Even though logistic regression is formulated with continuous input data in mind, one can also try to apply it to categorical inputs. For example, consider the following set-up: We observe n samples...
Even though logistic regression is formulated with continuous input data in mind, one can also try to apply it to categorical inputs. For example, consider the following set-up: We observe n samples Yİ E {0, 1). i = 1, . . . , n , and covariates Xi E {0, , . . . , n . Moreover, assume that given X,, the Y are independent. First, let us apply regular maximal likelihood estimation. To this end, write and assume that foo, fo,fro,I >0. We can parametrize this model in terms of Compute the maximum likelihood estimators po1 and P1 for poi and p11,respectively. Express your answer in terms of foo (enter "A"), fo1 (enter"B"), fio (enter "C") i (enter"D" and n. Por P11
Even though logistic regression is formulated with continuous input data in mind, one can also try to apply it to categorical inputs. For example, consider the following set-up: We observe n samples Yİ E {0, 1). i = 1, . . . , n , and covariates Xi E {0, , . . . , n . Moreover, assume that given X,, the Y are independent. First, let us apply regular maximal likelihood estimation. To this end, write and assume that foo, fo,fro,I >0. We can parametrize this model in terms of Compute the maximum likelihood estimators po1 and P1 for poi and p11,respectively. Express your answer in terms of foo (enter "A"), fo1 (enter"B"), fio (enter "C") i (enter"D" and n. Por P11