.png){kind=icon}
Return
Handling missing values in multiple factor analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.foodqual.2013.04.013.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Handling missing values is an unavoidable problem in the practice of statistics. We focus on multiple factor analysis in the sense of Escofier and Pages (2008), a principal component method that simultaneously takes into account several multivariate datasets composed of continuous and/or categorical variables. The suggested strategy to deal with missing values, named regularised iterative MFA, is derived from a method available in principal component analysis which consists in alternating a step of estimation of the axes and components and a step of estimation of the missing values. The pattern of missing values considered can be structured with missing rows in some datasets. Some simulations and real examples that cover several situations in sensory analysis are used to illustrate the methodology. We focus on the important issue of the maximum number of products that can be assessed during an evaluation task. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Exploratory multivariate analysis
Missing values
Multi-table data
Multiple factor analysis
Napping data
Journal
IF:
4.9
Papers:
4.3K
Citations:
1.9W
Organization
Cited Papers
ShareSaveRapid Determination of Ractopamine in Porcine Urine by a Fluorescence Immunochromatography Assay
PREAI
ShareSaveShareSaveThe effects of phenylpropanolamine on food intake and body weight in the genetically obese (ob/ob) mouse.png)
Appetite
AppetiteIF0
PREAI
ShareSave
ShareSave
View more