Mixed predictability and cross-validation to assess non-linear Granger causality in short cardiovascular variability series

2006 ◽  
Vol 51 (4) ◽  
pp. 255-259 ◽  
Author(s):  
Luca Faes ◽  
Roberta Cucino ◽  
Giandomenico Nollo
Keyword(s):  
Granger Causality ◽  
Cross Validation ◽  
Cardiovascular Variability ◽  
Non Linear

scholarly journals Uncertainty Due to Infectious Diseases and Stock–Bond Correlation

Econometrics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 17
Author(s):  
Konstantinos Gkillas ◽  
Christoforos Konstantatos ◽  
Costas Siriopoulos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Infectious Diseases ◽  
Granger Causality ◽  
Predictive Value ◽  
High Frequency ◽  
Causal Relation ◽  
Granger Causality Test ◽  
Causality Test ◽  
Non Linear

We study the non-linear causal relation between uncertainty-due-to-infectious-diseases and stock–bond correlation. To this end, we use high-frequency 1-min data to compute daily realized measures of correlation and jumps, and then, we employ a nonlinear Granger causality test with the use of artificial neural networks so as to investigate the predictability of this type of uncertainty on realized stock–bond correlation and jumps. Our findings reveal that uncertainty-due-to-infectious-diseases has significant predictive value on the changes of the stock–bond relation.

Adaptive efficiency of the Mexican Stock Exchange

2018 ◽  
Vol 31 (2) ◽  
pp. 326-335
Author(s):  
Esmeralda Brito-Cervantes ◽  
Semei Coronado ◽  
Manuel Morales-García ◽  
Omar Rojas
Keyword(s):  
Market Efficiency ◽  
Granger Causality ◽  
Latin American ◽  
Stock Exchange ◽  
Information Process ◽  
Causality Test ◽  
Content Type ◽  
Regulatory Policies ◽  
Adaptive Efficiency ◽  
Non Linear

Purpose The purpose of this paper is to analyse the adaptive market efficiency in the price–volume (P–V) relationship of the stocks listed in the Mexican Stock Exchange. The period under study goes from 1982 to 2015. In order to detect causality and, thus, determine adaptive efficiency in the market, one linear and two non-linear tests are applied. There are few papers in the literature that study the P–V relationship in Latin American markets; as such, this paper may be of interest and importance to financial academics and practitioners alike. Design/methodology/approach The Diks and Panchenko (DP) non-parametric Granger causality and the Brooks and Hinich (BH) cross-bicorrelation tests are applied. Findings Derived from the DP test, the findings show that there exists bi-directional non-linear Granger causality in 25.71 per cent of the firms studied, compared to 8 per cent when applying the linear Granger causality test. Therefore, there is evidence of weak-form efficiency in the market. From the BH test, evidence is shown of the adaptive market efficiency, since 71.42 per cent of firms exhibited some form of non-linear dependence in certain periods of time. With these results, the information process should be better studied for a greater comprehension of regulatory policies in the market and better decision-making tools for the investors. Originality/value This paper complements studies on the P–V relationship and efficiency in a Latin American market.

Accounting for Respiration is Necessary to Reliably Infer Granger Causality From Cardiovascular Variability Series

2012 ◽  
Vol 59 (3) ◽  
pp. 832-841 ◽  
Author(s):  
A. Porta ◽  
T. Bassani ◽  
V. Bari ◽  
G. D. Pinna ◽  
R. Maestri ◽  
...  
Keyword(s):  
Granger Causality ◽  
Cardiovascular Variability

scholarly journals Real-time estimation of daily physical activity intensity by a triaxial accelerometer and a gravity-removal classification algorithm

2011 ◽  
Vol 105 (11) ◽  
pp. 1681-1691 ◽  
Author(s):  
Kazunori Ohkawara ◽  
Yoshitake Oshima ◽  
Yuki Hikihara ◽  
Kazuko Ishikawa-Takata ◽  
Izumi Tabata ◽  
...  
Keyword(s):  
Physical Activity ◽  
Linear Regression ◽  
Cross Validation ◽  
Classification Algorithm ◽  
Daily Physical Activity ◽  
Triaxial Accelerometer ◽  
Validation Group ◽  
Activity Intensity ◽  
Metabolic Equivalents ◽  
Non Linear

