Shrinkage estimation usually reduces variance at the cost of bias. But when we care only about some parameters of a model, I show that we can reduce variance without incurring bias if we have additional information about the distribution of covariates. In a linear regression model with homoscedastic Normal noise, I consider shrinkage estimation of the nuisance parameters associated with control...

This paper proposes a new pathwise sensitivity estimator for chaotic SDEs. By introducing spring term between the original and perturbated SDEs, we derive by importance sampling. The variance of increases only linearly in time $T,$ compared with exponential increase standard estimator. We compare our Malliavin extend both them to Multilevel Monte Carlo method, which further improves computation...

We address the problem of state estimation, attack isolation, and control discrete-time linear time-invariant systems under (potentially unbounded) actuator sensor false data injection attacks. Using a bank unknown input observers, each observer leading to an exponentially stable estimation error (in attack-free case), we propose observer-based estimator that provides exponential estimates syst...

This paper discusses the approximate and feedback relevant parametric identi cation of the radial servo system present in a Compact Disc player. In this application the problem of approximate identi cation based on data from closed loop experiments will be analyzed to nd a nite dimensional linear time invariant discrete time model, suitable for model-based control design. The feedback relevant ...

In this paper, we show how Floquet theory may be combined with a technique known as Lifting to cast a linear periodically time-varying system subject to structured linear time invariant uncertainty in the form of a linear fractional transformation. The stability and performance robustness of the resulting system may then be analysed using standard μ-analysis methods. A significant advantage of ...

We consider the effect of parametric uncertainty on properties of Linear Time Invariant systems. Traditional approaches to this problem determine the worst-case gains of the system over the uncertainty set. Whilst such approaches are computationally tractable, the upper bound obtained is not necessarily informative in terms of assessing the influence of the parameters on the system performance....

We consider the best quadratic unbiased estimators of the integrated variance in the presence of independent market microstructure noise. We establish the asymptotic normality of a feasible best quadratic unbiased estimator under the assumption of constant volatility and show that it is asymptotically e cient when the market microstructure noise is normal. Since the class of quadratic estimator...

We constraint on computer the best linear unbiased generalized statistics of random field for the best linear unbiased generalized statistics of an unknown constant mean of random field and derive the numerical generalized least-squares estimator of an unknown constant mean of random field. We derive the third constraint of spatial statistics and show that the classic generalized least-squares ...

Image and geometry processing applications estimate the local geometry of objects using information localized at points. They usually consider information about the tangents as a side product of the points coordinates. This work proposes parabolic polygons as a model for discrete curves, which intrinsically combines points and tangents. This model is naturally affine invariant, which makes it p...

let be a random sample from a normal distribution with unknown mean and known variance the usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible estimator. in many practical situations, is known in advance to lie in an interval, say for some in this case, the maximum likelihood estimator changes and d...