Smoothness priors analysis of time series:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York [u.a.]
Springer
1996
|
| Schriftenreihe: | Lecture notes in statistics
116 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | X, 261 S. graph. Darst. |
| ISBN: | 0387948198 |
Internformat
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| 100 | 1 | |a Kitagawa, Genshiro |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Smoothness priors analysis of time series |c Genshiro Kitagawa ; Will Gersch |
| 264 | 1 | |a New York [u.a.] |b Springer |c 1996 | |
| 300 | |a X, 261 S. |b graph. Darst. | ||
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Datensatz im Suchindex
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| adam_text | GENSHIRO KITAGAWA WILL GERSCH SMOOTHNESS PRIORS ANALYSIS OF TIME SERIES
SPRINGER CONTENTS 1 INTRODUCTION 1 1.1 BACKGROUND 1 1.2 WHAT IS IN THE
BOOK 2 1.3 TIME SERIES EXAMPLES 3 2 MODELING CONCEPTS AND METHODS 9 2.1
AKAIKE S AIC: EVALUATING PARAMETRIC MODELS 9 2.1.1 THE KULLBACK-LEIBLER
MEASURE AND THE AKAIKE AIC 10 2.1.2 SOME APPLICATIONS OF THE AIC 12
2.1.3 A THEORETICAL DEVELOPMENT OF THE AIC 14 2.1.4 FURTHER DISCUSSION
OF THE AIC 17 2.2 LEAST SQUARES REGRESSION BY HOUSEHOLDER TRANSFORMATION
18 2.3 MAXIMUM LIKELIHOOD ESTIMATION AND AN OPTIMIZATION ALGORITHM . .
21 2.4 STATE SPACE METHODS 24 3 THE SMOOTHNESS PRIORS CONCEPT 27 3.1
INTRODUCTION 27 3.2 BACKGROUND, HISTORY AND RELATED WORK 28 3.3
SMOOTHNESS PRIORS BAYESIAN MODELING 31 4 SCALAR LEAST SQUARES MODELING
33 4.1 ESTIMATING A TREND 33 4.2 THE LONG AR MODEL 37 4.3 TRANSFER
FUNCTION ESTIMATION 47 4.3.1 ANALYSIS 48 4.3.2 A TRANSFER FUNCTION
ANALYSIS EXAMPLE 50 5 LINEAR GAUSSIAN STATE SPACE MODELING 55 5.1
INTRODUCTION 55 5.2 STANDARD STATE SPACE MODELING 57 5.3 SOME STATE
SPACE MODELS 60 5.4 MODELING WITH MISSING OBSERVATIONS 61 5.5 UNEQUALLY
SPACED OBSERVATIONS 62 5.6 AN INFORMATION SQUARE-ROOT FILTER/SMOOTHER
64 I VIII CONTENTS 6 GENERAL STATE SPACE MODELING 67 6.1 INTRODUCTION 67
6.2 THE GENERAL STATE SPACE MODEL 69 6.2.1 GENERAL FILTERING AND
SMOOTHING 70 6.2.2 MODEL IDENTIFICATION 71 6.3 NUMERICAL SYNTHESIS OF
THE ALGORITHMS 71 6.4 THE GAUSSIAN SUM-TWO FILTER FORMULA APPROXIMATION
73 6.4.1 THE GAUSSIAN SUM APPROXIMATION 73 6.4.2 THE TWO-FILTER FORMULA
AND GAUSSIAN SUM SMOOTHING . . . . 74 6.4.3 REMARKS ON THE GAUSSIAN
MIXTURE APPROXIMATION 76 6.5 A MONTE CARLO FILTERING AND SMOOTHING
METHOD 78 6.5.1 INTRODUCTION 79 6.5.2 NON-GAUSSIAN NONLINEAR STATE SPACE
MODEL AND FILTERING ... 79 6.5.3 SMOOTHING 84 6.6 A DERIVATION OF THE
KAIMAN FILTER 85 6.6.1 PREPARATIONS 85 6.6.2 DERIVATION OF THE FILTER
AND SMOOTHER 88 7 APPLICATIONS OF LINEAR GAUSSIAN STATE SPACE MODELING
91 7.1 AR TIME SERIES MODELING 92 7.2 KULLBACK-LEIBLER COMPUTATIONS 94
7.3 SMOOTHING UNEQUALLY SPACED DATA 97 7.4 A SIGNAL EXTRACTION PROBLEM
97 7.4.1 ESTIMATION OF THE TIME VARYING VARIANCE 100 7.4.2 SEPARATING A
MICRO EARTHQUAKE FROM NOISY DATA 101 7.4.3 A SECOND EXAMPLE 103 8
MODELING TRENDS 105 8.1 STATE SPACE TREND MODELS 106 8.2 STATE SPACE
ESTIMATION OF SMOOTH TREND 107 8.2.1 ESTIMATION OF A SMOOTH TREND 107
8.2.2 SMOOTH TREND PLUS AUTOREGRESSIVE MODEL 110 8.3 MULTIPLE TIME
SERIES MODELING: THE COMMON TREND PLUS INDIVIDUAL COMPONENT AR MODEL 112
8.3.1 MAXIMUM DAILY TEMPERATURES 1971-1992 112 8.3.2 TIAO AND TSAY FLOUR
PRICE DATA 116 8.4 MODELING TRENDS WITH DISCONTINUITIES 118 8.4.1
PEARSON FAMILY, GAUSSIAN MIXTURE AND MONTE CARLO FILTER ES- TIMATION OF
AN ABRUPTLY CHANGING TREND 118 9 SEASONAL ADJUSTMENT 123 9.1
INTRODUCTION 123 9.2 A STATE SPACE SEASONAL ADJUSTMENT MODEL 124 9.3
SMOOTH SEASONAL ADJUSTMENT EXAMPLES 126 9.4 NON-GAUSSIAN SEASONAL
ADJUSTMENT 130 9.5 MODELING OUTLIERS 132 CONTENTS IX 9.6 LEGENDS 136 10
ESTIMATION OF TIME VARYING VARIANCE 137 10.1 INTRODUCTION AND BACKGROUND
137 10.2 MODELING TIME-VARYING VARIANCE 138 10.3 THE SEISMIC DATA 139
10.4 SMOOTHING THE PERIODOGRAM 141 10.5 THE MAXIMUM DAILY TEMPERATURE
