Mathematical Road-Data analysis advanced-cox proportional risk regression model

Cox Proportional risk regression model (Cox's proportional hazards regression model), referred to as Cox regression models. The model, proposed by British statistician D.r.cox in 1972, is mainly used for prognostic analysis of tumors and other chronic diseases, as well as for etiological exploration of cohort studies.

h (t/x) =h0 (t) exp (β1 X1 +β2 X2 + ... +βp Xp) H0 (t): The benchmark risk function is the risk function of the T-moment at which all variables take zero, that is, the risk function without a co-variable X1, X2 ... Xp: Impact factor variable Beta 1, β2 ... Βp: Regression coefficient covariance (covariate) in psychology, behavioral science, is a variable that is linearly correlated with the dependent variable and is controlled by statistical techniques when discussing the relationship between the independent variable and the dependent variable. Commonly used co-variables include pre-measured fractions of dependent variables, demographic indicators, and personal characteristics that are significantly different from dependent variables. for Example: Rainfall (t) = k* temperature (t) + e where T is the argument time, the rainfall (t) is the dependent variable, and the temperature (t) is the covariance K is a constant.

Mathematical Road-Data analysis advanced-cox proportional risk regression model