• Title/Summary/Keyword: density-based divergence measures

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Test for Parameter Change based on the Estimator Minimizing Density-based Divergence Measures

  • Na, Ok-Young;Lee, Sang-Yeol;Park, Si-Yun
    • Proceedings of the Korean Statistical Society Conference
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    • 2003.05a
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    • pp.287-293
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    • 2003
  • In this paper we consider the problem of parameter change based on the cusum test proposed by Lee et al. (2003). The cusum test statistic is constructed utilizing the estimator minimizing density-based divergence measures. It is shown that under regularity conditions, the test statistic has the limiting distribution of the sup of standard Brownian bridge. Simulation results demonstrate that the cusum test is robust when there arc outliers.

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GOODNESS OF FIT TESTS BASED ON DIVERGENCE MEASURES

  • Pasha, Eynollah;Kokabi, Mohsen;Mohtashami, Gholam Reza
    • Journal of applied mathematics & informatics
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    • v.26 no.1_2
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    • pp.177-189
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    • 2008
  • In this paper, we have considered an investigation on goodness of fit tests based on divergence measures. In the case of categorical data, under certain regularity conditions, we obtained asymptotic distribution of these tests. Also, we have proposed a modified test that improves the rate of convergence. In continuous case, we used our modified entropy estimator [10], for Kullback-Leibler information estimation. A comparative study based on simulation results is discussed also.

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A Kullback-Leiber Divergence-based Spectrum Sensing for Cognitive Radio Systems (무선인지시스템을 위한 Kullback-Leiber Divergence 기반의 스펙트럼 센싱 기법)

  • Thuc, Kieu-Xuan;Koo, In-Soo
    • Journal of Internet Computing and Services
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    • v.13 no.1
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    • pp.1-6
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    • 2012
  • In the paper, an information divergence called Kullback-Leiber divergence, which measures the average of the logarithmic difference between two probability density functions, is utilized to derive a novel method for spectrum sensing in cognitive radio systems. In the proposed sensing method, we test whether the observed samples are drawn from the noise distribution by using Kullback-Leiber divergence. It is shown by numerical results that under the same conditions, the proposed Kullback-Leiber divergence-based spectrum sensing always outperforms the energy detection based spectrum sensing significantly, especially in low SNR regime and in fading circumstance.