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Regression analysis, normal and non-normal accurate p-values from likelihood analysis
(with D.A.S. Fraser and J. Wu)
Journal of the American Statistical Association , 94(448), 1999, pp. 1286-1295
Abstract: We develop simple approximations for the p-values to use with regression models having linear or nonlinear parameter structure and normal or non-normal error distribution; computer iteration then gives confidence intervals. Both frequentist and Bayesian versions are given. The approximations are derived from recent developments in likelihood analysis and have third order accuracy. Also for very small and medium sized samples the accuracy can typically be high. The likelihood basis of the procedure seems to provide the grounds for this general accuracy. Examples are discussed and simulations record the distributional accuracy.
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