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Nonlinear Discriminant Functions for Mixed Random Walk Models

Research Authors
H. M. Moustafa,
S. G. Ramadan,
S. A. Mohammed
Research Abstract

A procedure is presented for finding maximum likelihood estimates of the parameters of a mixture of two random walk distributions in two cases, using classified and unclassified observations. Based on small sample size, estimation of nonlinear discriminant functions is considered. Throughout simulation experiments, the performance of the corresponding estimated nonlinear discriminant functions is investigated. The total probabilities of misclassification and percentage biases are evaluated and discussed.

Research Department
Research Journal
Journal: Communications in Statistics - Simulation and Computation
Research Publisher
Taylor & Francis
Research Rank
1
Research Vol
Vol. 39, - No 10
Research Website
Taylor & Francis
Research Year
2010
Research Pages
PP. 1923–1938