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Mixture Models - Carnegie Mellon University

www.stat.cmu.edu

20.1.2 From Kernel Density Estimates to Mixture Models We have also previously looked at kernel density estimation, where we approximate the true distribution by sticking a small (1 n weight) copy of a kernel pdf at each ob-served data point and adding them up. With enough data, this comes arbitrarily

  Density, Kernel, Estimation, Kernel density estimation, Kernel density

Hypothesis Testing with the Bootstrap - TAU

www.tau.ac.il

population density is bimodal, but the Gaussian kernel density does not approximate it accurately, then the suggested test will not have high power. Bootstrap tests are useful when the alternative hypothesis is not well specified. In cases where there is parametric alternative hypothesis, likelihood or Bayesian methods might be preferable.

  With, Testing, Density, Kernel, Hypothesis, Bootstrap, Kernel density, Hypothesis testing with the bootstrap

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