Abstract
In this paper, a new approach to estimate matrices B and D, in subspace methods, is provided. The starting point was one method proposed by Van Overschee and De Moor (1996). We have derived new (and simpler) expressions and we found that the original method can be rewritten as a weighted least squares problem, involving the future outputs and inputs and the observability matrix.

Abstract
In this paper we present two subspace identification methods implemented through sequences of modified Householder algorithm. The main idea was to show that subspace identification methods can be represented as sequences of least squares problems and implemented wing QR factorizations. Therefore, it is possible to develop iterative algorithms with most of the advantages of this kind of methods, and still improve the numerical efficiency, in order to deal with real-tme applications and minimize the computational burden.

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Abstract

Abstract
Collecting and mining clickstream data from c-commerce sites has become increasingly important for marketing, advertising, and traffic analysis activities. Organizations are promoting many initiatives concerning user's navigation pattern discovery, in order to implement better sites, more functional and close to customers' needs. Basically, the main idea is to provide more quality of attendance in their sites, and, consequently, get more profitability. However, clickstream processing is not a simple task. The sequences of clicks are very difficult to handle using conventional techniques, essentially due to their diversity and nature. They include a lot of aspects that reveal the multidimensional perspective of web data. OLAP technology provides today the means and techniques to represent, store and analyse such kinds of multidimensional data. However, it does not offer discovery driven analysis to support traversal pattern identification processes on web sites. Mining traversal pattern techniques can be applied in conjunction with OLAP as an integrated alternative for understanding those particular sequences of clicks. In this paper we present an integrated OLAP and mining approach specially conceived for exploring user navigation patterns based on clickstreams. We also describe the multidimensional structure provided for modelling click sequences and the OLAP operations and mining techniques that can be pushed over data cubes to bring up navigation patterns.

Abstract
Recently, as a result of the growing interest in modelling stationary processes with discrete marginal distributions, several models for integer value time series have been proposed in the literature. One of these models is the INteger-AutoRegressive (INAR) model. Here we consider the higher-order moments and cumulants of the INAR(1) process and show that they satisfy a set of Yule-Walker type difference equations. We also obtain the spectral and bispectral density functions, thus characterizing the INAR(1) process in the frequency domain. We use a frequency domain approach, namely the Whittle criterion, to estimate the parameters of the model. The estimation theory and associated asymptotic theory of this estimation method are illustrated numerically.