Introduction to General and Generalized Linear Models
eBook - PDF

Introduction to General and Generalized Linear Models

  1. 316 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Introduction to General and Generalized Linear Models

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Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models presents likelihood-based techniques for statistical modelling using various types of data. Implementations using R are provided throughout the text, although other software packages are also discussed. Numerous

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Yes, you can access Introduction to General and Generalized Linear Models by Henrik Madsen, Poul Thyregod in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
CRC Press
Year
2010
ISBN
9781439891148
Edition
1
78
General
linear
models
1)
2)
3)
4)
Residuals
Residuals
Residuals
Residuals
Figure
3.7:
Residual
plots.
1)
Acceptable.
2)
The
variance
grows
with
Ė†
Ī¼
i
.
Do
a
weighted
analysis
or
transform
the
Y
ā€™s.
3)
Lack
of
constant
term.
The
regression
is
possibly
erroneously
forced
trough
zero.
4)
Bad
model,
try
transforming
the
Y
ā€™s.
x
Remark
3.24
It
should
be
noticed
that
plot
of
the
residuals
against
the
observations
will,
even
for
a
validated
model,
almost
always
show
a
dependence
structure
like
the
one
illustrated
as
No.
3
in
Figure
3.7,
i.e.,
the
residuals
and
the
observations
are
positively
correlated.
This
is
simply
due
to
the
fact
that
Cor[
Y
i
,
r
i
]
=
1
-
R
2
(3.71)
where
R
is
the
coeļ¬ƒcient
of
determination
deļ¬ned
in
(3.45)
on
page
57.
x
3.11
Representation
of
linear
models
As
we
have
seen,
classical
GLMs
may
be
represented
geometrically
in
terms
of
vector
spaces
and
subspaces.
Other
representations
are
by
means
of
design

Table of contents

  1. Cover
  2. Title
  3. Copyright
  4. Contents
  5. Preface
  6. CHAPTER 1: Introduction
  7. CHAPTER 2: The likelihood principle
  8. CHAPTER 3: General linear models
  9. CHAPTER 4: Generalized linear models
  10. CHAPTER 5: Mixed effects models
  11. CHAPTER 6: Hierarchical models
  12. CHAPTER 7: Real life inspired problems
  13. APPENDIX A: Supplement on the law of error propagation
  14. APPENDIX B: Some probability distributions
  15. APPENDIX C: List of symbols
  16. Bibliography