A Statistical Approach for Multilingual Document Clustering and Topic Extraction from Clusters

2000 Mathematics Subject Classification: 62H30This paper describes a statistics-based methodology for document unsupervised clustering and cluster topics extraction. For this purpose, multiword lexical units (MWUs) of any length are automatically extracted from corpora using the LiPXtractor extractor - a language independent statistics-based tool. The MWUs are taken as base-features to describe documents. These features are transformed and a document similarity matrix is constructed. From this matrix, a reduced set of features is selected using an approach based on Principal Component Analysis. Then, using the Model Based Clustering Analysis software, it is possible to obtain the best number of clusters. Precision and Recall for document-cluster assignment range above 90%. Most important MWUs are extracted from each cluster and taken as document cluster topics. Results on new document classification will just be mentioned

Micromechanisms of compressive failure of fibre reinforced polymers

Fibre reinforced polymers benefit from high flexural strength, corrosion resistance and low density. These qualities make them a candidate to substitute the conventional rigid steel pipelines for subsea transport of oil and gas. However, deep water pipelines are subject to high external hydrostatic compressive stresses alongside variable internal fluid pressure that can result in high compressive hoop, radial and axial stress. For aligned fibre reinforced composites, compressive strength is generally lower than the tensile strength and a design limiting factor. Therefore, failure mechanisms and conditions need to be well understood in order to design safe and cost-effective structures. Please click Additional Files below to see the full abstract

F-tests for generalized linear hypotheses in subnormal models

When the measurement errors may be assumed to be normal and independent from what is measured a subnormal model may be used. We define a linear and generalized linear hypotheses for these models, and derive F-tests for them. These tests are shown to be UMP for linear hypotheses as well as strictly unbiased and strongly consistent for these hypotheses. It is also shown that the F-tests are invariant for regular transformations, possess structural stability and are almost strongly consistent for generalized linear hypothesis. An application to a mixed model studied by Michalskyi and Zmyślony is shown

Likelihood and parametric heteroscedasticity in normal connected linear models

A linear model in which the mean vector and covariance matrix depend on the same parameters is connected. Limit results for these models are presented. The characteristic function of the gradient of the score is obtained for normal connected models, thus, enabling the study of maximum likelihood estimators. A special case with diagonal covariance matrix is studied

Heterocedasticidade Controlada, Espacos Vectoriais,Quociente e Testes F para Hipoteses sobre Vectores Medios

The Vector of observation is assumed to be N(#mu#, #sigma#"2C) with C known and regular to that there is controlled heterocedasticity. Given subspaces #omega# and #OMEGA# of R"n let a [a"n]#omega# and [b"n]#omega# be the congruence classes for the corresponding congruence relations. Then F tests are obtained for hypothesis H_o:#mu#(element of) [b"n]#omega# assuming either the restrictions #mu#"n(element of) [a"n]#omega# or that #UPSILON#"n is independent from S, that is the product by #sigma#"2 of a central chi-square with g degree of freedom. In the first [second] case we have F tests with [without] restrictions. The properties of both these classes of F testes are studied and the consequences of the violation of the restrictions or of having, in S, a non-central chisquare are considered A tests for the restrictions is also presented and it's behavior is studied using numerical methods. a special treatment is given for these case where #UPSILON#"n ... the sub-vectors being independent. This case occurs, for instance, in multi-treatment regression designs the sub-vectors being the estimates of the coefficients of the regressions that correspond to the treatmentsAvailable from Fundacao para a Ciencia e a Tecnologia, Servico de Informacao e Documentacao, Av. D. Carlos I, 126, 1200 Lisboa / FCT - Fundação para o Ciência e a TecnologiaSIGLEPTPortuga

Exact distribution for the generalized F tests

Generalized F statistics are the quotients of convex combinations of central chi-squares divided by their degrees of freedom. Exact expressions are obtained for the distribution of these statistics when the degrees of freedom either in the numerator or in the denominator are even. An example is given to show how these expressions may be used to check the accuracy of Monte-Carlo methods in tabling these distributions. Moreover, when carrying out adaptative tests, these expressions enable us to estimate the p-values whenever they are available

COMMUTATIVE ORTHOGONAL BLOCK STRUCTURE AND ERROR ORTHOGONAL MODELS

A model has orthogonal block structure, OBS, if it has variance-covariance matrix that is a linear combination of known pairwise orthogonal orthogonal projection matrices that sum to the identity matrix. These models were introduced by Nelder is 1965, and continue to play an important part in randomized block designs. Two important types of OBS are related, and necessary and sufficient conditions for model of one type belonging to the other are determined. The first type, is that of models with commutative orthogonal block structure in which T, the orthogonal projection matrix on the space spanned by the mean vector, commutes with the orthogonal projection matrices in the expression of the variance-covariance matrix. The second type, is that of error orthogonal models. These results open the possibility of deepening the study of the important class of models wit

Inference in nonorthogonal mixed models

Conferência realizada em Rhodes, Grécia de 23-28 de setembro de 2019.info:eu-repo/semantics/publishedVersio