Regression models for time series analysis:
Gespeichert in:
1. Verfasser: | |
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Format: | Buch |
Sprache: | English |
Veröffentlicht: |
Hoboken, N.J.
Wiley-Interscience
2002
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Schriftenreihe: | Wiley series in probability and statistics
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Schlagworte: | |
Online-Zugang: | Inhaltsverzeichnis |
Beschreibung: | Includes biblographical references (p. 297-326) and index |
Beschreibung: | XIV, 337 S. Ill. |
ISBN: | 0471363553 |
Internformat
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245 | 1 | 0 | |a Regression models for time series analysis |c Benjamin Kedem, Konstantinos Fokianos |
264 | 1 | |a Hoboken, N.J. |b Wiley-Interscience |c 2002 | |
300 | |a XIV, 337 S. |b Ill. | ||
336 | |b txt |2 rdacontent | ||
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490 | 0 | |a Wiley series in probability and statistics | |
500 | |a Includes biblographical references (p. 297-326) and index | ||
650 | 4 | |a Analyse de régression | |
650 | 7 | |a Regressiemodellen |2 gtt | |
650 | 4 | |a Série chronologique | |
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Datensatz im Suchindex
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adam_text | REGRESSION MODELS FOR TIME SERIES ANALYSIS BENJAMIN KEDEM UNIVERSITY OF
MARYLAND KONSTANTINOS FOKIANOS UNIVERSITY OF CYPRUS ,WILEY- INTERSCIENCE
A JOHN WILEY & SONS, INC., PUBLICATION CONTENTS DEDICATION V PREFACE
XIII 1 TIME SERIES FOLLOWING GENERALIZED LINEAR MODELS 1 1.1 PARTIAL
LIKELIHOOD 2 1.2 GENERALIZED LINEAR MODELS AND TIME SERIES 4 1.3 PA
RTIAL LIKELIHOOD INFERENCE 10 1.3.1 ESTIMATION OF THE DISPERSION
PARAMETER 14 1.3.2 ITERATIVE REWEIGHTED LEAST SQUARES 14 1.4 ASYMPTOTIC
THEORY 16 1.4.1 UNIQUENESS AND EXISTENCE 17 1.4.2 LARGE SAMPLE
PROPERTIES 17 1.5 TESTING HYPOTHESES 20 1.6 DIAGNOSTICS 23 1.6.1
DEVIANCE 24 1.6.2 MODEL SELECTION CRITERIA 25 1.6.3 RESIDUALS 25 1.7
QUASI-PARTIAL LIKELIHOOD 28 1.7.1 GENERALIZED ESTIMATING EQUATIONS 31
1.8 REAL DATA EXAMPLES 33 VII VIII CONTENTS 1.8.1 A NOTE ON COMPUTATION
33 1.8.2 A NOTE ON MODEL BUILDING 33 1.8.3 ANALYSIS OF MORTALITY COUNT
DATA 34 1.8.4 APPLICATION TO EVAPOTRANSPIRATION 39 1.9 PROBLEMS AND
COMPLEMENTS 42 2 REGRESSION MODELS FOR BINARY TIME SERIES 49 2.1 LINK
FUNCTIONS FOR BINARY TIME SERIES 50 2.1.1 THE LOGISTIC REGRESSION MODEL
51 2.1.2 PROBIT AND OTHER LINKS 54 2.2 PARTIAL LIKELIHOOD ESTIMATION 56
2.3 INFERENCE FOR LOGISTIC REGRESSION 59 2.3.1 ASYMPTOTIC RELATIVE
EFFICIENCY 64 2.4 GOODNESS OF FIT 65 2.4.1 DEVIANCE 66 2.4.2 GOODNESS OF
FIT BASED ON RESPONSE CLASSIFICATION 66 2.5 REAL DATA EXAMPLES 70 2.5.1
RAINFALL PREDICTION 70 2.5.2 MODELING SUCCESSIVE ERUPTIONS 72 2.5.3
STOCK PRICE PREDICTION 76 2.5.4 MODELING SLEEP DATA 79 2.6 PROBLEMS AND
COMPLEMENTS 81 3 REGRESSION MODELS FOR CATEGORICAL TIME SERIES 89 3.1
MODELING 90 3.2 LINK FUNCTIONS FOR CATEGORICAL TIME SERIES 92 3.2.1
MODELS FOR NOMINAL TIME SERIES 93 3.2.2 MODELS FOR ORDINAL TIME SERIES
97 3.3 PARTIAL LIKELIHOOD ESTIMATION 101 3.3.1 INFERENCE FOR M=3 101
3.3.2 INFERENCE FOR M 3 104 3.3.3 LARGE SAMPLE THEORY 107 3.3.4
INFERENCE FOR THE MULTINOMIAL LOGIT MODEL 108 3.3.5 TESTING HYPOTHESES
109 3.4 GOODNESS OF FIT 110 3.4.1 GOODNESS OF FIT BASED ON RESPONSE
CLASSIFICATION 111 CONTENTS IX 3.4.2 POWER DIVERGENCE FAMILY OF GOODNESS
