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m →Latent-variable formulation: add link to uniform |
m add one more step in derivation of conditional expectation |
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For this model,
:<math> E[Y|X] = 0\cdot \Pr(Y=0|X) +1\cdot \Pr(Y=1|X) = \Pr(Y=1|X) =x'\beta,</math>
and hence the vector of parameters β can be estimated using [[least squares]]. This method of fitting would be inefficient,<ref name=Cox /> and can be improved by adopting an iterative scheme based on [[weighted least squares]],<ref name=Cox/> in which the model from the previous iteration is used to supply estimates of the conditional variances, <math>\operatorname{Var}(Y|X=x)</math>, which would vary between observations. This approach can be related to fitting the model by [[maximum likelihood]].<ref name=Cox/>
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