Editorial for the special issue on high-dimensional and functional data analysis. (English) Zbl 1471.00015
From the text: High-dimensional and functional data structures have been an important focus of methodological, theoretical and applied statistics research for over two decades, with applications areas including biostatistics, chemometrics, environmetrics, genetics, geophysics, and neuroimaging. The aim of this special issue is to collect research papers concerned with computational and data-analytic aspects of high-dimensional and functional data analysis.
MSC:
| 00B15 | Collections of articles of miscellaneous specific interest |
| 62-06 | Proceedings, conferences, collections, etc. pertaining to statistics |
| 62-08 | Computational methods for problems pertaining to statistics |
| 62R07 | Statistical aspects of big data and data science |
| 62R10 | Functional data analysis |
References:
| [1] | Abpeykar, Shadi; Ghatee, Mehdi; Zare, Hadi, Ensemble decision forest of RBF networks via hybrid feature clustering approach for high-dimensional data classification, Comput. Statist. Data Anal., 131, 12-36, (2019), http://dx.doi.org/10.1016/j.csda.2018.08.015 · Zbl 1471.62010 |
| [2] | Ahmad, M. Rauf, A significance test of the RV coefficient in high dimensions, Comput. Statist. Data Anal., 131, 116-130, (2019), http://dx.doi.org/10.1016/j.csda.2018.10.xx · Zbl 1471.62170 |
| [3] | Dai, Wenlin; Genton, Marc G., Directional outlyingness for multivariate functional data, Comput. Statist. Data Anal., 131, 50-65, (2019), http://dx.doi.org/10.1016/j.csda.2018.03.017 · Zbl 1471.62049 |
| [4] | Febrero-Bande, Manuel; Galeano, Pedro; González-Manteiga, Wenceslao, Estimation, imputation and prediction for the functional linear model with scalar response with responses missing at random, Comput. Statist. Data Anal., 131, 91-103, (2019), http://dx.doi.org/10.1016/j.csda.2018.07.006 · Zbl 1471.62061 |
| [5] | French, Joshua; Kokoszka, Piotr; Stoev, Stilian; Hall, Lauren, Quantifying the risk of heat waves using extreme value theory and spatio-temporal functional data, Comput. Statist. Data Anal., 131, 176-193, (2019), http://dx.doi.org/10.1016/j.csda.2018.07.004 · Zbl 1471.62065 |
| [6] | Fu, Eric; Heckman, Nancy, Model-based curve registration via stochastic approximation EM algorithm, Comput. Statist. Data Anal., 131, 159-175, (2019), http://dx.doi.org/10.1016/j.csda.2018.06.010 · Zbl 1471.62066 |
| [7] | Liebl, Dominik; Rameseder, Stefan, Partially observed functional data: the case of systematically missing parts, Comput. Statist. Data Anal., 131, 104-115, (2019), http://dx.doi.org/10.1016/j.csda.2018.08.011 · Zbl 1471.62112 |
| [8] | Ma, Haiqiang; Li, Ting; Zhu, Hongtu; Zhu, Zhongyi, Quantile regression for functional partially linear models in high dimensions, Comput. Statist. Data Anal., (2019), http://dx.doi.org/10.1016/j.csda.2018.06.005 · Zbl 1469.62114 |
| [9] | Martinez-Hernandez, Israel; Genton, Marc G.; Gonzalez-Farias, Graciela, Robust depth-based estimation of the functional autoregressive model, Comput. Statist. Data Anal., 131, 66-79, (2019), http://dx.doi.org/10.1016/j.csda.2018.06.003 · Zbl 1471.62133 |
| [10] | Park, Yeonjoo; Simpson, Douglas G., Robust probabilistic classication applicable to irregularly sampled functional data, Comput. Statist. Data Anal., 131, 37-49, (2019), http://dx.doi.org/10.1016/j.csda.2018.08.001 · Zbl 1471.62156 |
| [11] | Sang, Peijun; Wang, Liangliang; Cao, Jiguo, Weighted empirical likelihood inference for dynamical correlations, Comput. Statist. Data Anal., 131, 194-206, (2019), http://dx.doi.org/0.1016/j.csda.2018.07.003 · Zbl 1471.62180 |
| [12] | Shen, Keren; Yao, Jianfeng; Li, Wai Keung, On a spiked model for large volatility matrix estimation from noisy high-frequency data, Comput. Statist. Data Anal., 131, 207-221, (2019), http://dx.doi.org/10.1016/j.csda.2018.06.004 · Zbl 1471.62184 |
| [13] | Wang, Bo; Xu, Aiping, Gaussian process methods for nonparametric functional regression with mixed predictors, Comput. Statist. Data Anal., 131, 80-90, (2019), http://dx.doi.org/0.1016/j.csda.2018.07.009 · Zbl 1471.62204 |
| [14] | Wong, Raymond K. W.; Zhang, Xiaoke, Nonparametric operator-regularized covariance function estimation for functional data, Comput. Statist. Data Anal., 131, 131-144, (2019), http://dx.doi.org/10.1016/j.csda.2018.05.013 · Zbl 1471.62218 |
| [15] | Yue, Mu; Li, Jialiang; Cheng, Ming-Yen, Two-step sparse boosting for high-dimensional longitudinal data with varying coefficients, Comput. Statist. Data Anal., 131, 222-234, (2019), http://dx.doi.org/10.1016/j.csda.2018.10.002 · Zbl 1471.62229 |
| [16] | Zhang, Yaohua; Zou, Jian; Ravishanker, Nalini; Thavaneswaran, Aerambamoorthy, Modeling financial durations using penalized estimating functions, Comput. Statist. Data Anal., 131, 145-158, (2019), http://dx.doi.org/10.1016/j.csda.2018.08.020 · Zbl 1471.62232 |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
