The affine equivariant sign covariance matrix: asymptotic behavior and efficiencies.

We consider the affine equivariant sign covariance matrix (SCM) introduced by Visuri et al. (J. Statist. Plann. Inference 91 (2000) 557). The population SCM is shown to be proportional to the inverse of the regular covariance matrix. The eigenvectors and standardized eigenvalues of the covariance, matrix can thus be derived from the SCM. We also construct an estimate of the covariance and correlation matrix based on the SCM. The influence functions and limiting distributions of the SCM and its eigenvectors and eigenvalues are found. Limiting efficiencies are given in multivariate normal and t-distribution cases. The estimates are highly efficient in the multivariate normal case and perform better than estimates based on the sample covariance matrix for heavy-tailed distributions. Simulations confirmed these findings for finite-sample efficiencies. (C) 2003 Elsevier Science (USA). All rights reserved.affine equivariance; covariance and correlation matrices; efficiency; eigenvectors and eigenvalues; influence function; multivariate median; multivariate sign; robustness; multivariate location; discriminant-analysis; principal components; dispersion matrices; tests; estimators;

Influence function and asymptotic efficiency of the affine equivariant rank covariance matrix.

Visuri et al (2001) proposed and illustrated the use of the affine equivariant rank covariance matrix (RCM) in classical multivariate inference problems. The RCM was shown to be asymptotically multinormal but explicit formulas for the limiting variances and covariances were not given yet. In this paper the influence functions and the limiting variances and covariances of the RCM and the corresponding scatter estimate are derived in the multivariate elliptic case. Limiting efficiencies are given in the multivariate normal and t-distribution cases. The estimates based on the RCM are highly efficient in the multinormal case, and for heavy tailed distribution, perform better than those based on the regular covariance matrix.Efficiency;

Asymptotic and bootstrap tests for the dimension of the non-Gaussian subspace

Dimension reduction is often a preliminary step in the analysis of large data sets. The so-called non-Gaussian component analysis searches for a projection onto the non-Gaussian part of the data, and it is then important to know the correct dimension of the non-Gaussian signal subspace. In this paper we develop asymptotic as well as bootstrap tests for the dimension based on the popular fourth order blind identification (FOBI) method

Tools for Exploring Multivariate Data: The Package ICS

Invariant coordinate selection (ICS) has recently been introduced as a method for exploring multivariate data. It includes as a special case a method for recovering the unmixing matrix in independent components analysis (ICA). It also serves as a basis for classes of multivariate nonparametric tests, and as a tool in cluster analysis or blind discrimination. The aim of this paper is to briefly explain the (ICS) method and to illustrate how various applications can be implemented using the R package ICS. Several examples are used to show how the ICS method and ICS package can be used in analyzing a multivariate data set.

A Survey of Moral Concerns and Job-Related Stress among Academic Advisors of College Athletes

The author surveyed 186 athletic advisors/counselors regarding moral concerns and job-related stress in their jobs with a mailed questionnaire. Within responses, 68.3% noted they occasionally or frequently worry their decisions are based on athlete eligibility over concern for athlete long-term wellbeing, reflecting pressure advisors are under; 82.1% responded occasionally or frequently the advisors are under an uncomfortable amount of stress in their roles; 97.3% of respondents felt they occasionally or frequently made decisions with an ethical or moral consideration

Independent component analysis: algorithms and applications

A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of nongaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation. In this paper, we present the basic theory and applications of ICA, and our recent work on the subject

Wild cards, weak signals and prganizational improvisation

This paper addresses the need for reliable action guidelines that can be used by organisations in turbulent environments. Building on current conceptual and empirical research, we suggest an analytical approach for the management of surprising and potentially damaging events. In order to do so we use the wild card management system. Wild cards refer to sudden and unique incidents that can constitute turning points in the evolution of a certain trend or system. As the first of the two components of such a wild card system we advocate a weak signal methodology to take into account those wild cards that can be anticipated by scanning the decision environment. The second component, the nurture of improvisation capabilities, is designed to deal with ongoing crisis. This paper can be seen as part of a broader agenda on how to manage in conditions of continuous but unpredictable change.wild cards, weak signals, improvisation, minimal structures

