Likelihood Estimation for the INAR(p) Model by Saddlepoint Approximation
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Powerful mathematical tools have been developed for trading in stocks and bonds, but other markets that are equally important for the globalized world have to some extent been neglected. We decided to study the shipping market as an new area of development ...
An application to antenna optimization of bayesian network density of probability estimators is presented. This technique is very usefull for optimizations where abig number of parameters, multiple solutions and local minima increase the likelihood to conv ...
We have developed a model for pedestrian walking behavior, based on discrete choice modeling. The model is estimated by maximum likelihood estimation on a real data set of pedestrian trajectories, manually tracked from video sequences filmed in Japan. The ...
We propose a high-resolution ranging algorithm for impulse radio (IR) ultra-WideBand (UWB) communication systems in additive white Gaussian noise. We formulate the ranging problem as a maximum- likelihood (ML) estimation problem for the channel delays and ...
Institute of Electrical and Electronics Engineers2009
The article develops the approach of Ferro and Segers (2003) to the estimation of the extremal index, and proposes the use of a new variable decreasing the bias of the likelihood based on the point process character of the exceedances. Two estimators are d ...
The article develops the approach of Ferro and Segers (2003) to the estimation of the extremal index, and proposes the use of a new variable decreasing the bias of the likelihood based on the point process character of the exceedances. Two estimators are d ...
An application to antenna optimization of bayesian network density of probability estimators is presented. This technique is very usefull for optimizations where abig number of parameters, multiple solutions and local minima increase the likelihood to conv ...
A non-parametric method of distribution estimation for univariate data is presented. The idea is to adapt the smoothing spline procedure used in regression to the estimation of distributions via a scatterplot smoothing of theempirical distribution function ...
We formulate and solve a new parameter estimation problem in the presence of data uncertainties. The new method is suitable when a priori bounds on the uncertain data are available, and its solution leads to more meaningful results, especially when compare ...
Society for Industrial and Applied Mathematics1998
A new likelihood method to estimate the extremal index, together with an application to temperature data, is presented. Conditions for the validity of the model are discussed and diagnostics are proposed. ...