OF FIT TESTS 112 3.4.3 A FAMILY OF GOODNESS OF FIT TESTS 113 3.4.4
FURTHER DIAGNOSTIC TOOLS 115 3.5 EXAMPLES 116 3.5.1 EXPLANATORY ANALYSIS
OF DNA SEQUENCE DATA 116 3.5.2 SOCCER FORECASTING 119 3.5.3 SLEEP DATA
REVISITED 121 3.6 ADDITIONAL TOPICS 125 3.6.1 ALTERNATIVE MODELING 125
3.6.2 SPECTRAL ANALYSIS 125 3.6.3 LONGITUDINAL DATA 125 3.7 PROBLEMS AND
COMPLEMENTS 126 APPENDIX: ASYMPTOTIC THEORY 130 REGRESSION MODELS FOR
COUNT TIME SERIES 139 4.1 MODELING 140 4.2 MODELS FOR TIME SERIES OF
COUNTS 142 4.2.1 THE POISSON MODEL 142 4.2.2 THE DOUBLY TRUNCATED
POISSON MODEL 148 4.2.3 THE ZEGER-QAQISH MODEL 153 4.3 INFERENCE 154
4.3.1 PARTIAL LIKELIHOOD ESTIMATION FOR THE POISSON MODEL 154 4.3.2
ASYMPTOTIC THEORY 156 4.3.3 PREDICTION INTERVALS 157 4.3.4 INFERENCE FOR
THE ZEGER-QAQISH MODEL 157 4.3.5 HYPOTHESIS TESTING 158 4.4 GOODNESS OF
FIT 159 4.4.1 DEVIANCE 159 4.4.2 RESIDUALS 159 4.5 DATA EXAMPLES 159
4.5.1 MONTHLY COUNT OF RAINY DAYS 160 4.5.2 TOURIST ARRIVAL DATA 163 4.6
PROBLEMS AND COMPLEMENTS 168 OTHER MODELS AND ALTERNATIVE APPROACHES 175
5. 1 INTEGER AUTOREGRESSIVE AND MOVING AVERAGE MODELS 175 5.1.1
BRANCHING PROCESSES WITH IMMIGRATION 175 CONTENTS 5.1.2 INTEGER
AUTOREGRESSIVE MODELS OF ORDER 1 178 5.1.3 ESTIMATION FOR INAR(L)
PROCESS 183 5.1.4 INTEGER AUTOREGRESSIVE MODELS OF ORDER P 184 5.1.5
REGRESSION ANALYSIS OF INTEGER AUTOREGRESSIVE MODELS 185 5.1.6 INTEGER
MOVING AVERAGE MODELS 185 5.1.7 EXTENSIONS AND MODIFICATIONS 188 5.2
DISCRETE AUTOREGRESSIVE MOVING AVERAGE MODELS 189 5.3 THE MIXTURE
TRANSITION DISTRIBUTION MODEL 190 5.3.1 ESTIMATION IN MTD MODELS 192
5.3.2 OLD FAITHFUL DATA REVISITED 192 5.3.3 EXPLANATORY ANALYSIS OFDNA
SEQUENCE DATA REVISITED 193 5.3.4 SOCCER FORECASTING DATA REVISITED 193
5.4 HIDDEN MARKOV MODELS 194 5.5 VARIABLE MIXTURE MODELS 197 5.5.1
THRESHOLD MODELS 198 5.5.2 PARTIAL LIKELIHOOD INFERENCE 198 5.5.3
COMPARISON WITH THE THRESHOLD MODEL 199 5.6 ARCH MODELS 199 5.6.1 THE
ARCH(L) MODEL 199 5.6.2 MAXIMUM LIKELIHOOD ESTIMATION 200 5.6.3
EXTENSIONS OF ARCH MODELS 200 5.7 SINUSOIDAL REGRESSION MODEL 201 5.8
MIXED MODELS FOR LONGITUDINAL DATA 205 5.9 PROBLEMS AND COMPLEMENTS 208
STATE SPACE MODELS 213 6.1 INTRODUCTION 213 6.1.1 HISTORICAL NOTE 214
6.2 LINEAR GAUSSIAN STATE SPACE MODELS 215 6.2.1 EXAMPLES OF LINEAR
STATE SPACE MODELS 216 6.2.2 ESTIMATION BY KALMAN FILTERING AND
SMOOTHING 218 6.2.3 ESTIMATION IN THE LINEAR GAUSSIAN MODEL 221 6.3
NONLINEAR AND NON-GAUSSIAN STATE SPACE MODELS 223 6.3.1 GENERAL
FILTERING AND SMOOTHING 225 6.3.2 DYNAMIC GENERALIZED LINEAR MODELS 227
6.4 SIMULATION BASED METHODS FOR STATE SPACE MODELS 231 6.4.1 A BRIEF
MCMC TUTORIAL 232 CONTENTS XI 6.4.2 MCMC INFERENCE FOR STATE SPACE
MODELS 233 6.4.3 SEQUENTIAL MONTE CARLO SAMPLING METHODS 237 6.4.4
LIKELIHOOD INFERENCE 240 6.4.5 LONGITUDINAL DATA 241 6.5 KALMAN
FILTERING IN SPACE-TIME DATA 241 6.6 PROBLEMS AND COMPLEMENTS 241 7
PREDICTION AND INTERPOLATION 249 7. 1 INTRODUCTION 249 7.1.1 ELEMENTS OF
STATIONARY RANDOM FIELDS 251 7.1.2 ORDINARY KRIGING 252 7.2 BAYESIAN
SPATIAL PREDICTION 258 7.2.1 AN AUXILIARY GAUSSIAN PROCESS 258 7.2.2 THE
LIKELIHOOD 260 7.2.3 PRIOR AND POSTERIOR OF MODEL PARAMETERS 262 7.2.4
PREDICTION OFZ 0 263 7.2.5 NUMERICAL ALGORITHM FOR THE CASE K = 1 264
7.2.6 NORMALIZING TRANSFORMATIONS 265 7.2.7 SOFTWARE FOR BTG
IMPLEMENTATION 265 7.3 APPLICATIONS OF BTG 267 7.3.1 SPATIAL RAINFALL
PREDICTION 267 7.3.2 COMPARISON WITH KRIGING 274 7.3.3 TIME SERIES
