753 research outputs found

    Goodness-of-Fit Test: Khmaladze Transformation vs Empirical Likelihood

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    This paper compares two asymptotic distribution free methods for goodness-of-fit test of one sample of location-scale family: Khmaladze transformation and empirical likelihood methods. The comparison is made from the perspective of empirical level and power obtained from simulations. When testing for normal and logistic null distributions, we try various alternative distributions and find that Khmaladze transformation method has better power in most cases. R-package which was used for the simulation is available online. See section 5 for the detail

    Factors influencing CDM locations in China

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    Environmental Economics and Policy,

    A Fast Algorithm for Implementation of Koul's Minimum Distance Estimators and Their Application to Image Segmentation

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    Minimum distance estimation methodology based on an empirical distribution function has been popular due to its desirable properties including robustness. Even though the statistical literature is awash with the research on the minimum distance estimation, the most of it is confined to the theoretical findings: only few statisticians conducted research on the application of the method to real world problems. Through this paper, we extend the domain of application of this methodology to various applied fields by providing a solution to a rather challenging and complicated computational problem. The problem this paper tackles is an image segmentation which has been used in various fields. We propose a novel method based on the classical minimum distance estimation theory to solve the image segmentation problem. The performance of the proposed method is then further elevated by integrating it with the ``segmenting-together" strategy. We demonstrate that the proposed method combined with the segmenting-together strategy successfully completes the segmentation problem when it is applied to the complex, real images such as magnetic resonance images

    A 94-GHz Frequency Modulation Continuous Wave Radar Imaging and Motion Compensation

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    A compact and lightweight synthetic aperture radar (SAR) that can be loaded on a miniature unmanned aerial vehicle (UAV) was recently developed. The higher the frequency is, the smaller is the antenna size and the microwave characteristics are improved. Thus, a high frequency is favorable for miniaturization and weight reduction. In this chapter, a method of obtaining a radar image through a 94-GHz frequency modulation continuous wave (FMCW) radar is proposed. In addition, a method of motion compensation is described, and the W-band SAR image after motion compensation is confirmed. This kind of SAR imaging can provide geographic information and characteristics of extreme environments, disaster scenes, and information on sites where human access is difficult
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