Monitoring structural changes with the generalized fluctuation test

TL;DR: It is observed that the more recent the study, the higher the frequency of Landsat time series used and some of the widely-used change detection algorithms were discussed, including thresholding, differencing, segmentation, trajectory classification, and regression.

TL;DR: Strucchange as discussed by the authors is a R package for testing structural change in linear regression models. But it is not suitable for the analysis of regression relationships and does not have the ability to detect structural changes in regression relationships.

TL;DR: In this article, a multi-purpose time-series-based disturbance detection approach is proposed to identify and model stable historical variation to enable change detection within newly acquired data, which can analyse in-situ or satellite data time series of biophysical indicators from local to global scale since it is fast, does not depend on thresholds and does not require time series gap filling.

TL;DR: In this article, structural breaks in the unconditional and conditional mean as well as in the variance and covariance/correlation structure of time series models have been investigated for data exhibiting serial dependence.

Abstract: The classic text for understanding complex statistical probability An Introduction to Probability Theory and Its Applications offers comprehensive explanations to complex statistical problems. Delving deep into densities and distributions while relating critical formulas, processes and approaches, this rigorous text provides a solid grounding in probability with practice problems throughout. Heavy on application without sacrificing theory, the discussion takes the time to explain difficult topics and how to use them. This new second edition includes new material related to the substitution of probabilistic arguments for combinatorial artifices as well as new sections on branching processes, Markov chains, and the DeMoivreLaplace theorem.

TL;DR: In this article, the authors discuss their experience designing and implementing a statistical computing language, which combines what they felt were useful features from two existing computer languages, and they feel that the new language provides advantages in the areas of portability, computational efficiency, memory management, and scope.

TL;DR: In this article, the authors developed the statistical theory for testing and estimating multiple change points in regression models, and several test statistics were proposed to determine the existence as well as the number of change points.

TL;DR: In this article, the authors considered tests for parameter instability and structural change with unknown change point, and the results apply to a wide class of parametric models that are suitable for estimation by generalized method of moments procedures.