A note on parameter and standard error estimation in adaptive robust regression
Abbreviated Journal Title
J. Stat. Comput. Simul.
EM algorithm; iterative reweighting; normal/independent family; regression; SAS NLIN; t distribution; slash distribution; T-DISTRIBUTION; EM ALGORITHM; Computer Science, Interdisciplinary Applications; Statistics &; Probability
This paper introduces practical methods of parameter and standard error estimation for adaptive robust regression where errors are assumed to be from a normal/independent family of distributions. In particular, generalized EM algorithms (GEM) are considered for the two cases of t and slash families of distributions. For the t family, a one step method is proposed to estimate the degree of freedom parameter. Use of empirical information is suggested for standard error estimation. It is shown that this choice leads to standard errors that can be obtained as a by-product of the GEM algorithm. The proposed methods, as discussed, can be implemented in most available nonlinear regression programs. Details of implementation in SAS NON are given using two specific examples.
Journal of Statistical Computation and Simulation
"A note on parameter and standard error estimation in adaptive robust regression" (2001). Faculty Bibliography 2000s. 8046.