23,890 research outputs found
Continuous Empirical Characteristic Function Estimation of Mixtures of Normal Parameters
This paper develops an e±cient method for estimating the discrete mix- tures of normal family based on the continuous empirical characteristic function (CECF). An iterated estimation procedure based on the closed form objective distance function is proposed to improve the estimation effciency. The results from the Monte Carlo simulation reveal that the CECF estimator produces good finite sample properties. In particular, it outperforms the discrete type of methods when the maximum likelihood estimation fails to converge. An empirical example is provided for illustrative purposes.Empirical characteristic function; Mixtures of normal.
Discrete Surface Modeling Based on Google Earth: A Case Study
Google Earth (GE) has become a powerful tool for geological, geophysical and
geographical modeling; yet GE can be accepted to acquire elevation data of
terrain. In this paper, we present a real study case of building the discrete
surface model (DSM) at Haut-Barr Castle in France based on the elevation data
of terrain points extracted from GE using the COM API. We first locate the
position of Haut-Barr Castle and determine the region of the study area, then
extract elevation data of terrain at Haut-Barr, and thirdly create a planar
triangular mesh that covers the study area and finally generate the desired DSM
by calculating the elevation of vertices in the planar mesh via interpolating
with Universal Kriging (UK) and Inverse Distance Weighting (IDW). The generated
DSM can reflect the features of the ground surface at Haut-Barr well, and can
be used for constructingthe Sealed Engineering Geological Model (SEGM) in
further step.Comment: Proceedings of IEEE Conference, ICCSNT 2012, in Pres
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