Generalized linear mixture models for handling nonignorable by Fitzmaurice G.M., Laird N.M.

By Fitzmaurice G.M., Laird N.M.

Show description

Read or Download Generalized linear mixture models for handling nonignorable dropouts in longitudinal studies PDF

Similar linear books

Mathematik für Ingenieure: Eine anschauliche Einführung für das praxisorientierte Studium (Springer-Lehrbuch) (German Edition)

"Mathematik in entspannter Atmosphäre" ist das Leitbild dieses leicht verständlichen Lehrbuchs. Im Erzählstil und mit vielen Beispielen beleuchtet der Autor nicht nur die Höhere Mathematik, sondern er stellt auch den Lehrstoff in Bezug zu den Anwendungen. Die gesamte für den Ingenieurstudenten wichtige Mathematik wird in einem Band behandelt.

Applications of Lie Algebras to Hyperbolic and Stochastic Differential Equations (Mathematics and Its Applications)

The most a part of the publication is predicated on a one semester graduate path for college students in arithmetic. i've got tried to enhance the idea of hyperbolic platforms of differen­ tial equations in a scientific method, making as a lot use as attainable ofgradient platforms and their algebraic illustration. although, regardless of the powerful sim­ ilarities among the improvement of rules the following and that present in a Lie alge­ bras path this isn't a booklet on Lie algebras.

Linear Operators and Matrices: The Peter Lancaster Anniversary Volume

In September 1998, in the course of the 'International Workshop on research and Vibrat­ ing platforms' held in Canmore, Alberta, Canada, it used to be determined via a gaggle of contributors to honour Peter Lancaster at the get together of his seventieth birthday with a quantity within the sequence 'Operator concept: Advances and Applications'.

Harmonic Analysis on Exponential Solvable Lie Groups (Springer Monographs in Mathematics)

This booklet is the 1st one who brings jointly contemporary effects at the harmonic research of exponential solvable Lie teams. There nonetheless are many attention-grabbing open difficulties, and the booklet contributes to the long run growth of this study box. besides, a variety of similar issues are provided to inspire younger researchers.

Extra resources for Generalized linear mixture models for handling nonignorable dropouts in longitudinal studies

Sample text

7), attains the minimum for any solution of X, Xb = X'y. 13) and note that Xb = = X(X'X)-X'y+X(1 - (X'X)-X'X)w X(X' X)- X'y (which is independent of w). 81. 11) > (y - Xb)'(y - Xb) = 8(b) = y'y - 2y'Xb+ b'X'Xb = y'y - b'X'Xb = y'y - fj'fj. 3 Geometric Properties of OL8 For the T x K-matrix X, we define the column space n(x) = {(I: (I = X(3, (3 E n K }, which is a subspace of nT. If we choose the norm IIxli = (x'x)1/2 for x E nT, then the principle of least squares is the same as that of minimizing II y - (I II for (I E n(X).

3 Mean Dispersion Error The quadratic risk is closely related to the matrix-valued criterion of the mean dispersion error (MDE) of an estimator. The MDE is defined as the matrix M({J, {3) = E({J - {3)({J - {3)'. 42) We will denote the covariance matrix of an estimator (J by V({J): V({J) = E({J - E({J))(/3 - E(/3))'. If E(/3) = {3, then /3 will be called unbiased (for {3). If E(/3) called biased. The difference between E({J) and {3 is Bias({J, {3) = E({J) - {3. If {J is unbiased, then obviously Bias({J, {3) = 0 .

XKi)' is a K-vector 3. 2): Yt = x~{3 + et , t = 1, ... 4) where {3' = ({31, ... 5) where e' = (ell ... , eT). We consider the problems of estimation and testing of hypotheses on {3 under some assumptions. 6) t=l for a suitably chosen function M, some examples of which are M(x) = Ixl and x 2 • More generally, one could minimize a global function of e such as maxt Iet lover t. First we consider the case M (x) = x 2 , which leads to the least-squares theory, and later, introduce other functions that may be more appropriate in some situations.

Download PDF sample

Rated 4.40 of 5 – based on 48 votes