PREDICTION 274 7.3.4 SEASONAL TIME SERIES 278 7.4 PROBLEMS AND
COMPLEMENTS 282 APPENDIX: ELEMENTS OF STATIONARY PROCESSES 285
REFERENCES 297 INDEX 327
|
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author | Kedem, Benjamin 1944- |
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callnumber-first | Q - Science |
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ctrlnum | (OCoLC)50269705 (DE-599)BVBBV016497854 |
dewey-full | 519.55 |
dewey-hundreds | 500 - Natural sciences and mathematics |
dewey-ones | 519 - Probabilities and applied mathematics |
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dewey-search | 519.55 |
dewey-sort | 3519.55 |
dewey-tens | 510 - Mathematics |
discipline | Mathematik Wirtschaftswissenschaften |
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id | DE-604.BV016497854 |
illustrated | Illustrated |
indexdate | 2024-07-09T19:11:12Z |
institution | BVB |
isbn | 0471363553 |
language | English |
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physical | XIV, 337 S. Ill. |
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spelling | Kedem, Benjamin 1944- Verfasser (DE-588)1136292365 aut Regression models for time series analysis Benjamin Kedem, Konstantinos Fokianos Hoboken, N.J. Wiley-Interscience 2002 XIV, 337 S. Ill. txt rdacontent n rdamedia nc rdacarrier Wiley series in probability and statistics Includes biblographical references (p. 297-326) and index Analyse de régression Regressiemodellen gtt Série chronologique Tijdreeksen gtt Regression analysis Time-series analysis Regressionsmodell (DE-588)4127980-3 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 s Regressionsmodell (DE-588)4127980-3 s DE-604 Fokianos, Konstantinos Sonstige oth GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010197118&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
spellingShingle | Kedem, Benjamin 1944- Regression models for time series analysis Analyse de régression Regressiemodellen gtt Série chronologique Tijdreeksen gtt Regression analysis Time-series analysis Regressionsmodell (DE-588)4127980-3 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
subject_GND | (DE-588)4127980-3 (DE-588)4067486-1 |
title | Regression models for time series analysis |
title_auth | Regression models for time series analysis |
title_exact_search | Regression models for time series analysis |
title_full | Regression models for time series analysis Benjamin Kedem, Konstantinos Fokianos |
title_fullStr | Regression models for time series analysis Benjamin Kedem, Konstantinos Fokianos |
title_full_unstemmed | Regression models for time series analysis Benjamin Kedem, Konstantinos Fokianos |
title_short | Regression models for time series analysis |
title_sort | regression models for time series analysis |
topic | Analyse de régression Regressiemodellen gtt Série chronologique Tijdreeksen gtt Regression analysis Time-series analysis Regressionsmodell (DE-588)4127980-3 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
topic_facet | Analyse de régression Regressiemodellen Série chronologique Tijdreeksen Regression analysis Time-series analysis Regressionsmodell Zeitreihenanalyse |
url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010197118&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
work_keys_str_mv | AT kedembenjamin regressionmodelsfortimeseriesanalysis AT fokianoskonstantinos regressionmodelsfortimeseriesanalysis |