DATA 142 11 MODELING SCALAR NONSTATIONARY COVARIANCE TIME SERIES 147
11.1 INTRODUCTION 148 11.2 A TIME VARYING AR COEFFICIENT MODEL 149 11.3
A STATE SPACE MODEL 150 11.3.1 INSTANTANEOUS SPECTRAL DENSITY 151 11.4
PARCOR TIME VARYING AR MODELING 153 11.5 EXAMPLES 154 12 MODELING
MULTIVARIATE NONSTATIONARY COVARIANCE TIME SERIES 161 12.1 INTRODUCTION
161 12.2 THE INSTANTANEOUS RESPONSE-ORTHOGONAL INNOVATIONS MODEL 163
12.3 STATE SPACE MODELING 165 12.4 TIME VARYING PARCOR VAR MODELING 168
12.4.1 CONSTANT COEFFICIENT PARCOR VAR TIME SERIES MODELING . . 168
12.4.2 TIME VARYING PARCOR COEFFICIENT VAR MODELING 170 12.5 EXAMPLES
170 13 MODELING INHOMOGENEOUS DISCRETE PROCESSES 181 13.1 NONSTATIONARY
DISCRETE PROCESS 181 13.2 NONSTATIONARY BINARY PROCESSES 182 13.3
NONSTATIONARY POISSON PROCESS 184 14 QUASI-PERIODIC PROCESS MODELING 189
14.1 THE QUASI-PERIODIC MODEL 189 14.2 THE WOLFER SUNSPOT DATA 190 14.3
THE CANADIAN LYNX DATA 192 14.4 OTHER EXAMPLES 193 14.4.1
PHASE-UNWRAPPING 194 14.4.2 QUASI-PERIODICITY IN THE RAINFALL DATA 195
14.5 PREDICTIVE PROPERTIES OF QUASI-PERIODIC PROCESS MODELING 197 15
NONLINEAR SMOOTHING 201 15.1 INTRODUCTION 201 15.2 STATE ESTIMATION 202
15.3 A ONE DIMENSIONAL PROBLEM 204 15.4 A TWO DIMENSIONAL PROBLEM 208 X
CONTENTS 16 OTHER APPLICATIONS 213 16.1 A LARGE SCALE DECOMPOSITION
PROBLEM 213 16.1.1 DATA PREPARATION AND A STRATEGY FOR THE DATA ANALYSIS
. . . . 214 16.1.2 THE DATA ANALYSIS 217 16.2 MARKOV STATE
CLASSIFICATION 223 16.2.1 INTRODUCTION 223 16.2.2 A MARKOV SWITCHING
MODEL 223 16.2.3 ANALYSIS AND RESULTS 224 16.3 SPVAR MODELING FOR
SPECTRUM ESTIMATION 227 16.3.1 BACKGROUND 227 16.3.2 THE APPROACH AND AN
EXAMPLE 228 REFERENCES 231 AUTHOR INDEX 253 SUBJECT INDEX 257
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| discipline | Mathematik Wirtschaftswissenschaften |
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| indexdate | 2024-07-09T18:01:32Z |
| institution | BVB |
| isbn | 0387948198 |
| language | English |
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| series | Lecture notes in statistics |
| series2 | Lecture notes in statistics |
| spelling | Kitagawa, Genshiro Verfasser aut Smoothness priors analysis of time series Genshiro Kitagawa ; Will Gersch New York [u.a.] Springer 1996 X, 261 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Lecture notes in statistics 116 Statistische methoden gtt Série chronologique Séries chronologiques ram Tijdreeksen gtt Time-series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 s DE-604 Gersch, Will Verfasser aut Lecture notes in statistics 116 (DE-604)BV002447846 116 GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007323841&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kitagawa, Genshiro Gersch, Will Smoothness priors analysis of time series Lecture notes in statistics Statistische methoden gtt Série chronologique Séries chronologiques ram Tijdreeksen gtt Time-series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4067486-1 |
| title | Smoothness priors analysis of time series |
| title_auth | Smoothness priors analysis of time series |
| title_exact_search | Smoothness priors analysis of time series |
| title_full | Smoothness priors analysis of time series Genshiro Kitagawa ; Will Gersch |
| title_fullStr | Smoothness priors analysis of time series Genshiro Kitagawa ; Will Gersch |
| title_full_unstemmed | Smoothness priors analysis of time series Genshiro Kitagawa ; Will Gersch |
| title_short | Smoothness priors analysis of time series |
| title_sort | smoothness priors analysis of time series |
| topic | Statistische methoden gtt Série chronologique Séries chronologiques ram Tijdreeksen gtt Time-series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | Statistische methoden Série chronologique Séries chronologiques Tijdreeksen Time-series analysis Zeitreihenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007323841&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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