In short, R² is only the square of correlation if we happen to be (1) using linear regression models, and (2) are evaluating them on the same data they are fitted (as established previously). On the liberal use of R² outside the context of linear regression ...
In data analysis it is hence important to use methods that are robust to outliers. In this paper we develop a robust regression method for finding the largest subset in the data that can be approximated using a sparse linear model to a given precision. We show that the problem is NP-hard...
Exploratory analyses were performed to understand differences between the four groups using chi-squared tests and ANOVA. Multinomial logistic regression models were conducted to identify predictors for the allocation of patients in groups. Results A total of 1546 knee arthroplasty patients with a mean ...
The multiple linear regression model has an R-squared of .6.The main factors explaining glucose dynamics in the presence of mild-to-moderate exercise in T1D have been identified. The clinical parameters are formally quantified using real data collected during clinical trials. The multiple linear ...
In order to verify if PCs were able to correct for the population structure, we used two different methods applied to the association GWA analysis: genomic control and LD regression. Both methods showed no evidence of test statistic inflation (λGC1.02, LD regression intercept 1.0021). Finally,...
𝚿𝜽^1(𝒙t−1)]∈ℝp×Kp; R(⋅) is a sparsity-inducing penalty term; and λ,γ≥0 are regularisation parameters. The loss function (see Equation 6) consists of three terms: (i) the mean squared error (MSE) loss, (ii) a sparsity-inducing regulariser,...
The conceptual model of this study was analysed using Partial least squared structural equation modelling (PLS-SEM) rather than co-variance-based squared structural equation modelling (CB-SEM) techniques. PLS-SEM entails comparative advantages over CB-SEM such as suitability in explorative research, ta...
year of participation in a survey + age – age squared – D: subjectively measured health as bad (subjectively measured health as good) – D: has higher education (baseline for all educational variable - finished 6 grades) – D: finished school – D: finished college + D: owns a house ...
Neither Gaussian additive noises or blurring is effective in removing enough noise such that its error on adversarial examples could match that of the error on clean data. Deep Contractive Network Contractive autoencoder (CAE) A variant of AE with additional penalty for minimizing the squared norm ...
This makes the regression head differentiable and allows the proposal generation to be fully task-driven. In addition, unlike Faster R-CNN (Girshick 2015), which generates a large number of boxes and then thresholds them based on the predicted objectness, we completely remove the classification ...