Biased power regression: a new biased estimation procedure in linear regression

In order to circumvent the effects of multicollinearity on the quality of a multiple linear regression, a new strategy of analysis is proposed. It is based on a biased estimation of the vector of coefficients. Properties of this approach of analysis are shown. Moreover, the link between this new strategy of analysis and existing strategies are discussed, particularly Ridge and Generalized Ridge regression. Illustrations on the basis of two datasets are also outlined and the outcomes are compared to those of Ridge regression

Understanding aroma release from model cheeses by a statistical multiblock approach on oral processing

For human beings, the mouth is the first organ to perceive food and the different signalling events associated to food breakdown. These events are very complex and as such, their description necessitates combining different data sets. This study proposed an integrated approach to understand the relative contribution of main food oral processing events involved in aroma release during cheese consumption. In vivo aroma release was monitored on forty eight subjects who were asked to eat four different model cheeses varying in fat content and firmness and flavoured with ethyl propanoate and nonan-2-one. A multiblock partial least square regression was performed to explain aroma release from the different physiological data sets ( masticatory behaviour, bolus rheology, saliva composition and flux, mouth coating and bolus moistening). This statistical approach was relevant to point out that aroma release was mostly explained by masticatory behaviour whatever the cheese and the aroma, with a specific influence of mean amplitude on aroma release after swallowing. Aroma release from the firmer cheeses was explained mainly by bolus rheology. The persistence of hydrophobic compounds in the breath was mainly explained by bolus spreadability, in close relation with bolus moistening. Resting saliva poorly contributed to the analysis whereas the composition of stimulated saliva was negatively correlated with aroma release and mostly for soft cheeses, when significant

Gait characterization in golden retriever muscular dystrophy dogs using linear discriminant analysis

Background: Accelerometric analysis of gait abnormalities in golden retriever muscular dystrophy (GRMD) dogs isof limited sensitivity, and produces highly complex data. The use of discriminant analysis may enable simpler andmore sensitive evaluation of treatment benefits in this important preclinical model.Methods: Accelerometry was performed twice monthly between the ages of 2 and 12 months on 8 healthy and20 GRMD dogs. Seven accelerometric parameters were analysed using linear discriminant analysis (LDA). Manipulationof the dependent and independent variables produced three distinct models. The ability of each model to detect gaitalterations and their pattern change with age was tested using a leave-one-out cross-validation approach.Results: Selecting genotype (healthy or GRMD) as the dependent variable resulted in a model (Model 1) allowing agood discrimination between the gait phenotype of GRMD and healthy dogs. However, this model was not sufficientlyrepresentative of the disease progression. In Model 2, age in months was added as a supplementary dependentvariable (GRMD_2 to GRMD_12 and Healthy_2 to Healthy_9.5), resulting in a high overall misclassification rate (83.2%).To improve accuracy, a third model (Model 3) was created in which age was also included as an explanatory variable.This resulted in an overall misclassification rate lower than 12%. Model 3 was evaluated using blinded data pertainingto 81 healthy and GRMD dogs. In all but one case, the model correctly matched gait phenotype to the actualgenotype. Finally, we used Model 3 to reanalyse data from a previous study regarding the effects ofimmunosuppressive treatments on muscular dystrophy in GRMD dogs. Our model identified significant effect ofimmunosuppressive treatments on gait quality, corroborating the original findings, with the added advantages ofdirect statistical analysis with greater sensitivity and more comprehensible data representation.Conclusions: Gait analysis using LDA allows for improved analysis of accelerometry data by applying adecision-making analysis approach to the evaluation of preclinical treatment benefits in GRMD dogs

A simple alternative to Generalized Procrustes Analysis. Application to sensory profiling data

A statistical method for analysing sensory profiling data obtained by means of fixed vocabulary or free choice profiling is discussed. The most interesting feature of this method is that it involves only simple statistical treatment and can therefore be performed using standard software packages. The outcomes of this method are compared to those of Generalized Procrustes Analysis on the basis of two data sets obtained respectively by means of fixed vocabulary and free choice profiling. A significance test is also discussed in order to assess whether the overall configuration of the products is meaningful. This significance test is based upon a simulation study involving the permutation procedure. Keywords : Sensory profiling, Principal Components Analysis, Generalized Procrustes Analysis, Isotropic scaling factors, Permutation test. 3 Introduction Generalized Procrustes Analysis (GPA) was introduced and popularized by Gower (1975). It is used for the analysis of sensory profiling da..

A continuum standardization of the variables. Application to principal components analysis and PLS-regression

International audienceA continuum standardization of the variables is proposed. It consists of dividing each variable by its standard deviation to the power α, where α is a scalar between 0 and 1. The non-standardized dataset and the standardized datasets appear as the two extreme points of this continuum and the Pareto standardization is its mid-point. Properties related to this standardization are discussed. Application to principal components analysis and PLS-regression is outlined and illustrated on the basis of real datasets

A simple alternative to generalized procrustes analysis: application to sensory pro®ling data.

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Measures of association between two datasets; Application to sensory data

International audienceWe review three measures of association between two datasets in view of their use in sensory data. The aim is threefold: (i) to show in which situations each measure of association is appropriate, (ii) to show their properties and how they can be applied efficiently to sensory data, (iii) to compare them. The three measures of association are multivariate correlation coefficient, RV coefficient and Procrustes similarity index. A particular emphasis is put on RV coefficient since it is very popular among sensory scientists. We stress the properties and shortcomings of this coefficient and propose an adjusted RV coefficient to be used instead of RV coefficient, particularly in situations where the number of samples is small or/and the number of variables is large

CLUSTATIS: cluster analysis of blocks of variables

The STATIS method is one of many strategies of analysis devoted to the unsupervised analysis of multiblock data. A new optimization criterion to define this method of analysis is introduced and an extension to the cluster analysis of several blocks of variables is discussed. This consists in a hierarchical cluster analysis and a partitioning algorithm akin to the K-means algorithm. Moreover, in order to improve the cluster analysis outcomes, an additional cluster called noise cluster which contains atypical blocks of variables is introduced. The general strategy of analysis is illustrated by means of two cases studies