https://popups.uliege.be/3041-539x/index.php?id=1981 Publications of Auteurs Edgars K. Vasermanis fr 0 https://popups.uliege.be/3041-539x/index.php?id=2459 Project portfolio investment is a crucial decision in many organizations, which must make informed decisions on investment, where the appropriate distribution of investment is complex, due to varying levels of risk, resource requirements, and interaction among the proposed projects. In this paper, we discuss an analytical optimal solution to the mean-variance formulation of the problem of optimization of multiperiod investment strategy for project portfolio. Specifically, analytical optimal multiperiod investment strategy for project portfolio is derived. An efficient algorithm is proposed in order to maximize the expected value of the terminal wealth under constraint that the variance of the terminal wealth is not greater than a preassigned risk level or to minimize the variance of the terminal wealth under constraint that the expected terminal wealth is not smaller than a preassigned level. A numerical example is given. Thu, 22 Aug 2024 10:03:16 +0200 Thu, 10 Oct 2024 09:46:52 +0200 https://popups.uliege.be/3041-539x/index.php?id=2459 https://popups.uliege.be/3041-539x/index.php?id=2184 A primary application of regression analysis is prediction. In this paper, we consider the definition of the domain of the model in which prediction is valid. This is important because prediction made outside the domain may be unacceptably different from the true responses. We provide a criterion that can be used to decide whether prediction is valid at a certain point. The criterion is based on the existence of an unbiased estimate of the distribution function associated to the "future" observation. In addition, in the context of regression analysis, the categorical control problem that is quite different from the numerical control problem in the setting of the target is considered. Categorical control may be compared to interval prediction, whereas numerical control is compared to point prediction. Our derivation is based on the Scheffé-type simultaneous tolerance interval at two distinct points. Tue, 30 Jul 2024 12:43:28 +0200 Tue, 30 Jul 2024 12:43:35 +0200 https://popups.uliege.be/3041-539x/index.php?id=2184 https://popups.uliege.be/3041-539x/index.php?id=2051 In the present paper, for constructing the minimum risk estimators of state of stochastic systems, a new technique of invariant embedding of sample statistics in a loss function is proposed. This technique represents a simple and computationally attractive statistical method based on the constructive use of the invariance principle in mathematical statistics. Unlike the Bayesian approach, an invariant embedding technique is independent of the choice of priors. It allows one to eliminate unknown parameters from the problem and to find the best invariant estimator, which has smaller risk than any of the well-known estimators. Also the problem of how to select the total number of the observations optimally when a constant cost is incurred for each observation taken is discussed. To illustrate the proposed technique, an example is given. Fri, 26 Jul 2024 16:07:39 +0200 Fri, 26 Jul 2024 16:07:49 +0200 https://popups.uliege.be/3041-539x/index.php?id=2051 https://popups.uliege.be/3041-539x/index.php?id=1980 In the present paper, for optimization of interval estimators, a new technique of invariant embedding of sample statistics in a loss function is proposed. This technique represents a simple and computationally attractive statistical method based on the constructive use of the invariance principle in mathematical statistics. Unlike the Bayesian approach, an invariant embedding technique is independent of the choice of priors. The aim of the paper is to show how the invariance principle may be employed in the particular case of finding the interval estimators that are uniformly best invariant. The technique proposed here is a special case of more general considerations applicable whenever the statistical problem is invariant under a group of transformations, which acts transitively on the parameter space. This technique may be used for constructing the minimum risk estimators of state of computing anticipatory systems. To illustrate the proposed technique, examples are given. Fri, 19 Jul 2024 09:08:39 +0200 Fri, 19 Jul 2024 09:08:49 +0200 https://popups.uliege.be/3041-539x/index.php?id=1980