We examine the serial correlation structure of six liquid cryptocurrencies with a long data record – Bitcoin, DASH, Stellar, Litecoin, Monero, and Ripple – with a use of the detrended ...cross-correlation (DCCA) and detrending moving-average cross-correlation (DMCA) correlation coefficients. We find that these cryptocurrencies behave differently from the stock markets which are much closer to the random walk (efficient) dynamics. We further discuss issues connected to strong statements about cryptocurrency markets practical inefficiency.
•We use DCCA and DMCA to estimate how long is the memory of cryptocurrencies.•We found long-range cross-correlations returns and respective lags for about 30 days.•Cryptocurrencies’ memory is lower than the observed in stock indices.
Aim: Glucose Management Indicator (GMI) is correlated with HbA1c, while studies reported the discrepancy between them. Factors affecting this discrepancy, especially in patients with good glycemic ...control, remain unclear. This study was designed to explore the related factors affecting the relationship between HbA1c and GMI in adult T2D patients with good glycemic control.
Methods: Adult T2D patients with good glycemic control who received HbA1c test and CGM were retrospectively analyzed. GMI and glycemic variability (GV) indices including SD and MAGE were derived from CGMS. The absolute value of hemoglobin glycation index (HbA1c minus GMI) (|HGI|) was used to quantify the difference between HbA1c and GMI. Linear regression and correlation analyses were used to analyze the correlation between GV indices and |HGI|, HbA1c and GMI and whether GV affected their relationship.
Results: Eighty-four patients (median HbA1c 6.6%, median GMI 6.4%) were included. |HGI| was higher than 0.5% in 40% of the patients (n=34). |HGI| was linearly correlated with SD and MAGE (β = 0.291 and 0.294, P<0.05). HbA1c was linearly correlated with GMI (β=0.525, P<0.001). This correlation remained after adjusting for sex, age, diabetes course, BMI, hemoglobin level and with chronic diabetic complications or not (Model 1, β=0.496, P<0.001). Further adjusting for SD (Model 2) or MAGE (Model 3) based on Model 1, the correlation between HbA1c and GMI became weaker (β=0.398 and 0.425, respectively). The correlation between HbA1c and GMI was closer in the patients with normal SD (<1.4mmol/L) than those with abnormal SD (r=0.563 vs. r=0.505). A similar result could be found in patients with normal MAGE (<3.9mmol/L) and abnormal MAGE (r=0.579 vs. r=0.514).
Conclusion: HbA1c was positively correlated with GMI. But even in adult T2D patients with good glycemic control, the correlation between HbA1c and GMI was significantly affected by GV. SD or MAGE accounted for this discrepancy.
Disclosure
Z.Liu: None. B.Lin: None. D.Chen: None. H.Lin: None. D.Yang: None. J.Yan: None. B.Yao: None. W.Xu: None.
In this article, a just-in-time-learning (JITL)-aided canonical correlation analysis (CCA) is proposed for the monitoring and fault detection of multimode processes. A canonical correlation analysis ...(CCA)-based fault detection method has been applied to single-operating-mode processes. However, CCA has limitations in handling processes with multiple operating points. These limitations are illustrated by a numerical example. To reduce the time for searching relevant data, K-means is integrated into the JITL to build the local CCA model. Furthermore, the proposed method is compared with commonly used kernel-based methods in terms of computational complexity and interpretability of the results. Finally, the validity and efficacy of the proposed method are shown using an industrial benchmark process. Results show that the proposed method has better performance than conventional methods in terms of fault detection rate while still tracking changes in the system.
Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant ...attentions in recent years. Canonical correlation analysis (CCA) is one of the powerful multivariate tools to jointly investigate relationships among multiple data sets, which can uncover disease or environmental effects in various modalities simultaneously and characterize changes during development, aging, and disease progressions comprehensively. In the past 10 years, despite an increasing number of studies have utilized CCA in multivariate analysis, simple conventional CCA dominates these applications. Multiple CCA‐variant techniques have been proposed to improve the model performance; however, the complicated multivariate formulations and not well‐known capabilities have delayed their wide applications. Therefore, in this study, a comprehensive review of CCA and its variant techniques is provided. Detailed technical formulation with analytical and numerical solutions, current applications in neuroscience research, and advantages and limitations of each CCA‐related technique are discussed. Finally, a general guideline in how to select the most appropriate CCA‐related technique based on the properties of available data sets and particularly targeted neuroscience questions is provided.
Neuroscience applications of canonical correlation analysis (CCA) and its variants are systematically reviewed from a technical perspective. Detailed formulations, analytical and numerical solutions, current applications, and advantages and limitations of CCA and its variants are discussed. A general guideline to select the most appropriate CCA‐related technique is provided.
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