We consider a chaotic system with a double-scroll attractor proposed by Elwakil, composing with a second-order system, which has low-dimensional multiple invariant subspaces and multi-level on-off intermittency. This type of composite system always includes a skew-product structure and some invariant subspaces, which are associated with different levels of laminar phase. In order for the level of laminar phase be adjustable, we adopt a nonlinear function with saturation characteristic to tune the...

Planteamos el modelo lineal sobre variables dummy difusas asociadas a variables cuantitativas codificadas o cualitativas. En el modelo bicriterio aparecen las mismas dificultades conceptuales que se presentan en el caso multicriterio, pero su desarrollo resulta mucho más ágil y transparente. Resolvemos las ecuaciones normales, caracterizando el conjunto de soluciones y encontramos finalmente las funciones paramétricas estimables para este modelo.

Continuando el desarrollo del modelo lineal sobre dos variables dummy difusas, encontramos expresiones para las descomposiciones clásicas en sumas de cuadrados y estudiamos sus distribuciones bajo hipótesis de normalidad. Damos una interpretación geométrica del ajuste cuando las variables numéricas explicativas se ven sometidas a una codificación difusa de tipo semilineal y contrastamos en estos casos la mejora de este nuevo modelo respecto de la regresión sobre las variables originales. Concluimos...

By using three theorems (Oktaba and Kieloch [3]) and Theorem 2.2 (Srivastava and Khatri [4]) three results are given in formulas (2.1), (2.8) and (2.11). They present asymptotically normal confidence intervals for the determinant $|{\sigma }^2\sum |$ in the MGM model $(U,XB,{\sigma }^2\sum \otimes V)$, $\sum >0$, scalar ${\sigma }^2>0$, with a matrix $V\ge 0$. A known $n\times p$ random matrix $U$ has the expected value $E\left(U\right)=XB$, where the $n\times d$ matrix $X$ is a known matrix of an experimental design, $B$ is an unknown $d\times p$ matrix of parameters and ${\sigma }^2\sum \otimes V$ is the covariance matrix of $U,\otimes$ being the symbol of the Kronecker...

Linear relations, containing measurement errors in input and output data, are taken into account in this paper. Parameters of these so-called errors-in-variables (EIV) models can be estimated by minimizing the total least squares (TLS) of the input-output disturbances. Such an estimate is highly non-linear. Moreover in some realistic situations, the errors cannot be considered as independent by nature. Weakly dependent ($\alpha$- and $\varphi$-mixing) disturbances, which are not necessarily stationary nor identically...