The present paper deals with optimizing the maximum premium values using Hamiltonian-Jacobi-Bellamann equation. Here it is considering that quadratic and fractional power utility functions for different loss distributions of discrete analogues continuous distributions. It has seen function more beneficial to insured insurer. Numerical illustrations these two are given.

background: the celebrated generalized estimating equations (gee) approach is often used in longitudinal data analysis. while this method behaves robustly against misspecification of the working correlation structure, it has some limitations on efficacy of estimators, goodness-of-fit tests and model selection criteria. the quadratic inference functions (qif) is a new statistical methodology tha...

support vector regression (svr) solves regression problems based on the concept of support vector machine (svm). in this paper, a new model of svr with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...

in this paper, we first introduce the notion of $c$-affine functions for $c> 0$.then we deal with some properties of strongly convex functions in real inner product spaces by using a quadratic support function at each point which is $c$-affine. moreover, a hyers–-ulam stability result for strongly convex functions is shown.

in this paper, we consider convex quadratic semidefinite optimization problems and provide a primal-dual interior point method (ipm) based on a new kernel function with a trigonometric barrier term. iteration complexity of the algorithm is analyzed using some easy to check and mild conditions. although our proposed kernel function is neither a self-regular (sr) function nor logarithmic barrier ...