Pseudo-Maximization and Self-Normalized Processes *

  title={Pseudo-Maximization and Self-Normalized Processes *},
  author={Victor H. de la Pe{\~n}a and Michael J. Klass and Tze Leung Lai},
Self-normalized processes are basic to many probabilistic and statistical studies. They arise naturally in the the study of stochastic integrals, martingale inequalities and limit theorems, likelihood-based methods in hypothesis testing and parameter estimation, and Studentizedpivots and bootstrap-t methods for confidence intervals. In contrast to standard normalization, large values of the observations play a lesser role as they appear both in the numerator and its self-normalized denominator… CONTINUE READING


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