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BINARY RESPONSE MODELS
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9 e ft 9 e Q apply approximation assume assumption asymptotic normality asymptotic properties bivariate normal distribution BOX-COX TRANSFORMATION censoring chi-square compact subset consider correlation table defined dose-response problem Figure finite given grouped data Hessian Implicit Function Theorem interior point intervals iterative J. R. Statist K-L information number Lemma A.2 likelihood function linear logistic regression log(2ir log(n log(x log[p 9 logistic model logistic regression matrix maximum likelihood equations Maximum likelihood estimation MLE of 9 MLE's Normal pdf normal regression observations falling obtain the MLE order partial derivatives Original Scale parameter space Pethybridge point of Q probability Probit procedure random variables Regression Line Slutsky's Theorem Stirling's approximation Stirling's formula strong consistency Theorem 2.3 thesis tion transformation to normality Transformed Scale ungrouped uniform SLLN uniformly in 9 unique global maximum univariate University of Wisconsin-Madison value 9 variance-covariance matrix