Linear regression models describe a linear relationship between a response and one or more predictive terms. Many times, however, a nonlinear relationship exists. Nonlinear Regression describes general nonlinear models. A special class of nonlinear models, called generalized linear models, uses linear methods. Parametric nonlinear models represent the relationship between a continuous response variable and one or more continuous predictor variables in the form y = f(X, b) + e, with f is a nonlinear function. fitnlm attempts to find values of the parameters b that minimize the mean squared differences between the observed responses y and the predictions of the model f(X, b). To do so, it needs a starting value beta0 before iteratively modifying the vector b to a vector with minimal mean squared error. Survival analysis consists of parametric, semiparametric, and nonparametric methods. You can use these to estimate the most commonly used measures in survival studies, survivor and hazard functions, compare them for different groups, and assess the relationship of predictor variables to survival time. Some statistical probability distributions describe survival times well. Commonly used distributions are exponential, Weibull, lognormal, Burr, and Birnbaum-Saunders distributions. Statistics and Machine Learning Toolbox functions ecdf and ksdensity compute the empirical and kernel density estimates of the cdf, cumulative hazard, and survivor functions. coxphfit fits the Cox proportional hazards model to the data. This book develops the Generalized Linear Models and Nonlinear regression Models The most important content is the following: - Multinomial Models for Nominal Responses - Multinomial Models for Ordinal Responses - Hierarchical Multinomial Models - Generalized Linear Models - Lasso Regularization of Generalized Linear Models - Regularize Poisson Regression - Regularize Logistic Regression - Regularize Wide Data in Parallel - Generalized Linear Mixed-Effects Models - Fit a Generalized Linear Mixed-Effects Model - Nonlinear Regression - Represent the Nonlinear Model - Choose Initial Vector beta0 - Fit Nonlinear Model to Data - Examine Quality and Adjust the Fitted Nonlinear Model - Predict or Simulate Responses Using a Nonlinear Model - Mixed-Effects Models - Introduction to Mixed-Effects Models - Mixed-Effects Model Hierarchy - Specifying Mixed-Effects Models - Specifying Covariate Models - Choosing nlmefit or nlmefitsa - Using Output Functions with Mixed-Effects Models - Examining Residuals for Model Verification - Mixed-Effects Models Using nlmefit and nlmefitsa - Survival Analysis - Kaplan-Meier Method - Hazard and Survivor Functions for Different Groups - Survivor Functions for Two Groups - Cox Proportional Hazards Model - Cox Proportional Hazards Model for Censored Data