Yuvraj sunecher (40 risultati)

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Paperback. Condizione: New.

Condizione: New.

hardcover. Condizione: Very Good.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Hardback. Condizione: New. This book tackles the complexities of integer-valued time series analysis, focusing on over-dispersion, excess zeros, and non-stationarity. It explores high-ordered INAR(p) models with diverse thinning mechanisms and innovation distributions, finding CML superior for inference. Addressing periodic-ity, harmonic functions are introduced for COVID-19 data. Novel BINAR (1) models with BPWE and SPWE innovations are applied to stock transactions, while new BPGL and SPGL bivariate distributions analyze crime data.The book derives methodologies, tests performance via simulation, and provides real-life applications, filling a gap in existing literature. This comprehensive work significantly advances the field of integer-valued time series analysis by addressing key challenges such as over-dispersion and periodicity. The detailed exploration of high-ordered INAR(p) models under various thinning mechanisms and innovation distributions provides valuable insights into their performance, with the clear outperformance of the CML inferential method offering practical guidance for researchers. The innovative incorporation of harmonic functions to model the periodic nature of the COVID-19 data in Mauritius demonstrates a crucial adaptation to real-world phenomena. Furthermore, the development and application of novel BINAR (1) models and bivariate distributions like BPGL and SPGL expand the analytical toolkit for understanding the relationships between multiple integer-valued series, exemplified by their application to stock transactions and crime data. By deriving new methodologies, rigorously testing their performance through simulation, and illustrating their utility with diverse real-life applications, this book offers substantial theoretical and practical contributions to the field, addressing limitations in existing literature.The target audience includes researchers, statisticians, and practitioners working with count data and time series analysis in fields like econometrics, finance, epidemiology, and criminology.…

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Hardcover. Condizione: Brand New. 148 pages. 9.18x6.12x9.45 inches. In Stock.

Condizione: New.

Condizione: New.

Condizione: New.

Paperback. Condizione: New.

Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

Hardback. Condizione: New. This book proposes some novel models based on the autoregressive and moving average structures under various distributional assumptions of the innovation series for analysing non-stationary bivariate time series of counts.Time series of count responses are recorded for different correlated variables which may be marginally dispersed relative to their means, may exhibit different levels of dispersion and may be commonly influenced by one or more dynamic explanatory variables. Analysis of such type of bivariate time series data is quite challenging and the challenge mounts up further if these time series are non-stationary. This book proposes some bivariate models that allow for different levels of dispersion as well as non-stationarity. Specifically, BINAR(1) and BINMA(1) models under Poisson, NB and COM-Poisson innovations are constructed and tested. Another important contribution of this book is in developing a novel estimation procedure for estimating the parameters of the proposed BINAR(1) and BINMA(1) models. Hence, a new estimation approach based on the GQL is proposed. Monte-Carlo simulations are implemented to assess the performance of the GQL. In some simple cases of stationarity, we also compare the GQL with the other estimation techniques such as CMLE and FGLS.This book is a useful resource for undergraduate students, postgraduate students, researchers and academics in the field of time series models.…

Hardback. Condizione: New. This book tackles the complexities of integer-valued time series analysis, focusing on over-dispersion, excess zeros, and non-stationarity. It explores high-ordered INAR(p) models with diverse thinning mechanisms and innovation distributions, finding CML superior for inference. Addressing periodic-ity, harmonic functions are introduced for COVID-19 data. Novel BINAR (1) models with BPWE and SPWE innovations are applied to stock transactions, while new BPGL and SPGL bivariate distributions analyze crime data.The book derives methodologies, tests performance via simulation, and provides real-life applications, filling a gap in existing literature. This comprehensive work significantly advances the field of integer-valued time series analysis by addressing key challenges such as over-dispersion and periodicity. The detailed exploration of high-ordered INAR(p) models under various thinning mechanisms and innovation distributions provides valuable insights into their performance, with the clear outperformance of the CML inferential method offering practical guidance for researchers. The innovative incorporation of harmonic functions to model the periodic nature of the COVID-19 data in Mauritius demonstrates a crucial adaptation to real-world phenomena. Furthermore, the development and application of novel BINAR (1) models and bivariate distributions like BPGL and SPGL expand the analytical toolkit for understanding the relationships between multiple integer-valued series, exemplified by their application to stock transactions and crime data. By deriving new methodologies, rigorously testing their performance through simulation, and illustrating their utility with diverse real-life applications, this book offers substantial theoretical and practical contributions to the field, addressing limitations in existing literature.The target audience includes researchers, statisticians, and practitioners working with count data and time series analysis in fields like econometrics, finance, epidemiology, and criminology.…

Condizione: New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

Hardcover. Condizione: Brand New. 336 pages. 9.18x6.12x9.21 inches. In Stock.

