Change points estimated by OLSaCL and Kmeans (DGP 2).

NIAID Data Ecosystem2026-03-11 收录

变点检测

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https://figshare.com/articles/dataset/Change_points_estimated_by_OLSaCL_and_Kmeans_DGP_2_/10249079 数据链接链接失效反馈

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Change points estimated by OLSaCL and Kmeans (DGP 2).

创建时间：

2019-11-04

Computationally Efficient Changepoint Detection for a Range of Penalties

变点检测

计算效率

In the multiple changepoint setting, various search methods have been proposed, which involve optimizing either a constrained or penalized cost function over possible numbers and locations of changepo

DataCite Commons2020-09-04 更新90

Mean-structure and Autocorrelation Consistent Covariance Matrix Estimation

非平稳时间序列协方差估计

变点检测

We consider estimation of the asymptotic covariance matrix in non-stationary time series. A non-parametric estimator that is robust against unknown forms of trends and possibly a divergent number of c

Taylor & Francis Group2021-09-29 更新80

Bayesian Detection of Changepoints in Finite-State Markov Chains for Multiple Sequences

变点检测

古气候序列分析

We consider the analysis of sets of categorical sequences consisting of piecewise homogeneous Markov segments. The sequences are assumed to be governed by a common underlying process with segments occ

NIAID Data Ecosystem40

Dataset for: Trend Assessment for Daily Snow Depths with Changepoint Considerations

积雪深度趋势分析

变点检测

This paper develops methods to estimate a long-term trend in a daily snow depth record. The methods use a storage equation model for the daily snow depths that allows for seasonality, support set feat

NIAID Data Ecosystem80

The means and standard deviations of estimation error of break point position.

变点检测

生态统计模型

The means and standard deviations of estimation error of break point position.

NIAID Data Ecosystem60