Introduction to State Space Time Series Analysis 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Introduction to State Space Time Series Analysis

簡體網頁||繁體網頁
Siem Jan Koopman 作者
OUP Oxford
譯者
2007-7-17 出版日期
190 頁數
GBP 48.49 價格
Hardcover
叢書系列
9780199228874 圖書編碼

Introduction to State Space Time Series Analysis 在線電子書 圖書標籤: Time_Series  狀態空間  時間序列  state  space  Statistics  NLP文庫  Econometrics   


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Introduction to State Space Time Series Analysis 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Introduction to State Space Time Series Analysis 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Introduction to State Space Time Series Analysis 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



Introduction to State Space Time Series Analysis 在線電子書 用戶評價

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術語、概念和技術處理的內涵並沒有講述清楚,內容過於單薄,總的來說過於蜻蜓點水瞭;不值一讀~~~

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Nice nontechnical introduction. It's enjoyable to go through the complete analysis of several data sets step by step.

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SS是現代TS研究的WORKHORSE,具有極強的兼容性和處理上的便捷……

評分

SS是現代TS研究的WORKHORSE,具有極強的兼容性和處理上的便捷……

評分

SS是現代TS研究的WORKHORSE,具有極強的兼容性和處理上的便捷……

Introduction to State Space Time Series Analysis 在線電子書 著者簡介


Introduction to State Space Time Series Analysis 在線電子書 著者簡介


Introduction to State Space Time Series Analysis 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Introduction to State Space Time Series Analysis 在線電子書 圖書描述

Providing a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. The only background required in order to understand the material presented in the book is a basic knowledge of classical linear regression models, of which a brief review is provided to refresh the reader's knowledge. Also, a few sections assume familiarity with matrix algebra, however, these sections may be skipped without losing the flow of the exposition. The book offers a step by step approach to the analysis of the salient features in time series such as the trend, seasonal, and irregular components. Practical problems such as forecasting and missing values are treated in some detail. This useful book will appeal to practitioners and researchers who use time series on a daily basis in areas such as the social sciences, quantitative history, biology and medicine. It also serves as an accompanying textbook for a basic time series course in econometrics and statistics, typically at an advanced undergraduate level or graduate level.

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