Robust depth-based estimation of the functional autoregressive model
Name:
Martinez-Hernandez et al 2018 - Robust Depth-Based.pdf
Size:
707.2Kb
Format:
PDF
Description:
Accepted Manuscript
Type
Article
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
Date
2018-06-14Online Publication Date
2018-06-14Print Publication Date
2019-03Permanent link to this record
http://hdl.handle.net/10754/628526Metadata
Show full item recordAbstract
A robust estimator for functional autoregressive models is proposed, the Depth-based Least Squares (DLS) estimator. The DLS estimator down-weights the influence of outliers by using the functional directional outlyingness as a centrality measure. It consists of two steps: identifying the outliers with a two-stage functional boxplot, then down-weighting the outliers using the functional directional outlyingness. Theoretical properties of the DLS estimator are investigated such as consistency and boundedness of its influence function. Through a Monte Carlo study, it is shown that the DLS estimator performs better than estimators based on Principal Component Analysis (PCA) and robust PCA, which are the most commonly used. To illustrate a practical application, the DLS estimator is used to analyze a dataset of ambient CO concentrations in California.Citation
Martínez-Hernández I, Genton MG, González-Farías G (2018) Robust depth-based estimation of the functional autoregressive model. Computational Statistics & Data Analysis. Available: http://dx.doi.org/10.1016/j.csda.2018.06.003.Sponsors
This research was partially supported by (1) CONACYT, México, scholarship as visiting research student, (2) CONACYT, México, CB-2015-01-252996, and (3) King Abdullah University of Science and Technology (KAUST). The authors thank the two anonymous referees for their valuable comments.Publisher
Elsevier BVAdditional Links
http://www.sciencedirect.com/science/article/pii/S0167947318301415Scopus Count
Related items
Showing items related by title, author, creator and subject.
-
Spatial modelling of gully erosion using evidential belief function, logistic regression, and a new ensemble of evidential belief function-logistic regression algorithm(Land Degradation & Development, Wiley, 2018-09-25) [Article]This study aims to assess gully erosion susceptibility and delineate gully erosion-prone areas in Toroud Watershed, Semnan Province, Iran. Two different methods, namely, logistic regression (LR) and evidential belief function (EBF), were evaluated, and a new ensemble method was proposed using the combination of both methods. We initially created a gully erosion inventory map using different resources, including early reports, Google Earth images, and Global Positioning System-aided field surveys. We subsequently split this information randomly and selected 70% (90) of the gullies for calibration and 30% (38) for validation. The method was constructed using a combination of morphometric and thematic predictors that include 16 conditioning parameters. We also assessed the following: (a) potential multicollinearity issues using tolerance and variance inflation factor indices and (b) covariate effects using LR coefficients and EBF class weights. Results show that land use/land cover, lithology, and distance to roads dominate the method with the greatest effect on gully occurrences. We produced three susceptibility maps and evaluated their predictive power through area under the curve (AUC) and seed cell area index analyses. AUC results revealed that the ensemble method presented a considerably higher performance (AUC = 0.909) than did the individual LR (0.802) and EBF (0.821) methods. Similarly, seed cell area index displayed a constant decrease from the ensemble to single methods. The resulted gully erosion-susceptibility map could be used by decision makers and local managers for soil conservation, and for minimising damages in development activities including construction of infrastructures such as roads and the route of gas and electricity transmission lines.
-
The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens.(Genome biology, Springer Science and Business Media LLC, 2019-11-19) [Article]BACKGROUND:The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. RESULTS:Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. CONCLUSION:We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.
-
mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning(Frontiers in Genetics, Frontiers Media SA, 2019-01-22) [Article]As a great challenge in bioinformatics, enzyme function prediction is a significant step toward designing novel enzymes and diagnosing enzyme-related diseases. Existing studies mainly focus on the mono-functional enzyme function prediction. However, the number of multi-functional enzymes is growing rapidly, which requires novel computational methods to be developed. In this paper, following our previous work, DEEPre, which uses deep learning to annotate mono-functional enzyme's function, we propose a novel method, mlDEEPre, which is designed specifically for predicting the functionalities of multi-functional enzymes. By adopting a novel loss function, associated with the relationship between different labels, and a self-adapted label assigning threshold, mlDEEPre can accurately and efficiently perform multi-functional enzyme prediction. Extensive experiments also show that mlDEEPre can outperform the other methods in predicting whether an enzyme is a mono-functional or a multi-functional enzyme (mono-functional vs. multi-functional), as well as the main class prediction across different criteria. Furthermore, due to the flexibility of mlDEEPre and DEEPre, mlDEEPre can be incorporated into DEEPre seamlessly, which enables the updated DEEPre to handle both mono-functional and multi-functional predictions without human intervention.
