On the Noncentral Distributions of the Second Largest Roots of Three Matrices in Multivariate Analysis

1970 ◽  
Vol 13 (3) ◽  
pp. 299-304 ◽  
Author(s):  
Sabri Al-Ani
Keyword(s):  
Multivariate Analysis ◽  
Covariance Matrix ◽  
Canonical Correlation ◽  
Null Hypothesis ◽  
Covariance Matrices ◽  
Series Form ◽  
In Series ◽  
Limiting Case

The central distribution of the second largest (smallest) root following the Fisher-Girshick-Hsu-Roy distribution under certain null-hypothesis has been derived in series form by Pillai and Al-Ani [6]. In this paper the noncentral distributions of the second largest roots in the MANOVA situation, the canonical correlation, and equality of two covariance matrices are obtained. Further, the distribution of the second largest root of the covariance matrix is obtained as a limiting case. The largest root and its noncentral distributions have been considered already by Pillai and Sugiyama [7] for the situations stated above. However, in the present paper, the joint densities of the largest and the second largest roots are derived in all the above cases from which the distributions of the largest roots can be obtained, although in more elaborate forms.

scholarly journals XXV.—The Generating Function of the Reciprocal of a Determinant

1905 ◽  
Vol 40 (3) ◽  
pp. 615-629
Author(s):  
Thomas Muir
Keyword(s):  
Generating Function ◽  
Specific Character ◽  
Partial Fraction ◽  
Homogeneous Functions ◽  
Series Form ◽  
General Proposition ◽  
In Series ◽  
Image Position ◽  
The Given

(1) This is a subject to which very little study has been directed. The first to enunciate any proposition regarding it was Jacobi; but the solitary result which he reached received no attention from mathematicians,—certainly no fruitful attention,—during seventy years following the publication of it.Jacobi was concerned with a problem regarding the partition of a fraction with composite denominator (u1 − t1) (u2 − t2) … into other fractions whose denominators are factors of the original, where u1, u2, … are linear homogeneous functions of one and the same set of variables. The specific character of the partition was only definable by viewing the given fraction (u1−t1)−1 (u2−t2)−1…as expanded in series form, it being required that each partial fraction should be the aggregate of a certain set of terms in this series. Of course the question of the order of the terms in each factor of the original denominator had to be attended to at the outset, since the expansion for (a1x+b1y+c1z−t)−1 is not the same as for (b1y+c1z+a1x−t)−1. Now one general proposition to which Jacobi was led in the course of this investigation was that the coefficient ofx1−1x2−1x3−1…in the expansion ofy1−1u2−1u3−1…, whereis |a1b2c3…|−1, provided that in energy case the first term of uris that containing xr.

The Effect of Two Rigid Spherical Inclusions on the Stresses in an Infinite Elastic Solid

1966 ◽  
Vol 33 (1) ◽  
pp. 68-74 ◽  
Author(s):  
Joseph F. Shelley ◽  
Yi-Yuan Yu
Keyword(s):  
Stress Field ◽  
Uniaxial Tension ◽  
Elastic Solid ◽  
Displacement Function ◽  
Hydrostatic Tension ◽  
Series Form ◽  
Spherical Inclusions ◽  
Spherical Cavities ◽  
In Series ◽  
The Common

Presented in this paper is a solution in series form for the stresses in an infinite elastic solid which contains two rigid spherical inclusions of the same size. The stress field at infinity is assumed to be either hydrostatic tension or uniaxial tension in the direction of the common axis of the inclusions. The solution is based upon the Papkovich-Boussinesq displacement-function approach and makes use of the spherical dipolar harmonics developed by Sternberg and Sadowsky. The problem is closely related to, but turns out to be much more involved than, the corresponding problem of two spherical cavities solved by these authors.

scholarly journals Extension of the Sophomore’s Dream

2021 ◽  
Vol 29 (1) ◽  
pp. 211-218
Author(s):  
Gábor Román
Keyword(s):  
Convergence Properties ◽  
Series Form ◽  
In Series

Abstract In this article, we are going to look at the convergence properties of the integral ∫ 0 1 ( a x + b ) c x + d d x \int_0^1 {{{\left( {ax + b} \right)}^{cx + d}}dx} , and express it in series form, where a, b, c and d are real parameters.

Trimmed estimators for large dimensional sparse covariance matrices

2018 ◽  
Vol 08 (01) ◽  
pp. 1950003
Author(s):  
Guangren Yang ◽  
Xia Cui
Keyword(s):  
Convergence Rate ◽  
Covariance Matrix ◽  
Covariance Matrices ◽  
Optimal Convergence Rate ◽  
Sample Covariance Matrices ◽  
Optimal Convergence ◽  
Sample Covariance ◽  
Starting Point ◽  
Pairwise Product

In this paper, we will propose two new estimators for sparse covariance matrix. Our starting point is to make the estimator of each element of covariance matrix more robust. More precisely, we will trim the observations for each pairwise product of components of population as a first step. Then we form the sample covariance matrices based on the trimmed data. Finally, we apply the thresholding to the derived sample covariance matrices. These two new estimators will be shown to achieve the optimal convergence rate.

ENCODING STRUCTURAL SIMILARITY BY CROSS-COVARIANCE TENSORS FOR IMAGE CLASSIFICATION

2014 ◽  
Vol 28 (07) ◽  
pp. 1460008
Author(s):  
MARCO SAN BIAGIO ◽  
SAMUELE MARTELLI ◽  
MARCO CROCCO ◽  
MARCO CRISTANI ◽  
VITTORIO MURINO
Keyword(s):  
Computer Vision ◽  
Covariance Matrix ◽  
Image Representation ◽  
Structural Similarity ◽  
Covariance Matrices ◽  
Integral Image ◽  
Computational Burden ◽  
Feature Vectors ◽  
Single Region ◽  
Cross Covariance

In computer vision, an object can be modeled in two main ways: by explicitly measuring its characteristics in terms of feature vectors, and by capturing the relations which link an object with some exemplars, that is, in terms of similarities. In this paper, we propose a new similarity-based descriptor, dubbed structural similarity cross-covariance tensor (SS-CCT), where self-similarities come into play: Here the entity to be measured and the exemplar are regions of the same object, and their similarities are encoded in terms of cross-covariance matrices. These matrices are computed from a set of low-level feature vectors extracted from pairs of regions that cover the entire image. SS-CCT shares some similarities with the widely used covariance matrix descriptor, but extends its power focusing on structural similarities across multiple parts of an image, instead of capturing local similarities in a single region. The effectiveness of SS-CCT is tested on many diverse classification scenarios, considering objects and scenes on widely known benchmarks (Caltech-101, Caltech-256, PASCAL VOC 2007 and SenseCam). In all the cases, the results obtained demonstrate the superiority of our new descriptor against diverse competitors. Furthermore, we also reported an analysis on the reduced computational burden achieved by using and efficient implementation that takes advantage from the integral image representation.

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