Analysis techniques for multivariate root loci

Analysis and techniques are developed for the multivariable root locus and the multivariable optimal root locus. The generalized eigenvalue problem is used to compute angles and sensitivities for both types of loci, and an algorithm is presented that determines the asymptotic properties of the optimal root locus

Data analysis techniques

A large and diverse number of computational techniques are routinely used to process and analyze remotely sensed data. These techniques include: univariate statistics; multivariate statistics; principal component analysis; pattern recognition and classification; other multivariate techniques; geometric correction; registration and resampling; radiometric correction; enhancement; restoration; Fourier analysis; and filtering. Each of these techniques will be considered, in order

Using the Multivariate Data Analysis Techniques on the Insurance Market

In the present financial theory, we confront with complex economic phenomena and activities which cannot be studied or analyzed profoundly because of the plurality of existing variables, ratios and information. The economic, financial and social activity carried on under crisis or economic growth conditions registered year by year a development of the products and instruments in use. The complexity of the economic area may be simplified through techniques of multi-dimensional analysis. Such a method is the analysis of the principal components which allows the decreasing of the initial causal space dimension generated by the functional links which are established among the initial explanatory variables. The dimension of this space is determined by the number of explanatory variables identified as causes of the economic phenomenon and the higher their number, the more difficult it is to analyze the initial causal space because the information volume, the complexity of calculations, the risk not to identify the contribution of each variable to the creation of the initial causal space variability and the decrease in the initial variables significance in case they would be inter-correlated grow. The simplification of the initial causal space means the determination of a change which consists in transition from a space with a large number of variables to another one of fewer dimensions, equivalent but on the conditions of keeping maximum information from the initial space and maximizing the variability of the new space (called principal space). Variables from the principal space represent the principal components, they are un-correlated and the vectors which define them have a unitary length.original variables, covariance matrix, eigenvalue, eigenvector, principal components, total variance, generalized variance, factor matrix, factor loadings, factor scores, classification

Multinomial selection index

Comparison of multivariate statistical analysis techniques for multinomial selection indice

Inferring causal relations from multivariate time series : a fast method for large-scale gene expression data

Various multivariate time series analysis techniques have been developed with the aim of inferring causal relations between time series. Previously, these techniques have proved their effectiveness on economic and neurophysiological data, which normally consist of hundreds of samples. However, in their applications to gene regulatory inference, the small sample size of gene expression time series poses an obstacle. In this paper, we describe some of the most commonly used multivariate inference techniques and show the potential challenge related to gene expression analysis. In response, we propose a directed partial correlation (DPC) algorithm as an efficient and effective solution to causal/regulatory relations inference on small sample gene expression data. Comparative evaluations on the existing techniques and the proposed method are presented. To draw reliable conclusions, a comprehensive benchmarking on data sets of various setups is essential. Three experiments are designed to assess these methods in a coherent manner. Detailed analysis of experimental results not only reveals good accuracy of the proposed DPC method in large-scale prediction, but also gives much insight into all methods under evaluation

Multivariate Analysis, Retrieval, and Storage System (MARS). Volume 1: MARS System and Analysis Techniques

A method for rapidly examining the probable applicability of weight estimating formulae to a specific aerospace vehicle design is presented. The Multivariate Analysis Retrieval and Storage System (MARS) is comprised of three computer programs which sequentially operate on the weight and geometry characteristics of past aerospace vehicles designs. Weight and geometric characteristics are stored in a set of data bases which are fully computerized. Additional data bases are readily added to the MARS system and/or the existing data bases may be easily expanded to include additional vehicles or vehicle characteristics