Notations
= number of points (observations) of vector of factors and corresponding independent variable;
= number of independent factors;
= vector of independent factors at point ;
= point of dependent variable corresponding to the point ;
= decision variable; coefficient of degree in polynomial piece for factor n,;
= Gain Functions with zero scenario benchmark for factor n at point . The piece number depends on the factor n and point number ;
= joint vector of decision variables; vector of coefficients of polynomial pieces;
= sum of Gain Functions with zero scenario benchmark at point ;
= Loss Functions at point ;
= degree of spline of factor n, , integer;
= number of pieces for factor n, , integer;
= smoothing degree of a spline of factor n, , integer;
= variable for range of variation for individual spline in knots of factor n;
= upper bounds for range of variation for individual spline in knots of factor n;
= vector of polynomial degrees;
= vector of polynomial piece numbers;
= vector of polynomial smoothness;
= vector of upper bounds for splines knots ranges;
= PSG function Spline_sum generating a set of loss scenarios using initial data and a smoothing constraints (assuring smoothness according to specification);
= PSG Maximum Likelihood for Logistics Regression function Logarithms Exponents Sum applied to Spline_sum function. All should be 0 or 1;
= vector with components: |
Note. Function evaluates probabilities of the outcome of 1 for trial i.
Optimization Problem 1
maximizing Logarithms Exponents Sum for building spline
(CS1)
calculation of Logarithms Exponents Sum and Logistic on built spline
Optimization Problem 2
maximizing Logarithms Exponents Sum for building spline
(CS2)
calculation of Logarithms Exponents Sum and Logistic on built spline
Optimization Problem 3
CrossValidation
maximizing Logarithms Exponents Sum for building spline
(CS3)
calculation of Logarithms Exponents Sum and Logistic on built spline