未读消息消息

店铺

我的订单收藏

拍卖

拍卖交易我的竞拍收藏

我的好友资金账户

客服 |

帮助中心 9:00-20:30 在线留言

客服电话

010-89648155

服务时间

客服咨询 8:00-21:00

纠纷处理 9:00-21:00

图书审核 9:00-18:00

监督与建议

请选择

手机孔网

RegressionModelsforTimeSeriesAnalysis

RegressionModelsforTimeSeriesAnalysis

分享

作者: Benjamin Kedem 著 , Konstantinos Fokianos 著

出版社: Wiley-Interscience

出版时间: 2002-08

ISBN: 9780471363552

定价: 1863.70

装帧: 精装

页数: 360页

正文语种: 英语

丛书: WileySeriesinProabilityandStatistics

分类: 外文古旧书>英文书>经济

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 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.
内容简介:
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
作者简介:
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.

查看详情

相关分类

Literature&Fiction文学与虚构 ScienceFiction&Fantasy科幻与玄幻 Romance浪漫小说 Mystery&Thrillers神秘与惊悚 Teens青少年读物 Biographies&Memoirs传记与自传 Comics&GraphicNovels动漫与绘画小说 ELT英语学习 Reference参考书 StandardTest标准考试 Business&Investing商业与投资 Self-Help励志自助

目前没有书店销售此书