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968 results on '"Zhang, Chunming"'

3. Nonlinear Functional Principal Component Analysis Using Neural Networks

Author

Zhong, Rou, Zhang, Chunming, and Zhang, Jingxiao

Subjects
Statistics - Methodology
Abstract

Functional principal component analysis (FPCA) is an important technique for dimension reduction in functional data analysis (FDA). Classical FPCA method is based on the Karhunen-Lo\`{e}ve expansion, which assumes a linear structure of the observed functional data. However, the assumption may not always be satisfied, and the FPCA method can become inefficient when the data deviates from the linear assumption. In this paper, we propose a novel FPCA method that is suitable for data with a nonlinear structure by neural network approach. We construct networks that can be applied to functional data and explore the corresponding universal approximation property. The main use of our proposed nonlinear FPCA method is curve reconstruction. We conduct a simulation study to evaluate the performance of our method. The proposed method is also applied to two real-world data sets to further demonstrate its superiority.

Published
2023

5. Locally sparse estimator of generalized varying coefficient model for asynchronous longitudinal data

Author

Zhong, Rou, Zhang, Chunming, and Zhang, Jingxiao

Subjects
Statistics - Methodology
Abstract

In longitudinal study, it is common that response and covariate are not measured at the same time, which complicates the analysis to a large extent. In this paper, we take into account the estimation of generalized varying coefficient model with such asynchronous observations. A penalized kernel-weighted estimating equation is constructed through kernel technique in the framework of functional data analysis. Moreover, local sparsity is also considered in the estimating equation to improve the interpretability of the estimate. We extend the iteratively reweighted least squares (IRLS) algorithm in our computation. The theoretical properties are established in terms of both consistency and sparsistency, and the simulation studies further verify the satisfying performance of our method when compared with existing approaches. The method is applied to an AIDS study to reveal its practical merits.

Published
2022

35. Research on Experimental Teaching Reform of Electronic Technology Course

Author

Zhang, Chunming, Sun, Yiyue, Zhang, Xiaofeng, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Poniszewska-Maranda, Aneta, editor, and Park, Hyunhee, editor

Published
2021
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37. Perforin 1 in Cancer: Mechanisms, Therapy, and Outlook.

Author

Guan, Xiaoya, Guo, Huina, Guo, Yujia, Han, Qi, Li, Zhongxun, and Zhang, Chunming

Subjects
CYTOTOXIC T cells, GENE expression, OVERALL survival, CARCINOGENESIS, CANCER cells
Abstract

PRF1 (perforin 1) is a key cytotoxic molecule that plays a crucial role in the killing function of natural killer (NK) cells and cytotoxic T lymphocytes (CTLs). Recent studies have focused on PRF1's role in cancer development, progression, and prognosis. Studies have shown that aberrant PRF1 expression has a significant role to play in cancer development and progression. In some cancers, high expression of the PRF1 gene is associated with a better prognosis for patients, possibly because it helps enhance the body's immune response to tumors. However, some studies have also shown that the absence of PRF1 may make it easier for tumors to evade the body's immune surveillance, thus affecting patient survival. Furthermore, recent studies have explored therapeutic strategies based on PRF1, such as enhancing the ability of immune cells to kill cancer cells by boosting PRF1 activity. In addition, they have improved the efficacy of immunotherapy by modulating its expression to enhance the effectiveness of the treatment. Based on these findings, PRF1 may be a valuable biomarker both for the treatment of cancer and for its prognosis in the future. To conclude, PRF1 has an important biological function and has clinical potential for the treatment of cancer, which indicates that it deserves more research and development in the future. [ABSTRACT FROM AUTHOR]

Published
2024
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49. Learning Network-Structured Dependence From Non-Stationary Multivariate Point Process Data

Author

Gao, Muhong, Zhang, Chunming, and Zhou, Jie

Abstract

Learning the sparse network-structured dependence among nodes from multivariate point process data $\{{ {T}}_{i}\}_{i\in \mathcal {V} }$ has wide applications in information transmission, social science, and computational neuroscience. This paper develops new continuous-time stochastic models of the conditional intensity functions $\{\lambda _{i}(t | \mathscr {F} _{t}): t\ge 0\}_{i\in \mathcal {V} }$ , dependent on past event counts of parent nodes, to uncover the network structure within an array of non-stationary multivariate counting processes $\{\boldsymbol {N}(t): t\ge 0\}$ for $\{{ {T}}_{i}\}_{i\in \mathcal {V} }$ . The stochastic mechanism is crucial for statistical inference of graph parameters relevant to structure recovery but does not satisfy the key assumptions of commonly used processes like the Poisson process, Cox process, Hawkes process, queuing model, and piecewise deterministic Markov process. We introduce a new marked point process for intensity discontinuities, derive compact representations of their conditional distributions, and demonstrate the cyclicity property of ${N}(t)$ driven by recurrence time points. These new theoretical properties enable us to establish statistical consistency and convergence properties of the proposed penalized M-estimators for graph parameters under mild regularity conditions. Simulation evaluations demonstrate computational simplicity and increased estimation accuracy compared to existing methods. Real multiple neuron spike train recordings are analyzed to infer connectivity in neuronal networks.

Published
2024
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