Dynamic Linear Models with R

Front Cover
Springer Science & Business Media, Jun 12, 2009 - Mathematics - 252 pages

State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms.

The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online.

No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.

 

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Contents

I
1
II
2
III
5
IV
11
V
14
VI
24
VIII
25
IX
31
XXXVII
134
XXXVIII
136
XXXIX
138
XL
139
XLI
142
XLII
143
XLIII
144
XLIV
148

X
35
XI
39
XII
41
XIII
43
XIV
48
XV
49
XVI
51
XVII
53
XVIII
59
XIX
60
XX
66
XXI
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XXII
77
XXIII
83
XXIV
85
XXV
87
XXVI
88
XXVII
89
XXVIII
102
XXIX
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XXX
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XXXI
115
XXXII
121
XXXIII
125
XXXIV
126
XXXV
127
XXXVI
132
XLV
149
XLVI
150
XLVII
158
XLVIII
160
XLIX
161
L
162
LI
165
LII
167
LIV
171
LV
177
LVI
186
LVIII
192
LIX
200
LX
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LXI
207
LXII
208
LXIII
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LXIV
216
LXV
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LXVI
226
LXVII
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LXVIII
230
LXIX
237
LXX
241
LXXI
244
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