Who approves fraudulence? Configurational causes of consumers' unethical judgments

Corrupt behavior presents major challenges for organizations in a wide range of settings. This article embraces a complexity theoretical perspective to elucidate the causal patterns of factors underlying consumers’ unethical judgments. This study examines how causal conditions of four distinct domains combine into configurational causes of unethical judgments of two frequent forms of corrupt consumer behavior: shoplifting and fare dodging. The findings of fuzzy-set Qualitative Comparative Analyses indicate alternative, consistently sufficient ‘‘recipes’’ for the outcomes of interest. This study extends prior work on the topic by offering new insights into the interplay and the interconnected structures of multiple causal factors and by describing configurational causes of consumers’ ethical evaluations of corrupt behaviors. This knowledge may support practitioners and policy makers to develop education and control approaches to thwart corrupt consumer behaviors

Regression and asymptotical location of a multivariate sample

A broad class of multidimensional probability distributions is shown to have large samples which can be almost surely encompassed by a sequence of deterministic close fitting surfaces. Based on polar regression, a general method is proposed to estimate these surfaces.Almost surely stable extreme value Asymptotical location Elliptically contoured distribution Isobar Multivariate distribution Regression estimate

Regression and edge estimation

This short paper points out the fact that, for a large class of multidimensional probability distributions with bounded support, every estimate of the regression can be modified in order to give an estimate of the edge of the support.Edge estimate Multivariate distribution Regression estimate Support

Weak convergence in Lp(0,1) of the uniform empirical process under dependence

The weak convergence of the empirical process of strong mixing or associated random variables is studied in LP(0,1). We find minimal rates of convergence to zero of the mixing coefficients or the covariances, in either case, supposing stationarity of the underlying variables. The rates obtained improve, for p not too large, the corresponding results in the classical D(0,1) framework.Association Empirical process Strong mixing Tightness

An L"2[0,1] invariance principle for LPQD random variables

Using an explicit isometry between Hilbert spaces and an embedding of the space of signed measures we prove an invariance principle with weak convergence in L"2[0,1] for random variables which are linearly positive quadrant dependent under type condition and some regularity on the covariance structureAvailable from Departamento de Matematica, Universidade de Coimbra, 3000 Coimbra, Portugal / FCT - Fundação para o Ciência e a TecnologiaSIGLEPTPortuga

An invariance principle in L"2(0,1) for non stationary ?-mixing sequences

Invariance principle in l"2(0,1) is studied using signed random measures. This approach to the problem uses an explicit isometry between L"2(0,1) and a reproducing kernel Hilbert space giving a very convenient setting for the study of compactness and convergence of the sequence of Donsker functions. As an application, we prove a L"2(0,1) version of the invariance in the case of ?-mixing random variables. Our result is not available in the D(0,1)-settingAvailable from Departamento de Matematica, Universidade de Coimbra, 3000 Coimbra, Portugal / FCT - Fundação para o Ciência e a TecnologiaSIGLEPTPortuga