Let X1 and X2 be two independent random variables from gamma populations Pi1,P2 with means alphaθ1 and alphaθ2 respectively, where alpha(> 0) is the common known shape parameter and θ1 and θ2 are scale parameters. Let X(1) ≤ X(2) denote the order statistics ofX1 and X2. Suppose that the population corresponding to the largest X(2) (or the smallest X(1)) observation is selected. The problem ofin...

We present algorithms for self-localization of a network of sensors. We consider the case when no “anchor” nodes with known locations are present. We use source signals in the scene, also at unknown locations, to estimate time-of-arrival and direction-of-arrival between sources and sensors. These measurements are used to compute maximum likelihood relative calibration solutions, in which sensor...

Abstract Propensity score weighting adjustment is commonly used to handle unit nonresponse. When the response mechanism is nonignorable in the sense that the response probability depends directly on the study variable, a followup sample is commonly used to obtain an unbiased estimator using the framework of two-phase sampling, where the follow-up sample is assumed to respond completely. In prac...

In this paper, we address the problem of robustly recovering several instances of a curve model from a single noisy data set with outliers. Using M-estimators revisited in a Lagrangian formalism, we derive an algorithm that we call SMRF, which extends the classical Iterative Reweighted Least Squares algorithm (IRLS). Compared to the IRLS, it features an extra probability ratio, which is classic...

Estimation of parametric variance functions for assays relies on transformation of standard deviations based on replication at each concentration. The quality of such estimates has been shown to have direct impact on the quality of inference based on the fitted calibration curve. The theory of Davidian and Carroll (1987) is used to demonstrate that ignoring unequal replication can lead to bias ...

The key point limits to define the statistical model describing the data distribution. Hence, it turns out that the characteristics related to the so-called Inverse Tully-Fisher relation and the Direct relation are maximum likelyhood (ml) estimators of different statistical models, and we obtain coherent distance estimates as long as the same model is used for the calibration of the TF relation...

Images produced by catadioptric sensors contain a significant amount of radial distortion and variation in inherent scale. Blind application of conventional shift-invariant operators or optical flow estimators yields erroneous results. One could argue that given a calibration of such a sensor we would always be able to remove distortions and apply any operator in a local perspective plane. In a...

For the problem of variable selection for the normal linear model, selection criteria such as , C p ,  and  have fixed dimensionality penalties. Such criteria are shown to correspond to selection of maximum posterior models under implicit hyperparameter choices for a particular hierarchical Bayes formulation. Based on this calibration, we propose empirical Bayes selection criteria that...

In this paper we study multi-task kernel ridge regression and try to understand when the multi-task procedure performs better than the single-task one, in terms of averaged quadratic risk. In order to do so, we compare the risks of the estimators with perfect calibration, the oracle risk. We are able to give explicit settings, favorable to the multi-task procedure, where the multi-task oracle p...

In this paper, we address the problem of robustly recovering several instances of a curve model from a single noisy data set with outliers. Using M-estimators revisited in a Lagrangian formalism, we derive an algorithm that we call Simultaneous Multiple Robust Fitting (SMRF), which extends the classical Iterative Reweighted Least Squares algorithm (IRLS). Compared to the IRLS, it features an ex...