Thus, data processing of multivariate time series needs an efficient data representation method.
Incomplete data was handled by using the maximum likelihood estimation approach, i.e. treatment of missing data assumption of multivariate normality, based on the direct maximation of the likelihood of the observed data.
The CHI model is developed in [7] which specifically utilizes the monotonicity of disease progression to enhance the data fusion of multivariate clinical measurements taken at irregular time points.
Data analysis based on differential topology has long been primarily focused on data samples on univariate (scalar-valued) functions f : R n → R. Nonetheless, with the recent advent of high-performance computers and sensors of high resolution, we are more likely to tackle data samples of multivariate functions f : R n → R m, m>1.
The square roots of the explained variances mean that the data of the multivariate regression correlate to the empirically measured ones with values of 0.88 for grip strength and 0.82 for pinch strength [ 29].
Multivariate SPC has been proposed to fully utilize the available data, however, interpretation of multivariate information is often too complex for most line operators.
Variable selection represents an irreplaceable tool in the analysis of high-dimensional data, preventing numerous approaches of multivariate statistics and data mining from overfitting the data or even from being computationally infeasible due to the curse of dimensionality.
In the analysis of hydrochemical data, the application of multivariate statistical analyses has been profusely taken up by various researchers.
Finally, to minimize the possibility of any confounding bias, we considered a large number of variables, such as the presence of underlying diseases, and analysed our data by means of multivariate conditional analysis.
In the supplementary data, the results of multivariate logistic regression analysis for CRC and advanced adenomas are shown for the cut-off values of 50 and 100 ng/ml.
