Abstract
Introduction: Self-reported height and weight data have been used in several studies with the purpose of determining the prevalence of overweight and obesity. Despite being a simple methodology, little information exists about the reliability of these measures, namely, in university students. The objective of this study was to determine the sensitivity and specificity of self-reported body mass index (BMI) to evaluate the prevalence of overweight and obesity in university students. Methods: In a convenience sample of 380 university students (226 women and 154 men), weight and height were obtained by self-reported measures and anthropometrie assessment according to international standards methodology (objective). BMI was calculated from self-reported and direct measures. Results: The discrepancy between objective and self-reported weight was not significative. For height, this discrepancy was significantly different in women, in men, and between genders. The difference between BMI values was significantly different in women (0,8 ± 1,1 kg/m2), in men (0,4 ± 1,1 kg /m2) and between genders. Concerning overweight and obesity, according to the objective BMI, the sensitivity was only 50% in women, and 70% in men, while the specificity was 99% in women and 98% in men. Conclusion: Our results show a poor sensitivity of self-reported weight and height data, to estimate overweight and obesity, thus, this method might not be reliable for studies of prevalence of obesity in this population.

Abstract
In this paper we provide a different way to estimate matrices B and D, in subspace identification algorithms. The starting point was the method proposed by Van Overschee and De Moor [10] - the only one applying subspace ideas to the estimation of those matrices. We have derived new (and simpler) expressions and we found that the method proposed by Van Overschee and De Moor [10] can be rewritten as a weighted least squares problem, involving the future outputs and inputs.

Abstract
In this paper, two approaches, for the estimation of matrices A and C in CV A-type subspace identification algorithms are compared and the differences between the two obtained estimates are analysed. One of the methods, "least squares" based, was proposed in the original CVA algorithm. The other method, inspired in the techniques of the classical Realization Theory and proposed by Verhaegen, is far more efficient. Therefore, although the two methods produce two different estimates, a replacement is proposed and an expression for correction of the estimates is obtained, in order to reduce the loss of accuracy.

Abstract
In this paper, a recursive algorithm is presented, based on the CVA subspace identification algorithm. The main idea was to explore the relations between the orthogonal and oblique projections involved and to provide simpler expressions that allowed a recursive version of the algorithm - guaranteeing most of the advantages of this kind of methods, and still improving the numerical efficiency.

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.