Abstract We propose a new estimator for the ultimate prediction uncertainty within famous Mack’s distribution-free chain-ladder model, which can be proved to unbiased (conditionally given first triangle column) under some additional technical assumptions. A peculiar behaviour of is by its possible negativity. This drawback might worth trading off unbiasedness property, since there empirical evi...

Given a 3D object and some measurements for points in this object, it is desired to find the 3D location of the object. A new model based pose estimator from stereo pairs based on linear programming (LP) is presented. In the presence of outliers, the new LP estimator provides better results than maximum likelihood estimators such as weighted least squares, and is usually almost as good as robus...

This paper presents a novel nonlinear estimator called the fuzzy finite memory (FFM) state for electro-hydraulic active suspension systems, based on techniques and impulse response. The Takagi-Sugeno model is introduced to effectively describe highly systems with actuator dynamics. Compared conventional estimator, which has an infinite structure requires whole data from initial current time, pr...

We present an approach for determining the temporal consistency of Particle Filters in video tracking based on model validation of their uncertainty over sliding windows. The filter uncertainty is related to the consistency of the dispersion of the filter hypotheses in the state space. We learn an uncertainty model via a mixture of Gamma distributions whose optimum number is selected by modifie...

UNCERTAIN LINEAR EQUATIONS Mert Pilancı M.S. in Electrical and Electronics Engineering Supervisor: Prof. Dr. Orhan Arıkan July 2010 In this thesis, new theoretical and practical results on linear equations with various types of uncertainties and their applications are presented. In the first part, the case in which there are more equations than unknowns (overdetermined case) is considered. A no...

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...

It is common in practice to estimate the quantiles of a complicated distribution by using the order statistics of a simulated sample. If the distribution of interest has known population mean, then it is often possible to improve the mean square error of the standard quantile estimator substantially through the simple device of meancorrection: subtract off the sample mean and add on the known p...

When making estimates of a quantum state, point estimators express one’s best guess of the true state, but estimator regions are important to express the uncertainty associated with the estimate. Employing the Bayesian approach, we discuss the use of smallest credible regions (SCRs), the smallest possible regions for given credibilities, as optimal estimator regions. However, constructing the S...

Abstract. We consider the problem of evaluation of the measurement results from the interlaboratory comparisons in metrology. It is assumed that the laboratories have either normally, uniformly, or triangularly distributed systematic errors (biases). We propose an approximate interval estimator for the common mean, i.e. the true value of the measurand. The empirical coverage probabilities of th...