Hardback. Condizione: New. This book proposes some novel models based on the autoregressive and moving average structures under various distributional assumptions of the innovation series for analysing non-stationary bivariate time series of counts.Time series of count responses are recorded for different correlated variables which may be marginally dispersed relative to their means, may exhibit different levels of dispersion and may be commonly influenced by one or more dynamic explanatory variables. Analysis of such type of bivariate time series data is quite challenging and the challenge mounts up further if these time series are non-stationary. This book proposes some bivariate models that allow for different levels of dispersion as well as non-stationarity. Specifically, BINAR(1) and BINMA(1) models under Poisson, NB and COM-Poisson innovations are constructed and tested. Another important contribution of this book is in developing a novel estimation procedure for estimating the parameters of the proposed BINAR(1) and BINMA(1) models. Hence, a new estimation approach based on the GQL is proposed. Monte-Carlo simulations are implemented to assess the performance of the GQL. In some simple cases of stationarity, we also compare the GQL with the other estimation techniques such as CMLE and FGLS.This book is a useful resource for undergraduate students, postgraduate students, researchers and academics in the field of time series models.…

Hardcover. Condizione: new. Hardcover. This book tackles the complexities of integer-valued time series analysis, focusing on over-dispersion, excess zeros, and non-stationarity. It explores high-ordered INAR(p) models with diverse thinning mechanisms and innovation distributions, finding CML superior for inference. Addressing periodic-ity, harmonic functions are introduced for COVID-19 data. Novel BINAR (1) models with BPWE and SPWE innovations are applied to stock transactions, while new BPGL and SPGL bivariate distributions analyze crime data.The book derives methodologies, tests performance via simulation, and provides real-life applications, filling a gap in existing literature. This comprehensive work significantly advances the field of integer-valued time series analysis by addressing key challenges such as over-dispersion and periodicity. The detailed exploration of high-ordered INAR(p) models under various thinning mechanisms and innovation distributions provides valuable insights into their performance, with the clear outperformance of the CML inferential method offering practical guidance for researchers. The innovative incorporation of harmonic functions to model the periodic nature of the COVID-19 data in Mauritius demonstrates a crucial adaptation to real-world phenomena. Furthermore, the development and application of novel BINAR (1) models and bivariate distributions like BPGL and SPGL expand the analytical toolkit for understanding the relationships between multiple integer-valued series, exemplified by their application to stock transactions and crime data. By deriving new methodologies, rigorously testing their performance through simulation, and illustrating their utility with diverse real-life applications, this book offers substantial theoretical and practical contributions to the field, addressing limitations in existing literature.The target audience includes researchers, statisticians, and practitioners working with count data and time series analysis in fields like econometrics, finance, epidemiology, and criminology. This book tackles the complexities of integer-valued time series analysis, focusing on over-dispersion, excess zeros, and non-stationarity. It explores high-ordered INAR(p) models with diverse thinning mechanisms and innovation distributions, finding CML superior for inference. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Hardcover. Condizione: new. Hardcover. This book tackles the complexities of integer-valued time series analysis, focusing on over-dispersion, excess zeros, and non-stationarity. It explores high-ordered INAR(p) models with diverse thinning mechanisms and innovation distributions, finding CML superior for inference. Addressing periodic-ity, harmonic functions are introduced for COVID-19 data. Novel BINAR (1) models with BPWE and SPWE innovations are applied to stock transactions, while new BPGL and SPGL bivariate distributions analyze crime data.The book derives methodologies, tests performance via simulation, and provides real-life applications, filling a gap in existing literature. This comprehensive work significantly advances the field of integer-valued time series analysis by addressing key challenges such as over-dispersion and periodicity. The detailed exploration of high-ordered INAR(p) models under various thinning mechanisms and innovation distributions provides valuable insights into their performance, with the clear outperformance of the CML inferential method offering practical guidance for researchers. The innovative incorporation of harmonic functions to model the periodic nature of the COVID-19 data in Mauritius demonstrates a crucial adaptation to real-world phenomena. Furthermore, the development and application of novel BINAR (1) models and bivariate distributions like BPGL and SPGL expand the analytical toolkit for understanding the relationships between multiple integer-valued series, exemplified by their application to stock transactions and crime data. By deriving new methodologies, rigorously testing their performance through simulation, and illustrating their utility with diverse real-life applications, this book offers substantial theoretical and practical contributions to the field, addressing limitations in existing literature.The target audience includes researchers, statisticians, and practitioners working with count data and time series analysis in fields like econometrics, finance, epidemiology, and criminology. This book tackles the complexities of integer-valued time series analysis, focusing on over-dispersion, excess zeros, and non-stationarity. It explores high-ordered INAR(p) models with diverse thinning mechanisms and innovation distributions, finding CML superior for inference. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…