An introduction to discrete-valued time series:
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Hoboken, NJ, USA
Wiley
2018
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis Inhaltsverzeichnis Klappentext |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | xv, 284 Seiten Diagramme |
| ISBN: | 9781119096962 |
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MARC
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| 245 | 1 | 0 | |a An introduction to discrete-valued time series |c Christian H. Weiss |
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| adam_text |
AN INTRODUCTION TO DISCRETE-VALUED TIME SERIES
/ WEISS, CHRISTIAN H.YYD1977-YYEAUTHOR
: 2017
TABLE OF CONTENTS / INHALTSVERZEICHNIS
A FIRST APPROACH FOR MODELING TIME SERIES OF COUNTS : THE THINNING-BASED
INAR (1) MODEL
FURTHER THINNING-BASED MODELS FOR COUNT TIME SERIES
INGARCH MODELS FOR COUNT TIME SERIES
FURTHER MODELS FOR COUNT TIME SERIES
ANALYZING CATEGORICAL TIME SERIES
MODELS FOR CATEGORICAL TIME SERIES
CONTROL CHARTS FOR COUNT PROCESSES
CONTROL CHARTS FOR CATEGORICAL PROCESSES
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
Contents
Preface xi
About the Companion Website xv
1 Introduction 1
Parti Count Time Series 9
2 A First Approach for Modeling Time Series of Counts: The
Thinning-based INARfl) Model 11
2.0 Preliminaries: Notation and Characteristics of Count
Distributions 11
2.1 The INAR(l) Model for Time-dependent Counts 16
2.1.1 Definition and Basic Properties 17
2.1.2 The Poisson INAR(l) Model 20
2.1.3 INAR(l) Models with More General Innovations 22
2.2 Approaches for Parameter Estimation 26
2.2.1 Method of Moments 26
2.2.2 Maximum Likelihood Estimation 28
2.3 Model Identification 29
2.4 Checking for Model Adequacy 32
2.5 A Real-data Example 34
2.6 Forecasting of INAR(l) Processes 37
3 Further Thinning-based Models for Count Time Series 43
3.1 Higher-order INARM A Models 43
3.2 Alternative Thinning Concepts 54
3.3 The Binomial AR Model 59
3.4 Multivariate INARMA Models 64
Contents
4 INGARCH Models for Count Time Series 73
4.1 Poisson Autoregression 73
4.2 Further Types of INGARCH Models 85
4.3 Multivariate INGARCH Models 93
5 Further Models for Count Time Series 95
5.1 Regression Models 95
5.2 Hidden-Markov Models 107
5.3 Discrete ARM A Models 116
Part II Categorical Time Series 119
6 Analyzing Categorical Time Series 121
6.1 Introduction to Categorical Time Series Analysis 122
6.2 Marginal Properties of Categorical Time Series 126
6.3 Serial Dependence of Categorical Time Series 128
7 Models for Categorical Time Series 133
7.1 Parsimoniously Parametrized Markov Models 133
7.2 Discrete ARMA Models 139
7.3 Hidden-Markov Models 146
7.4 Regression Models 151
Part III Monitoring Discrete-Valued Processes 161
8 Control Charts for Count Processes 163
8.1 Introduction to Statistical Process Control 163
8.2 Shewhart Charts for Count Processes 165
8.2.1 Shewhart Charts for i.i.d. Counts 166
8.2.2 Shewhart Charts for Markov-Dependent Counts 171
8.3 Advanced Control Charts for Count Processes 177
8.3.1 CUSUM Charts for i.i.d. Counts 178
8.3.2 CUSUM Charts for Markov-dependent Counts 182
8.3.3 EWMA Charts for Count Processes 186
9 Control Charts for Categorical Processes 193
9.1 Sample-based Monitoring of Categorical Processes 194
9.1.1 Sample-based Monitoring: Binary Case 194
9.1.2 Sample-based Monitoring: Categorical Case 198
9.2 Continuously Monitoring Categorical Processes 203
9.2.1 Continuous Monitoring: Binary Case 203
9.2.2 Continuous Monitoring: Categorical Case 209
Part IV Appendices 213
A Examples of Count Distributions 215
A.l Count Models for an Infinite Range 215
A.2 Count Models for a Finite Range 221
A. 3 Multivariate Count Models 223
B Basics about Stochastic Processes and Time Series 229
B. l Stochastic Processes: Basic Terms and Concepts 229
B.2 Discrete-Valued Markov Chains 233
B.2.1 Basic Terms and Concepts 233
B.2.2 Stationary Markov Chains 236
B.3 ARMA Models: Definition and Properties 238
B.4 Further Selected Models for Continuous-valued Time Series 243
B.4.1 GARCH Models 243
B. 4.2 VARMA Models 245
C Computational Aspects 249
C. l Some Comments about the Use of R 250
C.2 List of R Codes 253
C.3 List of Datasets 256
References 257
List of Acronyms 275
List of Notations 277
Index 279
A much-needed introduction to the field of discrete-valued time series.
with a focus on count-data time series
Time series analysis is an essential tool in a wide array of fieids. including business, economics,
computer science, epidemiology, finance, manufacturing and meteorology, to name just a few.
Despite growing interest in discrete-valued time series - especially those arising from counting
specific objects or events at specified times - most books on time series give short shrift to
that increasingly important subject area. An Introduction to Discrete-Valued Time Series seeks
to rectify that state of affairs by providing a much needed introduction to discrete-valued time
series, with particular focus on count-data time series.
The main focus of this book is on modeling. Throughout, numerous examples are provided
illustrating models currently used in discrete-valued time senes applications. Statistical
process control, including various control charts (such as cumulative sum control charts- and
performance evaluation are treated at length.
This book:
Provides a balanced presentation of theory and practice, exploring ootii ca
and integer-valued series
Covers common models for time series of counts as well as for categorical time
series, and works out their most important stochastic properties
Addresses statistical approaches for analyzing discrete-valued time series and
illustrates their implementation with numerous data examples
An introduction to Discrete-Valued Time Series is a valucible working resource for res
■ers
and practitioners in a broad range of fields, including statistics, data science, machine
learning ano engineering, it will also be of interest to postgraduate students In statistics,
mathematics and economics. |
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| spelling | Weiß, Christian 1977- Verfasser (DE-588)132130149 aut An introduction to discrete-valued time series Christian H. Weiss Hoboken, NJ, USA Wiley 2018 xv, 284 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 s DE-604 Erscheint auch als Online-Ausgabe, pdf 978-1-119-09698-6 Erscheint auch als Online-Ausgabe, epub 978-1-119-09699-3 LoC Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030104564&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030104564&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030104564&sequence=000004&line_number=0003&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Weiß, Christian 1977- An introduction to discrete-valued time series Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4067486-1 |
| title | An introduction to discrete-valued time series |
| title_auth | An introduction to discrete-valued time series |
| title_exact_search | An introduction to discrete-valued time series |
| title_full | An introduction to discrete-valued time series Christian H. Weiss |
| title_fullStr | An introduction to discrete-valued time series Christian H. Weiss |
| title_full_unstemmed | An introduction to discrete-valued time series Christian H. Weiss |
| title_short | An introduction to discrete-valued time series |
| title_sort | an introduction to discrete valued time series |
| topic | Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | Zeitreihenanalyse |
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