SMAT - Linear Models Course
I have some problems understanding these parts of the course/exercises
1) In series 12, why is the density of y_j, g(y_j-x_j^T beta), where g() is the denstiy of epsilon_j, my guess would be that y_j-x_j^T beta = epsilon_j but then why do they have different density functions ? Or are they actually the same "modulo a shift" ?
2)On the slides about Robust/Resistent Regression: M-estimation as Weighted Regression : when taking the derivative with respect to gamma, why is there still x_i^T in the sum with psi, since according to the chain rule we take the transpose of x_i^T ?
Thanks in advance.
I have a question about the exercise 1 of serie 12. I don't really understand why the variance is the inverse of the fisher information?
I don't understand how to find "la valeur critique de ce test au niveaux 5%" which is 5.32 in the correction ?
And how do we find s^2, "estimateur de la variance pour le modèle complet" to calculate Cp ?
In week6, slide 3 page3, when computing delta for the 3 different cases, I don't understand the calculation that results to the bias in the wrong model and to 0 (no bias) for the correct and true model.. What am I missing?
Thanks in advance,
I'm just wondering if for each step of the anova method we must have independant column to be able to compute the matrix H?
Hello! I was checking the course notes and on the slide "More on diagnosing Multicollinearity", we consider the spectral decomposition of the matrix (X^TX), but this matrix isn't always symethric! I'm a bit confused. Thanks in advanced, best wishes,
il me semble que lorsque j'utilise la fonction bisquare, ma regression robuste fonctionne. Cependant quand je veux plotter cette regression, je reçois le message d'erreur suivant:
rfit2 <- rlm(calls~year, phones, method="M", psi=psi.bisquare)