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Models, Weighting, and Transformations

That which is static and repetitive is boring. That which is dynamic and random is confusing. In between lies art. [Pg.125]

In the previous chapters it was assumed that the model relating an n-size matrix of predictor variables x to an n-size vector of paired responses Y was of the functional form [Pg.125]

Equation (4.2) is called a residual variance model, but it is not a very general one. In this case, the model states that random, unexplained variability is a constant. Two methods are usually used to estimate 0 least-squares (LS) and maximum likelihood (ML). In the case where e N(0, a2), the LS estimates are equivalent to the ML estimates. This chapter will deal with the case for more general variance models when a constant variance does not apply. Unfortunately, most of the statistical literature deals with estimation and model selection theory for the structural model and there is far less theory regarding choice and model selection for residual variance models. [Pg.125]


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