Multiple Testing of Local Extrema for Detection of Structural Breaks in Piecewise Linear Models

In this paper, we propose a new generic method for detecting the number and locations of structural breaks or change points in piecewise linear models under stationary Gaussian noise. Our method transforms the change point detection problem into identifying local extrema (local maxima and local minima) through kernel smoothing and differentiation of the data sequence. By computing p-values for all local extrema based on peak height distributions of smooth Gaussian processes, we utilize the Benjamini-Hochberg procedure to identify significant local extrema as the detected change points. Our method can distinguish between two types of change points: continuous breaks (Type I) and jumps (Type II). We study three scenarios of piecewise linear signals, namely pure Type I, pure Type II and a mixture of Type I and Type II change points. The results demonstrate that our proposed method ensures asymptotic control of the False Discover Rate (FDR) and power consistency, as sequence length, slope changes, and jump size increase. Furthermore, compared to traditional change point detection methods based on recursive segmentation, our approach only requires a single test for all candidate local extrema, thereby achieving the smallest computational complexity proportionate to the data sequence length. Additionally, numerical studies illustrate that our method maintains FDR control and power consistency, even in non-asymptotic cases when the size of slope changes or jumps is not large. We have implemented our method in the R package "dSTEM" (available from https://cran.r-project.org/web/packages/dSTEM)

Screening effects on field emission from arrays of (5,5) carbon nanotubes: Quantum-mechanical simulation

The simulation of field electron emission from arrays of micrometer-long open-ended (5, 5) carbon nanotubes is performed in the framework of quantum theory of many electrons. It is found that the applied external field is strongly screened when the spacing distance is shorter than the length of the carbon nanotubes. The optimal spacing distance is two to three times of the nanotube length, slightly depending on the applied external fields. The electric screening can be described by a factor that is a exponential function of the ratio of the spacing distance to the length of the carbon nanotubes. For a given length, the field enhancement factor decreases sharply as the screening factor larger than 0.05. The simulation implies that the thickness of the array should be larger than a value but it does not help the emission much by increasing the thickness a great deal

Network Inference Using the Hub Model and Variants

Statistical network analysis primarily focuses on inferring the parameters of an observed network. In many applications, especially in the social sciences, the observed data is the groups formed by individual subjects. In these applications, the network is itself a parameter of a statistical model. Zhao and Weko (2019) propose a model-based approach, called the hub model, to infer implicit networks from grouping behavior. The hub model assumes that each member of the group is brought together by a member of the group called the hub. The set of members which can serve as a hub is called the hub set. The hub model belongs to the family of Bernoulli mixture models. Identifiability of Bernoulli mixture model parameters is a notoriously difficult problem. This paper proves identifiability of the hub model parameters and estimation consistency under mild conditions. Furthermore, this paper generalizes the hub model by introducing a model component that allows hubless groups in which individual nodes spontaneously appear independent of any other individual. We refer to this additional component as the null component. The new model bridges the gap between the hub model and the degenerate case of the mixture model -- the Bernoulli product. Identifiability and consistency are also proved for the new model. In addition, a penalized likelihood approach is proposed to estimate the hub set when it is unknown.Comment: arXiv admin note: substantial text overlap with arXiv:2004.0970

Atomic decoration for improving the efficiency of field electron emission of carbon nanotubes

The field electron emission from the single-walled carbon nanotubes with their open ends terminated by -BH, -NH, and -O has been simulated. The apex-vacuum barrier and the emission current have been calculated. It has been found that -BH and -NH suppress the apex-vacuum barrier significantly and lead to higher emission current in contrast to the -O terminated structure in the same applied field. The calculated binding energy implies that the carbon nanotubes terminated with -BH and -NH are more stable than those saturated by oxygen atoms or by hydrogen atoms.Comment: 8 pages, 9 figures, LaTeX; content changed, typos corrected, references adde

A Penalized Functional Linear Cox Regression Model for Spatially-defined Environmental Exposure with an Estimated Buffer Distance

In environmental health research, it is of interest to understand the effect of the neighborhood environment on health. Researchers have shown a protective association between green space around a person's residential address and depression outcomes. In measuring exposure to green space, distance buffers are often used. However, buffer distances differ across studies. Typically, the buffer distance is determined by researchers a priori. It is unclear how to identify an appropriate buffer distance for exposure assessment. To address geographic uncertainty problem for exposure assessment, we present a domain selection algorithm based on the penalized functional linear Cox regression model. The theoretical properties of our proposed method are studied and simulation studies are conducted to evaluate finite sample performances of our method. The proposed method is illustrated in a study of associations of green space exposure with depression and/or antidepressant use in the Nurses' Health Study.Comment: 27 pages, 5 figure