936 resultados para Algebraic geometric code


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The new text of the Swiss penal code, which entered into effect at the beginning of 2007, has many incidences on the practice of the psychiatrists realizing expertises in the penal field or engaged in the application of legal measures imposing a treatment. The most notable consequences of this text are, on the one hand, a new definition of the concept of penal irresponsibility which is not necessarily any more related to a psychiatric diagnosis and, on the other hand, a new definition of legal constraints that justice can take to prevent new punishable acts and which appreciably modifies the place of the psychiatrists in the questions binding psychiatric care and social control.

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Report on applying agreed-upon procedures to the City of Protivin’s certification of compliance with Chapter 388.10 of the Code of Iowa

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It is proved the algebraic equality between Jennrich's (1970) asymptotic$X^2$ test for equality of correlation matrices, and a Wald test statisticderived from Neudecker and Wesselman's (1990) expression of theasymptoticvariance matrix of the sample correlation matrix.

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This paper extends multivariate Granger causality to take into account the subspacesalong which Granger causality occurs as well as long run Granger causality. The propertiesof these new notions of Granger causality, along with the requisite restrictions, are derivedand extensively studied for a wide variety of time series processes including linear invertibleprocess and VARMA. Using the proposed extensions, the paper demonstrates that: (i) meanreversion in L2 is an instance of long run Granger non-causality, (ii) cointegration is a specialcase of long run Granger non-causality along a subspace, (iii) controllability is a special caseof Granger causality, and finally (iv) linear rational expectations entail (possibly testable)Granger causality restriction along subspaces.

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The generalization of simple correspondence analysis, for two categorical variables, to multiple correspondence analysis where they may be three or more variables, is not straighforward, both from a mathematical and computational point of view. In this paper we detail the exact computational steps involved in performing a multiple correspondence analysis, including the special aspects of adjusting the principal inertias to correct the percentages of inertia, supplementary points and subset analysis. Furthermore, we give the algorithm for joint correspondence analysis where the cross-tabulations of all unique pairs of variables are analysed jointly. The code in the R language for every step of the computations is given, as well as the results of each computation.