نتایج جستجو برای: parameter estimation

تعداد نتایج: 451060  

ژورنال: اندیشه آماری 2021

‎Whenever approximate and initial information about the unknown parameter of a distribution is available, the shrinkage estimation method can be used to estimate it. In this paper, first the $ E $-Bayesian estimation of the parameter of inverse Rayleigh distribution under the general entropy loss function is obtained. Then, the shrinkage estimate of the inverse Rayleigh distribution parameter i...

Journal: :Geographical Analysis 2010

In this paper, we consider admissible estimation of the parameter ?r in the gamma distribution with truncated parameter space under entropy loss function. We obtain the classes of admissible estimators. The result can be applied to estimation of parameters in the normal, lognormal, pareto, generalized gamma, generalized Laplace and other distributions.

Journal: :international journal of robotics 0
saeed ebrahimi yazd university mohammad bozorg yazd university mehdi zare ernani kavian petrochemical company

in the procedure of designing an underwater vehicle or robot, its maneuverability and controllability must be simulated and tested, before the product is finalized for manufacturing. since the hydrodynamic forces and moments highly affect the dynamic and maneuverability of the system, they must be estimated with a reasonable accuracy. in this study, hydrodynamic coefficients of an autonomous un...

Journal: :Statistics, Optimization & Information Computing 2018

Journal: :رادار 0
عطاالله ابراهیم زاده محمد آخوندی درزیکلایی

parameter estimation is an important task in the modeling, classification, and detection of radar clutters. radar clutters have stochastic characteristics. therefore, statistical distributions are usually used to describe the features of clutters better. k distribution is one of the most common models utilized to the simulation of clutters. this distribution, which consists of scale and shape p...

Journal: :IEEE Transactions on Automatic Control 2008

Journal: :Statistical Inference for Stochastic Processes 2007

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