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Introduction to Time Series Modeling
  • Language: en
  • Pages: 315

Introduction to Time Series Modeling

  • Type: Book
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  • Published: 2010-04-21
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  • Publisher: CRC Press

In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very im

Information Criteria and Statistical Modeling
  • Language: en
  • Pages: 282

Information Criteria and Statistical Modeling

Statistical modeling is a critical tool in scientific research. This book provides comprehensive explanations of the concepts and philosophy of statistical modeling, together with a wide range of practical and numerical examples. The authors expect this work to be of great value not just to statisticians but also to researchers and practitioners in various fields of research such as information science, computer science, engineering, bioinformatics, economics, marketing and environmental science. It’s a crucial area of study, as statistical models are used to understand phenomena with uncertainty and to determine the structure of complex systems. They’re also used to control such systems, as well as to make reliable predictions in various natural and social science fields.

Introduction to Time Series Modeling with Applications in R
  • Language: en
  • Pages: 332

Introduction to Time Series Modeling with Applications in R

  • Type: Book
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  • Published: 2020-08-10
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  • Publisher: CRC Press

Praise for the first edition: [This book] reflects the extensive experience and significant contributions of the author to non-linear and non-Gaussian modeling. ... [It] is a valuable book, especially with its broad and accessible introduction of models in the state-space framework. –Statistics in Medicine What distinguishes this book from comparable introductory texts is the use of state-space modeling. Along with this come a number of valuable tools for recursive filtering and smoothing, including the Kalman filter, as well as non-Gaussian and sequential Monte Carlo filters. –MAA Reviews Introduction to Time Series Modeling with Applications in R, Second Edition covers numerous station...

Model-Based Monitoring and Statistical Control
  • Language: en
  • Pages: 467

Model-Based Monitoring and Statistical Control

  • Type: Book
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  • Published: 2024-06-11
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  • Publisher: CRC Press

Available in English for the first time, this classic and influential book by the late Kohei Ohtsu presents real examples of ships in motion under irregular ocean waves, how to understand the characteristics of fluctuations of stochastic phenomena through spectral analysis methods and statistical modeling. It also explains how to realize prediction and optimal control based on time series models. In recent years, the need to improve safety and reduce environmental impact in ship operations has been increasing, and the statistical methods presented in this book will be increasingly needed in the future. In addition, the recent development of innovative AI technology and highspeed communicatio...

Smoothness Priors Analysis of Time Series
  • Language: en
  • Pages: 265

Smoothness Priors Analysis of Time Series

Smoothness Priors Analysis of Time Series addresses some of the problems of modeling stationary and nonstationary time series primarily from a Bayesian stochastic regression "smoothness priors" state space point of view. Prior distributions on model coefficients are parametrized by hyperparameters. Maximizing the likelihood of a small number of hyperparameters permits the robust modeling of a time series with relatively complex structure and a very large number of implicitly inferred parameters. The critical statistical ideas in smoothness priors are the likelihood of the Bayesian model and the use of likelihood as a measure of the goodness of fit of the model. The emphasis is on a general state space approach in which the recursive conditional distributions for prediction, filtering, and smoothing are realized using a variety of nonstandard methods including numerical integration, a Gaussian mixture distribution-two filter smoothing formula, and a Monte Carlo "particle-path tracing" method in which the distributions are approximated by many realizations. The methods are applicable for modeling time series with complex structures.

The Making of Statisticians
  • Language: en
  • Pages: 260

The Making of Statisticians

Like many other scientists, I have long been interested in history. I enjoy reading about the minutiae of its daily unfolding: the coinage, food, clothes, games, literature and habits which characterize a people. I am carried away by the broad sweep of its major events: the wars, famines, migrations, reforms, political swings and scientific advances which shape a society. I know that historians value autobiographical accounts as part of the basic material from which the stuff of history is distilled; this should apply no less to statistical than to political or social history. Modem statistics is a relatively young science; it was while pondering this fact sometime in 1980 that I realized th...

Statistical Methods in Control & Signal Processing
  • Language: en
  • Pages: 574

Statistical Methods in Control & Signal Processing

  • Type: Book
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  • Published: 2018-10-08
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  • Publisher: CRC Press

Presenting statistical and stochastic methods for the analysis and design of technological systems in engineering and applied areas, this work documents developments in statistical modelling, identification, estimation and signal processing. The book covers such topics as subspace methods, stochastic realization, state space modelling, and identification and parameter estimation.

The Practice of Time Series Analysis
  • Language: en
  • Pages: 388

The Practice of Time Series Analysis

A collection of applied papers on time series, appearing here for the first time in English. The applications are primarily found in engineering and the physical sciences.

Robust Technology with Analysis of Interference in Signal Processing
  • Language: en
  • Pages: 204

Robust Technology with Analysis of Interference in Signal Processing

Robust Technology with Analysis of Interference in Signal Processing discusses for the first time the theoretical fundamentals and algorithms of analysis of noise as an information carrier. On their basis the robust technology of noisy signals processing is developed. This technology can be applied to solving the problems of control, identification, diagnostics, and pattern recognition in petrochemistry, energetics, geophysics, medicine, physics, aviation, and other sciences and industries. The text explores the emergent possibility of forecasting failures on various objects, in conjunction with the fact that failures follow the hidden microchanges revealed via interference estimates. This monograph is of interest to students, postgraduates, engineers, scientific associates and others who are concerned with the processing of measuring information on computers.

Discovery Science
  • Language: en
  • Pages: 515

Discovery Science

This book constitutes the refereed proceedings of the 6th International Conference on Discovery Science, DS 2003, held in Sapporo, Japan in October 2003. The 18 revised full papers and 29 revised short papers presented together with 3 invited papers and abstracts of 2 invited talks were carefully reviewed and selected from 80 submissions. The papers address all current issues in discovery science including substructure discovery, Web navigation patterns discovery, graph-based induction, time series data analysis, rough sets, genetic algorithms, clustering, genome analysis, chaining patterns, association rule mining, classification, content based filtering, bioinformatics, case-based reasoning, text mining, Web data analysis, and more.