Zobrazeno 1 - 10
of 424
pro vyhledávání: '"Shao Xiaofeng"'
Autor:
Lv Nanning, Zhou Zhangzhe, He Shuangjun, Shao Xiaofeng, Zhou Xinfeng, Feng Xiaoxiao, Qian Zhonglai, Zhang Yijian, Liu Mingming
Publikováno v:
Open Medicine, Vol 17, Iss 1, Pp 1216-1227 (2022)
Osteoporosis is a major health concern worldwide. The present study aimed to identify effective biomarkers for osteoporosis detection. In osteoporosis, 559 differentially expressed genes (DEGs) were enriched in PI3K-Akt signaling pathway and Foxo sig
Externí odkaz:
https://doaj.org/article/2ac4dc83303d45dd9358d261836ef531
This article addresses the problem of testing the conditional independence of two generic random vectors $X$ and $Y$ given a third random vector $Z$, which plays an important role in statistical and machine learning applications. We propose a new non
Externí odkaz:
http://arxiv.org/abs/2407.17694
Time series segmentation aims to identify potential change-points in a sequence of temporally dependent data, so that the original sequence can be partitioned into several homogeneous subsequences. It is useful for modeling and predicting non-station
Externí odkaz:
http://arxiv.org/abs/2404.07451
We propose a bootstrap-based test to detect a mean shift in a sequence of high-dimensional observations with unknown time-varying heteroscedasticity. The proposed test builds on the U-statistic based approach in Wang et al. (2022), targets a dense al
Externí odkaz:
http://arxiv.org/abs/2311.09419
Data objects taking value in a general metric space have become increasingly common in modern data analysis. In this paper, we study two important statistical inference problems, namely, two-sample testing and change-point detection, for such non-Euc
Externí odkaz:
http://arxiv.org/abs/2307.04318
Sliced inverse regression (SIR, Li 1991) is a pioneering work and the most recognized method in sufficient dimension reduction. While promising progress has been made in theory and methods of high-dimensional SIR, two remaining challenges are still n
Externí odkaz:
http://arxiv.org/abs/2304.06201
Change point testing for high-dimensional data has attracted a lot of attention in statistics and machine learning owing to the emergence of high-dimensional data with structural breaks from many fields. In practice, when the dimension is less than t
Externí odkaz:
http://arxiv.org/abs/2303.10808
In this article, we propose a class of $L_q$-norm based U-statistics for a family of global testing problems related to high-dimensional data. This includes testing of mean vector and its spatial sign, simultaneous testing of linear model coefficient
Externí odkaz:
http://arxiv.org/abs/2303.08197
We propose a novel method for testing serial independence of object-valued time series in metric spaces, which is more general than Euclidean or Hilbert spaces. The proposed method is fully nonparametric, free of tuning parameters, and can capture al
Externí odkaz:
http://arxiv.org/abs/2302.12322
The spectral density matrix is a fundamental object of interest in time series analysis, and it encodes both contemporary and dynamic linear relationships between component processes of the multivariate system. In this paper we develop novel inferenc
Externí odkaz:
http://arxiv.org/abs/2212.13686