Convergence rates for Markov chain order estimates using EDC criterion

The Efficient Determination Criterion (EDC) generalizes the AIC and BIC criteria and provides a class of consistent estimators for the order of a Markov chain with finite state space. In this note, we derive rates of convergence for the EDC estimates. *Partially supported by CNPq, CAPES/PROCAD, FAPDF/PRONEX, FINATEC and FUNPE/UnB. **Partially supported by CAPES.     

Nonparametric Density Estimation in Hidden Markov Models

Let { X n} be a Markov chain that is either f -mixing or satisfies the Poisson equation.In this note we obtain the convergence rate under L 1 -criterion for bounded functions of the X k 's. And in the hidden Markov model setup { (X n ,Y n ) }we study the kernel estimate of the density of the observed variables { Y n }when a 'stable' status is reached.     

Asymptotic Distribution of Extremes of Randomly Indexed Random Variables

Let {X _n} be a sequence of i.i.d. random variables and let {_k} be a sequence of random indexes. We study the problem of the existence of non-degenerated asymptotic distribution for min{X ₁,..., X _n}.     

Partial pole assignment using rank-one control and receptance in second-order systems with time delay

In this note, a partial pole assignment approach is presented for second-order systems with time delay. The method uses the versatile system receptance for designing state-feedback, rank-one controllers for second-order systems with time delay in the measurements or actuation. The stability of the closed-loop system is pursued throughout an optimization problem formulated with basis on the classical frequency domain technique known as the Nyquist stability criterion. Besides the partial pole assignment, robustness measured in terms of phase and gain margins can be achieved using a genetic algorithm to solve the optimization problem. The proposed approach is shown to provide effective solutions for systems with different time delays in the measurements of displacements and velocities, and with singular mass matrix. Numerical examples are presented to illustrate the benefits of the approach.

     

Effort associated with a class of random optimization methods

When differentiability is not assumed random procedures can be successfully used to estimate the extreme values of a given function. For a class of such algorithms we treat the problem of estimating the mean effort.Research partially supported by CNPq-Brazil.     

Kernel estimation for stationary density of Markov chains with general state space

Let {X _n } _n ≥0 be a Markov chain with stationary distributionf(x)ν(dx), ν being a σ-finite measure onE⊂R ^d . Under strict stationarity and mixing conditions we obtain the consistency and asymptotic normality for a general class of kernel estimates off(·). When the assumption of stationarity is dropped these results are extended to geometrically ergodic chains. Partially supported by CAPES. Partially supported by CNPq, PROCAD/CAPES, PRONEX/FAPDF and FINATEC/UnB.     

Strong consistency of Kernel density estimates for Markov chains failure rates

For a homogeneous and uniformly ergodic Markov chain, with transition kernel , we analyse some reliability measures and failure rates associated with the transition probabilities. Sufficient conditions for strong consistency are obtained for estimates based on kernel density estimators.      

Generalized Langevin equation driven by Lévy processes: A probabilistic, numerical and time series based approach

Lévy processes have been widely used to model a large variety of stochastic processes under anomalous diffusion. In this note we show that Lévy processes play an important role in the study of the Generalized Langevin Equation (GLE). The solution to the GLE is proposed using stochastic integration in the sense of convergence in probability. Properties of the solution processes are obtained and numerical methods for stochastic integration are developed and applied to examples. Time series methods are applied to obtain estimation formulas for parameters related to the solution process. A Monte Carlo simulation study shows the estimation of the memory function parameter. We also estimate the stability index parameter when the noise is a Lévy process.     

Capacity expansion and forward contracting in a duopolistic power sector

The surge in demand for electricity in recent years requires that power companies expand generation capacity sufficiently. Yet, at the same time, energy demand is subject to seasonal variations and peak-hour factors that cause it to be extremely volatile and unpredictable, thereby complicating the decision-making process. We investigate how power companies can optimise their capacity-expansion decisions while facing uncertainty and examine how expansion and forward contracts can be used as suitable tools for hedging against risk under market power. The problem is solved through a mixed-complementarity approach. Scenario-specific numerical results are analysed, and conclusions are drawn on how risk aversion, competition, and uncertainty interact in hedging, generation, and expansion decisions of a power company. We find that forward markets not only provide an effective means of risk hedging but also improve market efficiency with higher power output and lower prices. Power producers with higher levels of risk aversion tend to engage less in capacity expansion with the result that together with the option to sell in forward markets, very risk-averse producers generate at a level that hardly varies with scenarios.