Fokianos (18 risultati)

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: Good. Former library copy. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New. In English.

Hardback. Condizione: New. A thorough review of the most current regression methods in time series analysis Regression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis. Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a much-needed examination of recent statistical developments. Primary among them is the important class of models known as generalized linear models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data. The authors extend GLM methodology systematically to time series where the primary and covariate data are both random and stochastically dependent. They introduce readers to various regression models developed during the last thirty years or so and summarize classical and more recent results concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series that may be short and/or observed irregularly. Real data applications and further results are presented throughout by means of chapter problems and complements. Notably, the book covers: * Important recent developments in Kalman filtering, dynamic GLMs, and state-space modeling * Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm * Prediction and interpolation * Stationary processes.…

Gebunden. Condizione: New. BENJAMIN KEDEM, PhD, is Professor of Mathematics at the University of Maryland.KONSTANTINOS FOKIANOS, PhD, is Assistant Professor in the Department of Mathematics and Statistics at the University of Cyprus.A thorough review of the most current regressio.

Hardcover. Condizione: Brand New. 1st edition. 320 pages. 9.25x6.00x1.00 inches. In Stock.

Buch. Condizione: Neu. Neuware - A thorough review of the most current regression methods in time series analysisRegression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis.Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a much-needed examination of recent statistical developments. Primary among them is the important class of models known as generalized linear models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data.The authors extend GLM methodology systematically to time series where the primary and covariate data are both random and stochastically dependent. They introduce readers to various regression models developed during the last thirty years or so and summarize classical and more recent results concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series that may be short and/or observed irregularly. Real data applications and further results are presented throughout by means of chapter problems and complements.Notably, the book covers:\* Important recent developments in Kalman filtering, dynamic GLMs, and state-space modeling\* Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm\* Prediction and interpolation\* Stationary processes.…

Hardback. Condizione: New. A thorough review of the most current regression methods in time series analysis Regression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis. Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a much-needed examination of recent statistical developments. Primary among them is the important class of models known as generalized linear models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data. The authors extend GLM methodology systematically to time series where the primary and covariate data are both random and stochastically dependent. They introduce readers to various regression models developed during the last thirty years or so and summarize classical and more recent results concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series that may be short and/or observed irregularly. Real data applications and further results are presented throughout by means of chapter problems and complements. Notably, the book covers: * Important recent developments in Kalman filtering, dynamic GLMs, and state-space modeling * Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm * Prediction and interpolation * Stationary processes.…

Paperback. Condizione: new. Paperback. H: (Helen, the assistant to John Smith, owner of John Smith Industries, a colossal car manufacturer)JS: (John Smith)J: (Jim, the Chief Financial Officer and John Smith's trusted right-hand man)(Helen's office phone rings)H: John Smith's office, how can I help you?JS: Good morning, Helen. It's John.H: Good morning, Mr. Smith. How was your night?JS: Great, thanks. Listen, I need you to take this down-I'm driving, and it's urgent. Ask Jim, the CFO, to transfer $225,000 to the crypto account: xxxxxxxxxxxxx. Got it? Please repeat the account number to confirm.H: Certainly, Mr. Smith. The account number is xxxxxxxxxxxxx.JS: Perfect. Get him to proceed immediately. I'll sign off on it as soon as I'm back. Okay? I'm waiting on the line.H: Of course, Mr. Smith. One moment, please.(Helen puts the call on hold.)H: Mr. Smith, Jim would like to speak with you to verify. Shall I transfer the call?JS: Sure, Helen. Thanks.H: You're welcome, Sir. One moment.(Jim picks up the call)J: Hello? John, is that you? It's Jim. Why didn't you call me directly on my mobile?JS: Hi, Jim. Yes, it's me. My phone's screen broke a little while ago, so I redialed Helen's number. Is that a problem?J: Hahaha, of course not. Should I process this transfer now?JS: Yes, please.J: From your private account or the company's account?JS: Better use the company's account. It's for a new developer I've just recruited from Microsoft. They'll be building our next-generation infotainment software.J: Got it. One moment. Done. Anything else?JS: No, Jim. That's all for now. I've got to go. Talk later.J: Sure thing. Bye, John.JS: Bye.Now, imagine if this call was initiated using deepfake AI technology while the real John Smith was en route to his office. The deepfake could replicate his voice so convincingly that even he might question if it wasn't his own. If ambient traffic noise were layered into the call, it would become nearly impossible for Helen and Jim to detect the fraud. In such a scenario, they could unknowingly transfer significant money to a cybercriminal's anonymous cryptocurrency account.The names and companies mentioned here are purely fictional, a product of the writer's imagination. However, can you be certain such a scenario won't happen shortly? Are we truly prepared to face the challenges posed by deepfake technology?I don't think so.This book is designed to raise awareness about such risks and encourage readers to spread the word before it's too late. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. H: (Helen, the assistant to John Smith, owner of John Smith Industries, a colossal car manufacturer)JS: (John Smith)J: (Jim, the Chief Financial Officer and John Smith's trusted right-hand man)(Helen's office phone rings)H: John Smith's office, how can I help you?JS: Good morning, Helen. It's John.H: Good morning, Mr. Smith. How was your night?JS: Great, thanks. Listen, I need you to take this down-I'm driving, and it's urgent. Ask Jim, the CFO, to transfer $225,000 to the crypto account: xxxxxxxxxxxxx. Got it? Please repeat the account number to confirm.H: Certainly, Mr. Smith. The account number is xxxxxxxxxxxxx.JS: Perfect. Get him to proceed immediately. I'll sign off on it as soon as I'm back. Okay? I'm waiting on the line.H: Of course, Mr. Smith. One moment, please.(Helen puts the call on hold.)H: Mr. Smith, Jim would like to speak with you to verify. Shall I transfer the call?JS: Sure, Helen. Thanks.H: You're welcome, Sir. One moment.(Jim picks up the call)J: Hello? John, is that you? It's Jim. Why didn't you call me directly on my mobile?JS: Hi, Jim. Yes, it's me. My phone's screen broke a little while ago, so I redialed Helen's number. Is that a problem?J: Hahaha, of course not. Should I process this transfer now?JS: Yes, please.J: From your private account or the company's account?JS: Better use the company's account. It's for a new developer I've just recruited from Microsoft. They'll be building our next-generation infotainment software.J: Got it. One moment. Done. Anything else?JS: No, Jim. That's all for now. I've got to go. Talk later.J: Sure thing. Bye, John.JS: Bye.Now, imagine if this call was initiated using deepfake AI technology while the real John Smith was en route to his office. The deepfake could replicate his voice so convincingly that even he might question if it wasn't his own. If ambient traffic noise were layered into the call, it would become nearly impossible for Helen and Jim to detect the fraud. In such a scenario, they could unknowingly transfer significant money to a cybercriminal's anonymous cryptocurrency account.The names and companies mentioned here are purely fictional, a product of the writer's imagination. However, can you be certain such a scenario won't happen shortly? Are we truly prepared to face the challenges posed by deepfake technology?I don't think so.This book is designed to raise awareness about such risks and encourage readers to spread the word before it's too late. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condizione: New. pp. xiv + 337 Illus. This item is printed on demand.

Hardcover. Condizione: Brand New. 1st edition. 320 pages. 9.25x6.00x1.00 inches. In Stock. This item is printed on demand.