, } 5.16 A study has n; independent binary observations (yi, Yin) at x;, i = 1,...,N, with n = Σ; n;. Consider the model logit(л;) = ß₁+ ß₁ס, where ₪; = P(yij = 1). a. Show that the kernel of the likelihood function is the same if treating the data as n Bernoulli observations or N binomial observations. b. For the saturated model, explain why the likelihood function is different for these two data forms. Hence, the deviance reported by software depends on the form of data entry. c. Explain why the difference between deviances for two unsaturated models does not depend on the form of data entry.
, } 5.16 A study has n; independent binary observations (yi, Yin) at x;, i = 1,...,N, with n = Σ; n;. Consider the model logit(л;) = ß₁+ ß₁ס, where ₪; = P(yij = 1). a. Show that the kernel of the likelihood function is the same if treating the data as n Bernoulli observations or N binomial observations. b. For the saturated model, explain why the likelihood function is different for these two data forms. Hence, the deviance reported by software depends on the form of data entry. c. Explain why the difference between deviances for two unsaturated models does not depend on the form of data entry.
MATLAB: An Introduction with Applications
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Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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}
5.16 A study has n; independent binary observations (yi, Yin) at x;, i =
1,...,N, with n = Σ; n;. Consider the model logit(л;) = ß₁+ ß₁ס, where
₪; = P(yij = 1).
a. Show that the kernel of the likelihood function is the same if treating the
data as n Bernoulli observations or N binomial observations.
b. For the saturated model, explain why the likelihood function is different for
these two data forms. Hence, the deviance reported by software depends
on the form of data entry.
c. Explain why the difference between deviances for two unsaturated models
does not depend on the form of data entry.
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