Parametric nonlinear mixed effects models (NLMEs) are now widely used in biometrical studies, especially in pharmacokinetics research and HIV dynamics models, due to, among other aspects, the computational advances achieved during the last years. However, this kind of models may not be flexible enough for complex longitudinal data analysis. Semiparametric NLMEs (SNMMs) have been proposed by Ke ...

This paper presents application of parametric techniques (Estimation of signal parameter by rotational invariance techniques (ESPRIT), Root multiple signal classification (Root MUSIC)) for the estimation of harmonics/inter harmonics produced by wind generator under constant, variable rotor speed, load imbalance. Accuracy of estimation by parametric techniques is checked on synthetic signal as w...

‎consider an estimation problem in a one-parameter non-regular distribution when both endpoints of the support depend on a single parameter‎. ‎in this paper‎, ‎we give sufficient conditions for a generalized bayes estimator of a parametric function to be admissible‎. ‎some examples are given‎. ‎

The purpose of this chapter is to present the design, analysis, and simulation of a wide class of algorithms that can be used for online parameter identification of continuous-time plants. The online identification procedure involves the following three steps. Step 1. Lump the unknown parameters in a vector θ∗ and express them in the form of the parametric model SPM, DPM, B-SPM, or B-DPM. Step ...

We define strong regularity of a parametric interval matrix and give conditions that characterize it. The new conditions give a better estimation for regularity of a parametric matrix than the conditions used so far. Verifiable sufficient regularity conditions are also presented for parametric matrices. The new sufficient conditions motivate a generalization of Rump’s parametric fixed-point ite...

A DEA-based stochastic estimation framework is presented to evaluate contextual variables affecting productivity. Conditions are identified under which a two-stage procedure consisting of DEA followed by regression analysis yields consistent estimators of the impact of contextual variables. Conditions are also identified under which DEA in the first stage followed by maximum likelihood estimati...

In this paper, we propose a new algorithm for non-parametric estimation of hidden Markov models (HMM's). The algorithm is based on a \wavelet-shrinkage" density estimator for the state-conditional probability density functions of the HMM's. It operates in an iterative fashion, similar to the EM re-estimation formulae used for maximum likelihood estimation of parametric HMM's. We apply the resul...

A semi parametric profil~ likelihood method is proposed for estimation of sample selection models. The method is a two step scoring semi parametric estimation procedure based on index formulation and kernel density estimation. Under some regularity conditions, the estimator is asymptotically normal. This method can be applied to estimation of general sample selection models with multiple regime...

This paper proposes a semi-parametric software reliability model (SRM) based on a mixed gamma distribution, so-called the mixed gamma SRM. In addition, we develop the parameter estimation method for the mixed gamma SRM. Concretely, the estimation method is based on the Bayesian estimation and the parameter estimation algorithm is described by MCMC (Markov chain Monte Carlo) method with grouped ...