A geometric Newton method for Oja's vector field

Newton's method for solving the matrix equation $F(X)\equiv AX-XX^TAX=0$ runs up against the fact that its zeros are not isolated. This is due to a symmetry of $F$ by the action of the orthogonal group. We show how differential-geometric techniques can be exploited to remove this symmetry and obtain a ``geometric'' Newton algorithm that finds the zeros of $F$. The geometric Newton method does not suffer from the degeneracy issue that stands in the way of the original Newton method

Fungsi Pengawas Bidang SMP Dalam Melakukan Monitoring Terhadap Kualitas Pendidikan

Pengawasan yang berkualitas adalah pengawasan yang dilakukan pada semua sekolah dan membantuguru dan sekolah dalam hal peningkatan sistem belajar mengajar maupun pengelolaan sekolah. Penelitianini bertujuan untuk mendeskripsikan kualitas pengawasan pendidikan terhadap Sekolah MenengahPertama di Kabupaten Merauke. Penelitian ini menggunakan metode kualitatif dengan pendekatandeskriptif. Teknik pengumpulan data yaitu observasi, wawancara, dan dokumentasi. Teknik analisis datayaitu, reduksi data, display data dan verifikasi data. Hasil penelitian menemukan Pelaksanaanpengawasan yang dilakukan oleh Dinas Pendidikan dan Kebudayaan Kabupaten Merauke belummaksimal. Hal ini disebabkan karena keterbatasan sumber daya manusia yakni pengawas sekolah yangtersedia di dinas tidak seimbang dengan jumlah sekolah yang ada sehingga berpengaruh terhadapjangkauan pengawas ke sekolah binaan menjadi berkurang dan tidak merata. Kunjungan pengawas lebihintens dilakukan pada sekolah yang ada di kota sedangkan sekolah yang berada di pinggiran dan pelosokkunjungan hanya dilakukan tiga bulan sekali bahkan terdapat sekolah yang sangat jarang dilakukanpengawasan. Padahal dengan kunjungan pengawas yang teratur dapat membantu sekolah dalam melihatkekurangan sekolah untuk segera diperbaiki. Hal lain yang membuat pelaksanaan pengawasan belummaksimal adalah karena kekurangan dana. Untuk melakukan pengawasan pada sekolah yang jauh,pengawas kadang menggunakan uang pribadi karena dana supervisi yang diberikan pemerintah tidakmencukupi. Selain itu, kondisi geografis yang menantang juga membuat pengawas sulit mengunjungisekolah.Pengawasan yang berkualitas adalah pengawasan yang dilakukan pada semua sekolah dan membantuguru dan sekolah dalam hal peningkatan sistem belajar mengajar maupun pengelolaan sekolah. Penelitianini bertujuan untuk mendeskripsikan kualitas pengawasan pendidikan terhadap Sekolah MenengahPertama di Kabupaten Merauke. Penelitian ini menggunakan metode kualitatif dengan pendekatandeskriptif. Teknik pengumpulan data yaitu observasi, wawancara, dan dokumentasi. Teknik analisis datayaitu, reduksi data, display data dan verifikasi data. Hasil penelitian menemukan Pelaksanaanpengawasan yang dilakukan oleh Dinas Pendidikan dan Kebudayaan Kabupaten Merauke belummaksimal. Hal ini disebabkan karena keterbatasan sumber daya manusia yakni pengawas sekolah yangtersedia di dinas tidak seimbang dengan jumlah sekolah yang ada sehingga berpengaruh terhadapjangkauan pengawas ke sekolah binaan menjadi berkurang dan tidak merata. Kunjungan pengawas lebihintens dilakukan pada sekolah yang ada di kota sedangkan sekolah yang berada di pinggiran dan pelosokkunjungan hanya dilakukan tiga bulan sekali bahkan terdapat sekolah yang sangat jarang dilakukanpengawasan. Padahal dengan kunjungan pengawas yang teratur dapat membantu sekolah dalam melihatkekurangan sekolah untuk segera diperbaiki. Hal lain yang membuat pelaksanaan pengawasan belummaksimal adalah karena kekurangan dana. Untuk melakukan pengawasan pada sekolah yang jauh,pengawas kadang menggunakan uang pribadi karena dana supervisi yang diberikan pemerintah tidakmencukupi. Selain itu, kondisi geografis yang menantang juga membuat pengawas sulit mengunjungisekolah