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Robust Random Regression Imputation for Missing Data: English - Habshah Midi,Sohel Rana,Ahamefule Happy John

English
2015-11-30
€79.03 €112.90

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I have written this book for those students studying statistics and mathematics.The need for this book as become apparent in many years with several students having only classical back ground. We consider when it missing in Y,The aim of this book is to help in solving the problem when missing in both side and outliers exist.This book proposes simple but very interesting robust single imputation technique wh ... Full description

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Description

I have written this book for those students studying statistics and mathematics.The need for this book as become apparent in many years with several students having only classical back ground. We consider when it missing in Y,The aim of this book is to help in solving the problem when missing in both side and outliers exist.This book proposes simple but very interesting robust single imputation technique which gives more accurate estimates over the classical single imputation technique in the presence of outliers. we also considered a situation in which observations are missing in the X explanatory variable. In this respect, the Dummy Variable (DV) approach is one of the best approaches to predict the missing data model. However, this approach also becomes poor in the presence of outliers. As an alternative, Robust Inverse Regression Technique is proposed to get the better estimate. By examining the real data and Monte Carlo Simulation studies, it revealed that our proposed robust methods perform better than the classical methods.

More Information

Author Habshah Midi, Sohel Rana, Ahamefule Happy John
Publisher LAP LAMBERT Academic Publishing
Release year 2015
Cover type Softcover
EAN 9783659111969
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€79.03 €112.90