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In the context of Generalized Linear Models consider response variables Yl, which are assumed
to be exponentially distributed. The specification for the model is EYC=exp(+x) (i) Specify the
natural link function associated with linear predictor i=+xi [2] (ii) Given the maximum
likelihood estimates ^=7.35 and s.e. (^)=2.13 construct an approximate 99% confidence interval
for , and thus interpret the significance of this parameter. [3] (iii) Compare model A:EYi=, and
model B:EYi=+xi, by testing whether =0, and thus identify which of the two models is more
appropriate. To do this use the available measurements of scaled deviances: for model A 17.31,
and model B 13.19. [5]

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GLM Exponential Response Variable Models

  • 1. In the context of Generalized Linear Models consider response variables Yl, which are assumed to be exponentially distributed. The specification for the model is EYC=exp(+x) (i) Specify the natural link function associated with linear predictor i=+xi [2] (ii) Given the maximum likelihood estimates ^=7.35 and s.e. (^)=2.13 construct an approximate 99% confidence interval for , and thus interpret the significance of this parameter. [3] (iii) Compare model A:EYi=, and model B:EYi=+xi, by testing whether =0, and thus identify which of the two models is more appropriate. To do this use the available measurements of scaled deviances: for model A 17.31, and model B 13.19. [5]