I understand that you have multiple independent variables and want a suitable regression method to fit your model. When dealing with multiple independent variables, you can use multivariate regression methods to determine the expression for the parameter. Here are a few possible approaches to consider...
Publicly available data The performance of the CNN model remained high in the publicly available data sets although being characterized by considerable heterogeneity in image capturing and glaucoma ground truth procedures. The lowest AUC value of 0.854 [95% CI: 0.821–0.886] was recorded on the comp...
Example Data Sets for Model II Regressionmodex
Train on huge data sets. Explore models in the app trained on a subset of your data, and then generate code to train a selected model on a larger data set. Create scripts for training models without needing to learn syntax of the different functions. ...
In this section, we apply our BMRKR (Bayesian Multiple Response Kernel Regression) model on two simulated data sets and two real near infra-red spectroscopy data sets. Data pre-processing: The two real data sets are (i) Biscuit dough data (Osborne et al., 1984) and (ii) Wheat Data (...
For these classes of models, we discuss the construction of the power prior, prior elicitation issues, propriety conditions, model selection, and several other properties. For each class of models, we present real data sets to demonstrate the proposed methodology. 展开 ...
discrete statistical model; dispersion index; hazard rate function; parameter estimation; simulation; regression MSC: 62E15 1. Introduction In recent decades, count data analysis has drawn interest. There are many count data sets in practical as well as theoretical domains, including medicine, sports...
A linear regression model is of the formy=xTβ+ε, where ε∼N(0,σ2). The error variance σ2 and the coefficients β are estimated from the data. A GPR model explains the response by introducing latent variables, f(xi), i=1,2,...,n, from a Gaussian process (GP), and ...
由于它讨论的是time series data,所以具体操作上,就是先创建一个用所有Y的过去值、所有W的过去值、所有X的过去值加上一个噪音变量去回归Y的当前值的线性回归模型,称作full model,再创建一个用所有Y的过去值、所有W的过去值加上一个噪音变量(去除了所有X的过去值)去回归Y的当前值的线性回归模型,称作reduced ...
Approximate tests for the adequacy of the model are considered. Asymptotic tests are given for the significance of regression parameters. The GPR model has been applied to four observed data sets to which other regression models were applied earlier. 展开 ...