In a simple model composed of a structural equation and identity, the finite sample distribution of the IV/LIML estimator is always bimodal and this is most apparent when the concentration parameter is small. Weak instrumentation is the energy that feeds the secondary mode and the coefficient in the structural identity provides a point of compression in the density that gives rise to it. The IV...

This paper deals with the problem of estimating the Multivariate version of the Conditional-TailExpectation, proposed by Cousin and Di Bernardino (2012). We propose a new non-parametric estimator for this multivariate risk-measure, which is essentially based on the Kendall’s process (see Genest and Rivest, 1993). Using the Central Limit Theorem for the Kendall’s process, proved by Barbe et al. ...

This contribution presents a friction estimator for industrial purposes which identifies Coulomb friction in a steering system. The estimator only needs a few, usually known, steering system parameters. Friction occurs on almost every mechanical system and has a negative influence on high-precision position control. This is demonstrated on a steering angle controller for highly automated drivin...

We discuss the nonparametric Nadaraya-Watson (N-W) estimator of the drift function for ergodic stochastic processes driven by α-stable noises and observed at discrete instants. Under geometrical mixing condition, we derive consistency and rate of convergence of the N-W estimator of the drift function. Furthermore, we obtain a central limit theorem for stable stochastic integrals. The central li...

typical production objectives in distillation process require the delivery of products whose compositions meet certain specifications. the distillation control system, therefore, must hold product compositions as near the set points as possible in faces of upset. in this project, inferential model predictive control, that utilizes an artificial neural network estimator and model predictive cont...

A nonlinear regression model with correlated, normally distributed stationary errors is investigated. Limit properties of an approximate estimator of an unknown covariance function of stationary errors are studied and suucient conditions under which this estimator is consistent are shown.

In this paper we prove theoretically that for mixture models involving known component densities the variational Bayes estimator converges locally to the maximum likelihood estimator at the rate of O(1/n) in the large sample limit.

This paper is the second part of our study started with Cattiaux et al. (2014). For some ergodic hamiltonian systems we obtained a central limit theorem for a non-parametric estimator of the invariant density, under partial observation (only the positions are observed). Here we obtain similarly a central limit theorem for a non-parametric estimator of the drift term. This theorem relies on the ...