When an ordering among parameters is known in advance,the problem of estimating the smallest or the largest parametersarises in various practical problems. Suppose independent randomsamples of size ni drawn from two gamma distributions withknown arbitrary shape parameter no_i > 0 and unknown scale parameter beta_i > 0, i = 1, 2. We consider the class of mixed estimators of 1 and 2 under the res...

This paper considers a Hausman and Taylor (1981) panel data model that exhibits a Cliff and Ord (1973) spatial error structure. We analyze the small sample properties of a Generalized Moments estimation approach for that model. This spatial Hausman-Taylor estimator allows for endogeneity of the time-varying and time-invariant variables with the individual effects. For this model, the spatial fi...

This paper is the third part of our study started with Cattiaux, León and Prieur (2014 2013). For some ergodic hamiltonian systems we obtained a central limit theorem for a non-parametric estimator of the invariant density (Cattiaux et al. 2014) and of the drift term (Cattiaux et al. 2013), under partial observation (only the positions are observed). Here we obtain similarly a central limit the...

We establish the equivalence between the optimal least-squares state estimator for a linear time-invariant dynamic system with noise corrupted input and output, and an appropriately modified Kalman filter. The approach used is algebraic and the result shows that the noisy input/output filtering problem is not fundamentally different from the classical Kalman filtering problem. The result is ill...

Consider a first order, linear and time-invariant discrete time system driven by Gaussian and zero mean white process noise, a pre-processor that accepts causal measurements of the state of the system, and a state estimator. The pre-processor and the state estimator are not co-located, and, at every timestep, the pre-processor sends either a real number or an erasure symbol to the estimator. We...

An admissible estimator of the eigenvalues of the variance-covariance matrix is given for multivariate normal distributions with respect to the scale-invariant squared error loss. AMS(2000) Subject Classification: Primary 62C15; Secondary 62F10

While the statistical properties of the CMB anisotropies are a powerful means to discriminate amongst the possible cosmological scenarios, actually measuring non-Gaussianity in the data is a very difficult task1. The typically small signal should be compared to the noise and the key quantity is the signal to noise ratio. The noise creeps into the dataset through instrumental errors, foregrounds...

An admissible estimator of the eigenvalues of the variance-covariance matrix is given for multivariate normal distributions with respect to the scale-invariant squared error loss. AMS(2000) Subject Classification: Primary 62C15; Secondary 62F10