I am compiling a list of models under Regression analysis(Whatever I think is useful for Machine learning) which is divided into two models i.e Parametric and Non-parametric Regression. Got most of the models from wikepedia but it's so confusing as there are different models and approaches all over the place . Hence I wanted to make a list that can help others and myself, so that we can refer all possible models under Regression.
PARAMETRIC REGRESSION 1)Linear Regression 1.Simple linear Regression >Ordinary Least Squares(OLS) >Deming Regression >Least Absolute Deviations Regression(LAD) >Theil–Sen Estimator 2.Multiple Linear Regression >Polynomial Regression >Ordinary Least Squares(OLS) >Weighted least squares >Partial least squares 3.Multivariate/General Linear Regression 4.Gradient Descent >Batch Gradient Descent >Mini-Batch Gradient Descent >Stochastic Gradient Descent 2)Generalized Linear Model (GLM) 3)Logistic Regression 1.Generalized linear model 2.Iteratively reweighted least squares (IRLS) 3.Maximum likelihood estimation 4.Evaluating Goodness of fit >Deviance and Likelihood ratio test >Hosmer-Lemeshow test >Pseudo R squared >R squared 5.Assess significance of Regression coefficients >Case-control sampling >Likelihood ratio test >Wald statistics 6.Optimization >Batch Gradient Descent >Mini-Batch Gradient Descent >Stochastic Gradient Descent >Conjugate Gradient >BFGS >L-BFGS 4)Regularization 1.Ridge Regression 2.Lasso Regression 3.ElasticNet Regression 5)Non-Linear Regression >Ordinary Least Squares(OLS) >Weighted least squares 6)Quantile Regression 7)Segmented Regression 8)Percentage Regression NON-PARAMETRIC REGRESSION 1)Gaussian process regression or Kriging 2)Kernel Regression 3)Nonparametric multiplicative regression (NPMR)
Can you guys tell me if I have put the models in the correct list and also tell me where I should put the models that I haven't placed under any header yet like:
Principal Component Regression Partial Least Square Regression Support Vector Regression Ordinal Regression Poisson Regression Negative Binomial Regression Quasi-Poisson Regression Cox Regression
Also can you let me know which of these are necessary for machine learning(as I am interested in statistical analysis and applied machine learning).