標(biāo)題: Titlebook: Change Point Analysis for Time Series; Lajos Horváth,Gregory Rice Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusi [打印本頁(yè)] 作者: 壓榨機(jī) 時(shí)間: 2025-3-21 18:47
書目名稱Change Point Analysis for Time Series影響因子(影響力)
書目名稱Change Point Analysis for Time Series影響因子(影響力)學(xué)科排名
書目名稱Change Point Analysis for Time Series網(wǎng)絡(luò)公開(kāi)度
書目名稱Change Point Analysis for Time Series網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
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書目名稱Change Point Analysis for Time Series被引頻次學(xué)科排名
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書目名稱Change Point Analysis for Time Series讀者反饋
書目名稱Change Point Analysis for Time Series讀者反饋學(xué)科排名
作者: CHOP 時(shí)間: 2025-3-21 22:38 作者: Heresy 時(shí)間: 2025-3-22 03:06
Lajos Horváth,Gregory RiceProvides a comprehensive review of asymptotic methods in change point analysis for time series.Extends classical change point methods to the modern settings of high--dimensional, functional, and heter作者: Parallel 時(shí)間: 2025-3-22 08:13 作者: 極小 時(shí)間: 2025-3-22 10:15
https://doi.org/10.1007/978-981-16-2442-1A crucial step in approximating the distribution of the CUSUM statistics introduced in Chaps. . and . is estimating the variance parameter . describing the limiting variance of the partial sum of the observations under Assumption ..作者: Limited 時(shí)間: 2025-3-22 13:40 作者: Limited 時(shí)間: 2025-3-22 19:15
https://doi.org/10.1007/3-540-28333-1oaches to detect such a change point lead to the consideration of weighted functionals of the cumulative sum (CUSUM) processes computed from the observed data. As such, we begin by developing a comprehensive asymptotic theory for CUSUM processes under conditions that allow for serial dependence in t作者: 向宇宙 時(shí)間: 2025-3-22 21:26 作者: 初次登臺(tái) 時(shí)間: 2025-3-23 03:03 作者: SLAG 時(shí)間: 2025-3-23 07:37
The Identified Local Characteristics,ve generally taken into consideration potential serial dependence in the observations under study, in this chapter we are concerned with detecting change points in the parameters for models specifically designed to capture the serial dependence structure of a time series.作者: MORT 時(shí)間: 2025-3-23 10:57
https://doi.org/10.1007/978-981-16-2442-1ich the goal is to conduct change point analysis retrospectively on an observed series. In this chapter, we shift our focus to sequential or “online” change point detection methods. These aim to detect a change point in the data generating process, relative to a stable training or historical sample,作者: FOLD 時(shí)間: 2025-3-23 14:31
https://doi.org/10.1007/978-981-16-2442-1we change our notation slightly to denote such multivariate time series data as . where we think of . and . as denoting “time”, and . denotes the dimension or number of “cross-sectional units” that we observe. For example, such data might comprise real valued observations of . financial or economic 作者: Irksome 時(shí)間: 2025-3-23 21:32 作者: humectant 時(shí)間: 2025-3-23 23:24
Change Point Analysis for Time Series978-3-031-51609-2Series ISSN 0172-7397 Series E-ISSN 2197-568X 作者: 彎曲道理 時(shí)間: 2025-3-24 02:31 作者: FLIRT 時(shí)間: 2025-3-24 08:42
https://doi.org/10.1007/978-981-16-2442-1we change our notation slightly to denote such multivariate time series data as . where we think of . and . as denoting “time”, and . denotes the dimension or number of “cross-sectional units” that we observe. For example, such data might comprise real valued observations of . financial or economic time series over . time units.作者: Locale 時(shí)間: 2025-3-24 13:58 作者: Cytology 時(shí)間: 2025-3-24 17:35
High-Dimensional and Panel Data,we change our notation slightly to denote such multivariate time series data as . where we think of . and . as denoting “time”, and . denotes the dimension or number of “cross-sectional units” that we observe. For example, such data might comprise real valued observations of . financial or economic time series over . time units.作者: Celiac-Plexus 時(shí)間: 2025-3-24 19:31
Book 2024r models. The book primarily focuses on asymptotic theory and practical applications of change point analysis. The methods discussed in the book go beyond the traditional change point methods for univariate and multivariate series. It also explores techniques for handling heteroscedastic series, hig作者: CHOKE 時(shí)間: 2025-3-25 03:13 作者: tangle 時(shí)間: 2025-3-25 05:23 作者: Lice692 時(shí)間: 2025-3-25 08:39 作者: 粘土 時(shí)間: 2025-3-25 13:14 作者: 修改 時(shí)間: 2025-3-25 19:42
0172-7397 modern settings of high--dimensional, functional, and heterThis volume provides a comprehensive survey that covers various modern methods used for detecting and estimating change points in time series and their models. The book primarily focuses on asymptotic theory and practical applications of ch作者: 陳腐的人 時(shí)間: 2025-3-25 22:54 作者: 彩色 時(shí)間: 2025-3-26 00:50 作者: 彎曲道理 時(shí)間: 2025-3-26 08:09
Cumulative Sum Processes,ved data. As such, we begin by developing a comprehensive asymptotic theory for CUSUM processes under conditions that allow for serial dependence in the observations. This includes a careful analysis of how weights applied to the CUSUM process affect the limiting distribution of its functionals, and extensions to multivariate observations.作者: terazosin 時(shí)間: 2025-3-26 09:37
Regression Models,parametric regression model. In this case a change in the relationship may be characterized by a change in the model parameters. This chapter is devoted to the development of asymptotic methods to perform change point analysis in the context of regression models.作者: phytochemicals 時(shí)間: 2025-3-26 12:48
Change Point Analysis of the Mean,points in the series, the functionals of the CUSUM process that we have considered should be consistent in the sense that they diverge in probability to positive infinity as the sample size grows. One goal of this chapter is to carefully quantify the asymptotic behaviour of the CUSUM process in the presence of change points.作者: 修飾語(yǔ) 時(shí)間: 2025-3-26 19:10 作者: Talkative 時(shí)間: 2025-3-26 22:53 作者: 音的強(qiáng)弱 時(shí)間: 2025-3-27 04:21 作者: SPALL 時(shí)間: 2025-3-27 08:22
Regression Models,e, or if instead it appears to change. A simple framework to address questions of this type is when the variables are related to each other through a parametric regression model. In this case a change in the relationship may be characterized by a change in the model parameters. This chapter is devot作者: 過(guò)份好問(wèn) 時(shí)間: 2025-3-27 09:49 作者: octogenarian 時(shí)間: 2025-3-27 15:09 作者: defile 時(shí)間: 2025-3-27 17:46 作者: CLASH 時(shí)間: 2025-3-28 01:29
Functional Data, include data that can be imagined as curves or surfaces. A general object of this type is termed a functional data object. When functional data are observed sequentially over time, they are referred to as functional time series. Usually the data that are available in this setting are discrete measu作者: judicial 時(shí)間: 2025-3-28 02:45 作者: 可憎 時(shí)間: 2025-3-28 09:31
0172-7397 xercises.Additionally, the book provides a discussion of background literature, allowing readers to explore further resources for in-depth knowledge on specific topics. Overall, "Change Point Analysis for Time 978-3-031-51611-5978-3-031-51609-2Series ISSN 0172-7397 Series E-ISSN 2197-568X 作者: 自由職業(yè)者 時(shí)間: 2025-3-28 14:13
