The monte carlo newton-raphson algorithm

  title={The monte carlo newton-raphson algorithm},
  author={Anthony Y. C. Kuk and Yuk W. Cheng},
  journal={Journal of Statistical Computation and Simulation},
It is shown that the Monte Carlo Newton-Raphson algorithm is a viable alternative to the Monte Carlo EM algorithm for finding maximum likelihood estimates based on incomplete data. Both Monte Carlo procedures require simulations from the conditional distribution of the missing data given the observed data with the aid of methods like Gibbs sampling and rejective sampling. The Newton-Raphson algorithm is computationally more efficient than the EM algorithm as it converges faster. We further… Expand
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