Combining chromatography and chemometrics for the characterization and authentication of fats and oils from triacylglycerol compositional data—A review

The characterization and authentication of fats and oils is a subject of great importance for market and health aspects. Identification and quantification of triacylglycerols in fats and oils can be excellent tools for detecting changes in their composition due to the mixtures of these products. Most of the triacylglycerol species present in either fats or oils could be analyzed and identified by chromatographic methods. However, the natural variability of these samples and the possible presence of adulterants require the application of chemometric pattern recognition methods to facilitate the interpretation of the obtained data. In view of the growing interest in this topic, this paper reviews the literature of the application of exploratory and unsupervised/supervised chemometric methods on chromatographic data, using triacylglycerol composition for the characterization and authentication of several foodstuffs such as olive oil, vegetable oils, animal fats, fish oils, milk and dairy products, cocoa and coffee.     

Visualizing hypothesis tests in multivariate linear models: the heplots package for R

Hypothesis-error (or “HE”) plots, introduced by Friendly (J Stat Softw 17(6):1–42, 2006a; J Comput Graph Stat 16:421–444, 2006b), permit the visualization of hypothesis tests in multivariate linear models by representing hypothesis and error matrices of sums of squares and cross-products as ellipses. This paper describes the implementation of these methods in the heplots package for R, as well as their extension, for example from two to three dimensions and by scaling hypothesis ellipses and ellipsoids in a natural manner relative to error. This is a paper for the proceedings of the Directions in Statistical Computing conference.     

Statistics of biophysical signal characteristics and state specificity of the human EEG

The arguments are given that local exponents obtained in multifractal analysis by two methods: wavelet transform modulus maxima (WTMM) and multifractal detrended fluctuation analysis (MDFA) allow to separate statistically hearts of healthy people and subjects suffering from reduced left ventricle systolic function (NYHA I–III class). Proposed indices of fractality suggest that a signal of human heart rate is a mixture of two processes: monofractal and multifractal ones.     

A new proof of admissibility of tests in the multivariate analysis of variance

A new proof of admissibility of tests in MANOVA is given using Stein's theorem [7]. The convexity condition of Stein's theorem is proved directly by means of majorization rather than by the supporting hyperplane approach. This makes the geometrical meaning of the admissibility result clearer.     

Other Classes of Minimax Estimators of Variance Covariance Matrix in Multivariate Normal Distribution

It is well known that the best equivariant estimator of the variance covariance matrix of the multivariate normal distribution with respect to the full affine group of transformation is not even minimax. Some minimax estimators have been proposed. Here we treat this problem in the framework of a multivariate analysis of variance (MANOVA) model and give other classes of minimax estimators.     

Asymptotic expansions for the distributions of latent roots ofS h S e −1 and of certain test statistics in MANOVA

S _e andS _n are independent central and noncentral Wishart matrices having Wishart distributionsW _p (n _e , Σ) andW _p (n _h , Σ; Ω) respectively. Asymptotic expansions are given for the distributions of latent roots ofS _h S _e ⁻¹ and of certain test statistics in MANOVA under the assumption thatn=n _e +n _h becomes large with a fixed ration _e ∶n _h =e∶h(e+h=1,e>0,h>0) andΩ=O(n).     

Tests for dimensionality and interactions of mean vectors under general and reducible covariance structures

Likelihood ratio tests are derived for testing the structure of mean values in a two-way classification. The most general hypothesis considered is when the mean values are subject to row and column effects and interaction has a given complexity. The observations corresponding to a row or a column classification are assumed to have an unknown dispersion (variance covariance) matrix. Two types of dispersion matrices are considered, one with a general and another with a reducible structure. Some special cases are considered. The results of the paper provide generalizations of tests on dimensionality and interactions in a two-way array of mean values considered by Fisher, Anderson, Fujikoshi, Mandel, and Rao.     

The likelihood ratio tests for the dimensionality of regression coefficients

In this paper we give a unified derivation of the likelihood ratio (LR) statistics for testing the hypothesis on the dimensionality of regression coefficients under a usual MANOVA model. We also derive the LR statistics under a general MANOVA model and study their asymptotic null and nonnull distributions. Further it is shown that the test statistic used by Bartlett [4] for testing the hypothesis that the last p?k canonical correlations are all zero is the LR statistic.     

Dimensionality in Manova Tested by a Closed Testing Procedure

The decision on dimensionality of the space spanned by general linear functions of the parameter matrix of a MANOVA model is considered. This problem is related to the investigation, whether graphically or analytically, of significant empirical departures from the overall null hypothesis on these functions. A closed testing procedure for a sequence of relevant hypotheses is proposed. Unlike the classical procedures based on asymptotic distributions of the likelihood ratio statistics, the proposed method ensures that the Type I familywise error rate does not exceed the nominalα-level. Also, it is consistent with testing the overall null hypothesis, while relying on tests of subsequent linear hypotheses implied by the former. Examples are given to compare the proposed procedure with a classical one.     

序约束下多元正态均值的检验问题

设有k组均值有简单半序约束,协方差阵未知的p维正态分布．Sasabuchi等在2003年研究了均值是否相等的检验问题．考虑到似然比检验统计量的临界点难以获得,以致于它不容易实施,Sasabuchi提出了一个检验方法．称为Sasabuchi检验．Sasabuchi检验的一个不足之处在于,它并不优于经典的MANOVA检验．作者提出了一个新的检验方法,它比Sasabuchi检验有一致优的势,而且形式更为简单．通过模拟发现这个检验方法还优势于MANOVA．最后导出了这个检验统计量的渐近零分布．