We have recently developed a simple algorithm for the classification of household and locomotive activities using the ratio of unfiltered to filtered synthetic acceleration (gravity-removal physical activity classification algorithm, GRPACA) measured by a triaxial accelerometer. The purpose of the present study was to develop a new model for the immediate estimation of daily physical activity intensities using a triaxial accelerometer. A total of sixty-six subjects were randomly assigned into validation (n 44) and cross-validation (n 22) groups. All subjects performed fourteen activities while wearing a triaxial accelerometer in a controlled laboratory setting. During each activity, energy expenditure was measured by indirect calorimetry, and physical activity intensities were expressed as metabolic equivalents (MET). The validation group displayed strong relationships between measured MET and filtered synthetic accelerations for household (r 0·907, P < 0·001) and locomotive (r 0·961, P < 0·001) activities. In the cross-validation group, two GRPACA-based linear regression models provided highly accurate MET estimation for household and locomotive activities. Results were similar when equations were developed by non-linear regression or sex-specific linear or non-linear regressions. Sedentary activities were also accurately estimated by the specific linear regression classified from other activity counts. Therefore, the use of a triaxial accelerometer in combination with a GRPACA permits more accurate and immediate estimation of daily physical activity intensities, compared with previously reported cut-off classification models. This method may be useful for field investigations as well as for self-monitoring by general users.

Metabolomics signature in serum months to years before thoracic cancer: The HUNT study.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e23059-e23059
Author(s):  
Oluf D. Røe ◽  
Vincenzo Lagani ◽  
Hans Fredrik Kvitvang ◽  
Maria Markaki ◽  
Ioannis Tsamardinos ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Data Analysis ◽  
Cross Validation ◽  
Performance Estimation ◽  
Serum Samples ◽  
Analysis Pipeline ◽  
Early Cancer Diagnosis ◽  
Non Linear ◽  
Hunt Study ◽  
Data Analysis Pipeline

e23059 Background: The Cancer-Biomarkers in HUNTinitiative seeks to identify novel biomarkers for the early cancer diagnosis. For lung cancers and mesothelioma clinically useful early markers are not available. In the prospective HUNT study in Norway, pre-diagnostic samples ranging 0-20 years before diagnosis are available for research purposes. Here we present our first results on high-throughput metabolomics analysis in serum two months to 16 years before diagnosis. Methods: LC-MS untargeted (Amide-) metabolites (n = 1042) were profiled in serum samples from 48 future patients (12 each of adeno-, squamous cell carcinoma, small-cell lung cancer and mesothelioma) and from 48 controls that were cancer-free 5 years after blood sampling. All were active smokers. Metabolic features for (a) each cancer and (b) all cancers pooled together were analyzed with moderated t-test (R limma package). Multivariate analyses included (a) OPLS-DA and (b) signature identification through a data-analysis pipeline that includes feature selection (such as the algorithm in [1]), non-linear modelers (e.g., Random Forests) and Cross-Validation with bootstrapping [2] for optimizing algorithms and providing unbiased performance estimation. The pipeline is implemented in the Just Add Data software (Gnosis Data Analysis). Results: Univariate and OPLS-DA analyses did not identify any association between metabolites and cancer. The non-linear data analysis pipeline identified a signature containing five metabolites able to discriminate between cancer and non-cancer patients, statistically significantly better than random (AUC = 0.667, CI = [0.536, 0.784]). Conclusions: Our results indicate that metabolic profiling in serum may help in identifying subjects who are likely to be diagnosed with lung cancer/mesothelioma in a time period of several years before diagnosis. More data will be presented at the annual meeting. Further validation studies are planned for confirming the replicability of these findings. 1) Lagani V et al., 2016. arXiv:1611.03227 2) Greasidou L, 2017. Bias Correction of the Cross-Validation Performance Estimate and Speed Up of its Execution Time, MSc Thesis, University of Crete

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