Recursive partitioning is embedded into the general and well-established class of parametric models that can be fitted using M-type estimators (including maximum likelihood). An algorithm for model-based recursive partitioning is suggested for which the basic steps are: (1) fit a parametric model to a data set, (2) test for parameter instability over a set of partitioning variables, (3) if ther...

Bayesian semi-parametric inference is considered for a log-linear model. This model consists of a parametric component for the regression coeecients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coeecients and a mixture-of-Dirichlet-processes prior on the unknown error distribution. A Markov ch...

Abstract: In this paper the design of an observer for Multi-input Multi-output (MIMO) systems using eigenstructure assignment and the employment of this observer for fault diagnosis are investigated. Moreover, the designed observer will be implemented in a model of unmanned aircraft. Furthermore, the state feedback design with eigenstructure assignment has been accomplished on the aircraft. The...

Let P ,X and Y be Banach spaces. Suppose that f : P ×X → Y is continuously Fréchet differentiable function depend on the point (p, x) and F : X ⇒ 2 is a set-valued mapping with closed graph. Consider the following parametric generalized equation of the form: 0 ∈ f(p, x) + F (x). (1) In the present paper, we study an extended Newton-type method for solving parametric generalized equation (1). In...

We conduct a comprehensive study of some new or recently developed parametric methods to estimate loss given default using a common data set. We first propose to use a smearing estimator, a Monte Carlo estimator, and a global adjustment to refine transformation regressions that address loss given default boundary values. Although these refinements only marginally improve model performance, the ...

High dimensional models with parametric dependencies can be challenging to simulate. The computational e↵ort usually increases exponentially with the dimension of the parameter space. To keep the calculations feasible, one can use parametric model order reduction techniques. Multivariate Pad methods match higher order moments of the Laplace variable as well as the parameters. Interpolatory redu...

Consider a linear system A(p) · x = b(p), where the elements of the matrix and the right-hand side vector depend affine-linearly on a m-tuple of parameters p = (p1, . . . , pm) varying within given intervals. It is a fundamental problem how to describe the parametric solution set Σ (A(p), b(p), [p]) := {x ∈ Rn | ∃p ∈ [p], A(p)x = b(p)}. So far, the solution set description can be obtained by a ...

The existing parametric multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model could hardly capture the nonlinearity and the non-normality, which are widely observed in …nancial data. We propose semiparametric conditional covariance (SCC) model to capture the information hidden in the standardized residuals and missed by the parametric MGARCH models. Our two-stage...

We propose parametric regression analysis of cumulative incidence function with competing risks data. A simple form of Gompertz distribution is used for the improper baseline subdistribution of the event of interest. Maximum likelihood inferences on regression parameters and associated cumulative incidence function are developed for parametric models, including a flexible generalized odds rate ...

Given a set of integer vectors defined by linear inequalities over a fixed number of variables, where some of the variables are considered as parameters, we consider two different ways of representing the number of elements in the set in terms of the parameters. The first is an explicit function which generalizes Ehrhart quasi-polynomials. The second is its corresponding generating function and...