This article reports Monte Carlo results on the simulated maximum likelihood estimation of discrete panel statistical models. Among them are Markov, Generalized Poly, Renewal, and Habit Persistence Models with or without unobserved heterogeneity and serially correlated disturbances. We investigate statistical properties and computational performance of simulated maximum likelihood methods and a...

The maximum entropy spectral estimator is widely used because of its high spectral resolution, but it lacks an easy procedure for evaluating the statistical significance of the spectral estimates. We implemented the non-parametric computer intensive permutation test in order to evaluate the statistical significance of the maximum entropy spectral estimates. There is the possibility of choosing ...

Various approaches have been demonstrated for the automatic interpretation of crystallographic data in terms of atomic models. The use of a masked Fourier-based search function has some benefits for this task. The application and optimization of this procedure is discussed in detail. The search function also acquires a statistical significance when used with an appropriate electron-density targ...

Rate-distortion otpimization procedures are very often used in rate allocation problems encountered in source encoders. However , these procedures can be, depending on the number of quantizers considered, very time consuming. This paper describes a sim-pliied approach relying on statistical signal and noise modeling. The strategy presented leads to performances comparable to a classical rate-di...

age-structured stock assessment models. Historically, age estimates for fish have been obtained from hard structures, such as scales or otoliths, by enumerating seasonally deposited opaque or translucent bands in the calcified tissue (Beamish and McFarlane 1987). Such ageing methods are based on the assumption that the periodicity of band deposition is known or can be validated. To date, the on...

Most statistical process control (SPC) methods are not suitable for monitoring nonlinear and statedependent processes. This article introduces the context-based SPC (CSPC) methodology for statedependent data generated by a finite-memory source. The key idea of the CSPC is to monitor the statistical attributes of a process by comparing two context trees at any monitoring period of time. The firs...

An Online Actor Critic Algorithm and a Statistical Decision Procedure for Personalizing Intervention by Huitian Lei Chair: Professor Susan A. Murphy Assistant Professor Ambuj Tewari Increasing technological sophistication and widespread use of smartphones and wearable devices provide opportunities for innovative health interventions. An Adaptive Intervention (AI) personalizes the type, mode and...

In this paper we focus on latent class analysis of a large data set. The data deal with antisocial behaviour which is measured by 24 dichotomous variables. Latent class analysis of such huge data sets is unusual. One of the reasons is that latent class analysis defines models for contingency tables, so, if there is a set of k dichotomous items, then a table of 2 cells is modeled. It will be cle...

S U M M A R Y A new robust magnetotelluric (MT) data processing algorithm is described, involving Siegel estimation on the basis of a repeated median (RM) algorithm for maximum protection against the influence of outliers and large errors. The spectral transformation is performed by means of a fast Fourier transformation followed by segment coherence sorting. To remove outliers and gaps in the ...

Efficient tuberculosis treatment requires the use of good quality medicines. The present study developed a method of dosage by High Performance Liquid Chromatography (HPLC) coupled with the dissolution of a combination of fixed doses of four anti-tuberculous (Isoniazid, Pyrazinamide, Ethambutol Hydrochlorate, Rifampicine). The elaborated protocol was thereafter used to assess the quality of med...