LEADER 04058nam 2200517 450 001 9910270888103321 005 20200520144314.0 010 $a1-119-09698-7 010 $a1-119-09701-0 035 $a(CKB)4330000000008497 035 \\$a(Safari)9781119096962 035 $a(OCoLC)1031215700 035 $a(Au-PeEL)EBL5183772 035 $a(CaPaEBR)ebr11480884 035 $a(OCoLC)1004957494 035 $a(CaSebORM)9781119096962 035 $a(MiAaPQ)EBC5183772 035 $a(EXLCZ)994330000000008497 100 $a20180109h20182018 uy 0 101 0 $aeng 135 $aurunu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aAn introduction to discrete-valued time series /$fChristian Weiss 205 $a1st edition 210 1$aHoboken, New Jersey :$cWiley,$d2018. 210 4$dİ2018 215 $a1 online resource (1 volume) $cillustrations 311 $a1-119-09696-0 320 $aIncludes bibliographical references and index. 327 $aA 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. 330 $aA 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 fields, 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. This book 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 series applications. Statistical process control, including various control charts (such as cumulative sum control charts), and performance evaluation are treated at length. Classic approaches like ARMA models and the Box-Jenkins program are also featured with the basics of these approaches summarized in an Appendix. In addition, data examples, with all relevant R code, are available on a companion website. Provides a balanced presentation of theory and practice, exploring both categorical 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 Covers classical approaches such as ARMA models, Box-Jenkins program and how to generate functions Includes dataset examples with all necessary R code provided on a companion website An Introduction to Discrete-Valued Time Series is a valuable working resource for researchers and practitioners in a broad range of fields, including statistics, data science, machine learning, and engineering. It will also be of interest to postgraduate students in statistics, mathematics and economics. 606 $aTime-series analysis 606 $aDiscrete-time systems$xMathematical models 615 0$aTime-series analysis. 615 0$aDiscrete-time systems$xMathematical models. 676 $a519.5/5 700 $aWeiss$b Christian H.$f1977-$0868333 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910270888103321 996 $aAn introduction to discrete-valued time series$91938437 997 $aUNINA