consist of a small number ..."
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Cited by 69 (7 self)
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We propose nonparametric methods for functional linear regression which are designed for sparse longitudinal data, where both the predictor and response are functions of a covariate such as time. Predictor and response processes have smooth random trajectories, and the data consist of a small
Linear Regression Analysis under Sets of Conjugate Priors
"... REGRESSION is the central concept in applied statistics for analyzing multivariate, heterogenous data: The influence of a group of variables on one other variable is quantified by the regression parameter β. Here we extend standard Bayesian inference on β in linear regression models by considering i ..."
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Cited by 6 (4 self)
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imprecise conjugate priors. Inspired by a variation and an extension of a method for inference in i.i.d. exponential families presented at ISIPTA’05 by Quaeghebeur and de Cooman, we develop a general framework for handling linear regression models including analysis of variance models, and discuss obstacles
Possibility Linear Regression Analysis with Trapezoidal Fuzzy Data
"... Abstract: In general fuzzy linear regression, the coefficients of the fuzzy regression model are symmetric triangular fuzzy numbers, while we try to replace them by more general ones, which are asymmetric trapezoidal fuzzy numbers. Possibility of equality between two asymmetric trapezoidal fuzzy num ..."
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Abstract: In general fuzzy linear regression, the coefficients of the fuzzy regression model are symmetric triangular fuzzy numbers, while we try to replace them by more general ones, which are asymmetric trapezoidal fuzzy numbers. Possibility of equality between two asymmetric trapezoidal fuzzy
RESEARCH INTO MULTIPLE OUTLIERS IN LINEAR REGRESSION ANALYSIS
"... Studying the observations in regression analysis it is seen that the output of regression is affected from outliers in the direction of the dependent and / or the independent variables. In this paper multiple outliers are examined in two real data sets. The results concerned with which method can ..."
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Studying the observations in regression analysis it is seen that the output of regression is affected from outliers in the direction of the dependent and / or the independent variables. In this paper multiple outliers are examined in two real data sets. The results concerned with which method can
Chapter 4. Linear Regression Analysis..................... 25
, 2013
"... For a hardcopy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. For a web download or ebook: ..."
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For a hardcopy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. For a web download or ebook: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. The scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others ’ rights is appreciated.
A New Method of Robust Linear Regression Analysis: Some Monte Carlo Experiments
, 2008
"... A new method of robust linear regression analysis: some monte carlo experiments ..."
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A new method of robust linear regression analysis: some monte carlo experiments
Sketch of an Alternative Approach to Linear Regression Analysis under Sets of Conjugate Priors
 Discussion Paper, 2007. http://www.statistik.lmu.de/˜thomas/ team/isipta07_conjugate_prior.pdf
"... This note is one of two supplements to the paper “Linear Regression Analysis under Sets of Conjugate Priors”, by G. Walter, T. Augustin, and A. Peters, in revision for ISIPTA 07, in which Bayesian inference in linear regression models is extended by considering imprecise conjugated priors. In that p ..."
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Cited by 2 (2 self)
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This note is one of two supplements to the paper “Linear Regression Analysis under Sets of Conjugate Priors”, by G. Walter, T. Augustin, and A. Peters, in revision for ISIPTA 07, in which Bayesian inference in linear regression models is extended by considering imprecise conjugated priors
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