In this paper, the estimation of a scale parameter t under a new and bounded loss function, based on a reflection of the gamma density function, is discussed. The best scale-invariant estimator of tis obtained and the admissibility of all linear functions of the sufficient statistic, for estimating t in the absence of a nuisance parameter, is investigated

it is well known that a microperiodic function mapping a topological group into reals, which is continuous at some point is constant. we introduce the notion of a microperiodic multifunction, defined on a topological group with values in a metric space, and study regularity conditions implying an analogous result. we deal with vietoris and hausdorff continuity concepts.stability of microperiodi...

Motivated by quantizing a continuous distribution for the purpose of sampling, we develop a new cost function for histograms and give two algorithms which approximate the optimal cost. Our algorithms are greedy and have optimality guarantees. The second is an adaption of the first in the context of data streams. In this context, we show that the histogram becomes stable fast, in any case; in ad...

Hybrid separation processes can offer significant cost-savings over simple distillation processes for close-boiling mixtures. In addition, the separation boundaries of azeotropic mixtures can be overcome. The application of hybrid processes in industry, however, is hindered by the lack of robust and efficient process synthesis tools and methods for these complex and highly integrated processes....

Problems dealing with the design and the operations of gas transmission networks are challenging. The difficulty mainly arises from the simultaneous modeling of gas transmission laws and of the investment costs. The combination of the two yields a nonlinear non-convex optimization problem. To obviate this shortcoming, we propose a new formulation as a multi-objective problem, with two objective...

We propose a new framework for hybrid system identification, which relies on continuous optimization. This framework is based on the minimization of a cost function that can be chosen as either the minimum or the product of loss functions. The former is inspired by traditional estimation methods, while the latter is inspired by recent algebraic and support vector regression approaches to hybrid...

In this paper, a Bayesian approach is proposed for shift point detection in an inverse Gaussian distribution. In this study, the mean parameter of inverse Gaussian distribution is assumed to be constant and shift points in shape parameter is considered. First the posterior distribution of shape parameter is obtained. Then the Bayes estimators are derived under a class of priors and using variou...

Multidimensional scaling addresses the problem how proximity data can be faithfully visualized as points in a low-dimensional Euclidian space. The quality of a data embedding is measured by a cost function called stress which compares proximity values with Euclidian distances of the respective points. We present a novel deterministic an-nealing algorithm to eeciently determine embedding coordin...

A general nonlinear regression model is considered in the form of fitting a sum of damped sinusoids to a series of non-uniform observations. The problem of parameter estimation in this model is important in many applications like signal processing. The corresponding continuous optimization problem is typically difficult due to the high multiextremal character of the objective function. It is sh...