We introduce a novel probability density function for modeling the distribution of points around an ellipsoidal surface. This density is part of the family of elliptical distributions. We establish the theoretical con...
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We introduce a novel probability density function for modeling the distribution of points around an ellipsoidal surface. This density is part of the family of elliptical distributions. We establish the theoretical convergence properties of the parameter estimators and validate them using simulated data. Furthermore, we propose a mixture model utilizing this density, and we estimate its parameters using the Expectation-Maximization (em) algorithm. To assess its performance, we compare the algorithm to a state-of-the-art ellipse fitting method and conduct experiments on 3D real data obtained from depth cameras.
This paper considers the estimation of parameters based on a progressively type-I interval-censored data from a mixed generalized exponential distribution. The maximum likelihood estimation is used but an analytic for...
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This paper considers the estimation of parameters based on a progressively type-I interval-censored data from a mixed generalized exponential distribution. The maximum likelihood estimation is used but an analytic form cannot be obtained. The em algorithm is applied to obtain the maximum likelihood estimates. The performance of the estimates is judged by a simulating study and a real data is presented to illustrate the method of estimation developed here.
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