Concerns about the efficiency and the reliability of point sampling to estimate change in forest growth variables have been expressed ever since point sampling appeared in the literature more than 60 years ago. Change estimators for point samples based on point-to-tree distance in variable-radius plots were introduced about 30 years ago but are rarely implemented despite easy access to point-to...

We propose an efficient algorithm for principal component analysis (PCA) that is applicable when only the inner product with a given vector is needed. We show that Krylov subspace learning works well both in matrix compression and implicit calculation of the inner product by taking full advantage of the arbitrariness of the seed vector. We apply our algorithm to a PCA-based change-point detecti...

We consider several estimators for the change point in a sequence of independent observations. These are defined as the maximizing points of usually used statistics for nonparametric change point detection problems. Our investigations focus on the non asymptotic behaviour of the proposed estimators for sample sizes commonly observed in practice. We conducted a broad Monte Carlo study to compare...

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Change point problems are referred to detect heterogeneity in temporal or spatial data. They have applications in many areas like DNA sequences, financial time series, signal processing, etc. A large number of techniques have been proposed to tackle the problems. One of the most difficult issues is estimating the number of the change points. As in other examples of model selection, the Bayesian...

Fast multiple change-point segmentation methods, which additionally provide faithful statistical statements on the number, locations and sizes of the segments, have recently received great attention. In this paper, we propose a multiscale segmentation method, FDRSeg, which controls the false discovery rate (FDR) in the sense that the number of false jumps is bounded linearly by the number of tr...

We study the problem of learning two regimes (we have a normal and a prefault regime in mind) based on a train set of non-Markovian observation sequences. Key to the model is that we assume that once the system switches from the normal to the prefault regime it cannot restore and will eventually result in a fault. We refer to the particular setting as semi-supervised since we assume the only in...

Interfacial phase change memory (iPCM) is magnetic sensitive at room temperature while the alloy with same composition is not. The unique property has been thought to be related to the topological insulating property. We discuss the magnetic properties on the point of view of the spatial inversion symmetry of iPCM structures and ferroelectricity of GeTe layers sandwiched by topological insulati...

Identification of a real time of a change in a process, when an out-of-control signal is present is significant. This may reduce costs of defective products as well as the time of exploring and fixing the cause of defects. Another popular topic in the Statistical Process Control (SPC) is profile monitoring, where knowing the distribution of one or more quality characteristics may not be appropr...