Based on the analysis of the reflected attributes of the evaluation indexes of science and technology journals from the perspective of information transmission,the evaluation indexes are classified into the following four types:influence index,document index,medium index and management index.Seven principles of index selection are proposed on the basis of an analysis of the evaluation purpose,method and demand,an analysis of the evaluation index relevance and dimensional factors,and a study of the development trend of evaluation index selection.Those seven principles are:fitting the evaluation purpose,considering the differences of the evaluation demand,considering the influence of the index relevance,considering the dimensional factors of index,considering the features of the evaluated journals,the convenience and feasibility of obtaining normal indexes and considering the development trend of evaluation index selection.
We propose a nonparametric method to explicitly model and represent the derivatives of smooth underlying trajectories for longitudinal data. This representation is based on a direct Karhunen--Lo\`eve expansion of the unobserved derivatives and leads to the notion of derivative principal component analysis, which complements functional principal component analysis, one of the most popular tools of functional data analysis. The proposed derivative principal component scores can be obtained for irregularly spaced and sparsely observed longitudinal data, as typically encountered in biomedical studies, as well as for functional data which are densely measured. Novel consistency results and asymptotic convergence rates for the proposed estimates of the derivative principal component scores and other components of the model are derived under a unified scheme for sparse or dense observations and mild conditions. We compare the proposed representations for derivatives with alternative approaches in simulation settings and also in a wallaby growth curve application. It emerges that representations using the proposed derivative principal component analysis recover the underlying derivatives more accurately compared to principal component analysis-based approaches especially in settings where the functional data are represented with only a very small number of components or are densely sampled. In a second wheat spectra classification example, derivative principal component scores were found to be more predictive for the protein content of wheat than the conventional functional principal component scores.
An evaluation method on sci-tech journals based on principal component analysis is proposed to deal with the relevance between various indices and the selection of index weight.According to this evaluation method,the evaluation index is turned into mutually independent principal components on the basis of linear transformation through the eigenvector of correlation coefficient matrix.The dimension selection of the principal components is determined according to the accumulated contributing value of the principal components and the weight is determined by the variance of the principal components.When this method is employed to evaluate the sci-tech journals,the deviation brought about by the relevance between various indices can be eliminated and the calculating dimension can be lowered,hence making the selection of index easier and improving the credibility of the evaluation results.In addition,the problems arising from subjective determination of index weight can be solved,and the evaluation results tend to be more objective and accurate.
We propose a nonparametric method to explicitly model and represent the derivatives of smooth underlying trajectories for longitudinal data. This representation is based on a direct Karhunen--Lo\`eve expansion of the unobserved derivatives and leads to the notion of derivative principal component analysis, which complements functional principal component analysis, one of the most popular tools of functional data analysis. The proposed derivative principal component scores can be obtained for irregularly spaced and sparsely observed longitudinal data, as typically encountered in biomedical studies, as well as for functional data which are densely measured. Novel consistency results and asymptotic convergence rates for the proposed estimates of the derivative principal component scores and other components of the model are derived under a unified scheme for sparse or dense observations and mild conditions. We compare the proposed representations for derivatives with alternative approaches in simulation settings and also in a wallaby growth curve application. It emerges that representations using the proposed derivative principal component analysis recover the underlying derivatives more accurately compared to principal component analysis-based approaches especially in settings where the functional data are represented with only a very small number of components or are densely sampled. In a second wheat spectra classification example, derivative principal component scores were found to be more predictive for the protein content of wheat than the conventional functional principal component scores.
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