On a theorem of Wald and Wolfowitz on randomization in statistics

In [9] it is proved that to each randomized decision function, there exists an equivalent randomized decision rule (cf. [3]), provided that the space of decisions is Polish. The object of the present paper is to show that the above theorem is a special case of risk-equivalence (cf. [5]) even if the space of decisions is analytic.     

A variance minimization problem for a Markov decision process

In the steady state of a discrete time Markov decision process, we consider the problem to find an optimal randomized policy that minimizes the variance of the reward in a transition among the policies which give the mean not less than a specified value. The problem is solved by introducing a parametric Markov decision process with average cost criterion. It is shown that there exists an optimal policy which is a mixture of at most two pure policies. As an application, the toymaker's problem is discussed.     

The computational complexity of bilevel assignment problems

In bilevel optimization problems there are two decision makers, the leader and the follower, who act in a hierarchy. Each decision maker has his own objective function, but there are common constraints. This paper deals with bilevel assignment problems where each decision maker controls a subset of edges and each edge has a leader’s and a follower’s weight. The edges selected by the leader and by the follower need to form a perfect matching. The task is to determine which edges the leader should choose such that his objective value which depends on the follower’s optimal reaction is maximized. We consider sum- and bottleneck objective functions for the leader and follower. Moreover, if not all optimal reactions of the follower lead to the same leader’s objective value, then the follower either chooses an optimal reaction which is best (optimistic rule) or worst (pessimistic rule) for the leader. We show that all the variants arising if the leader’s and follower’s objective functions are sum or bottleneck functions are NP-hard if the pessimistic rule is applied. In case of the optimistic rule the problem is shown to be NP-hard if at least one of the decision makers has a sum objective function.     

Multiperson decision-making based on multiplicative preference relations

A multiperson decision-making problem, where the information about the alternatives provided by the experts can be presented by means of different preference representation structures (preference orderings, utility functions and multiplicative preference relations) is studied. Assuming the multiplicative preference relation as the uniform element of the preference representation, a multiplicative decision model based on fuzzy majority is presented to choose the best alternatives. In this decision model, several transformation functions are obtained to relate preference orderings and utility functions with multiplicative preference relations. The decision model uses the ordered weighted geometric operator to aggregate information and two choice degrees to rank the alternatives, quantifier guided dominance degree and quantifier guided non-dominance degree. The consistency of the model is analysed to prove that it acts coherently.     

On randomization of Halton quasi-random sequences

The problem of estimating the error of quasi-Monte Carlo methods by means of randomization is considered. The well known Koksma–Hlawka inequality enables one to estimate asymptotics for the error, but it is not useful in computational practice, since computation of the quantities occurring in it, the variation of the function and the discrepancy of the sequence, is an extremely timeconsuming and impractical process. For this reason, there were numerous attempts to solve the problem mentioned above by the probability theory methods. A common approach is to shift randomly the points of quasi-random sequence. There are known cases of the practical use of this approach, but theoretically it is scantily studied. In this paper, it is shown that the estimates obtained this way are upper estimates. A connection with the theory of cubature formulas with one random node is established. The case of Halton sequences is considered in detail. The van der Corput transformation of a sequence of natural numbers is studied, and the Halton points are constructed with its help. It is shown that the cubature formula with one free node corresponding to the Halton sequence is exact for some class of step functions. This class is explicitly described. The obtained results enable one to use these sequences more effectively for calculating integrals and finding extrema and can serve as a starting point for further theoretical studies in the field of quasi-random sequences.     

Using randomization tests to preserve type I error with response adaptive and covariate adaptive randomization

We demonstrate that clinical trials using response adaptive randomized treatment assignment rules are subject to substantial bias if there are time trends in unknown prognostic factors and standard methods of analysis are used. We develop a general class of randomization tests based on generating the null distribution of a general test statistic by repeating the adaptive randomized treatment assignment rule holding fixed the sequence of outcome values and covariate vectors actually observed in the trial. We develop broad conditions on the adaptive randomization method and the stochastic mechanism by which outcomes and covariate vectors are sampled that ensure that the type I error is controlled at the level of the randomization test. These conditions ensure that the use of the randomization test protects the type I error against time trends that are independent of the treatment assignments. Under some conditions in which the prognosis of future patients is determined by knowledge of the current randomization weights, the type I error is not strictly protected. We show that response adaptive randomization can result in substantial reduction in statistical power when the type I error is preserved. Our results also ensure that type I error is controlled at the level of the randomization test for adaptive stratification designs used for balancing covariates.     

四值非线性序集逻辑系统的随机化

利用赋值集的随机化方法,在四值非线性序集逻辑系统L24中提出了公式的随机真度和随机距离,建立了非线性序集逻辑系统上的随机逻辑度量空间.它是计量逻辑学和随机真度的相关理论在非线性序集逻辑系统上的推广.     

Markov ratio decision processes

A finite-state Markov decision process, in which, associated with each action in each state, there are two rewards, is considered. The objective is to optimize the ratio of the two rewards over an infinite horizon. In the discounted version of this decision problem, it is shown that the optimal value is unique and the optimal strategy is pure and stationary; however, they are dependent on the starting state. Also, a finite algorithm for computing the solution is given.     

A way to deal with fuzzy preferences in multi-criteria decision problems

This paper presents a special multiple criteria decision making approach for solving problems in context with fuzzy individual preferences.At first we briefly expose the proposed methodology. The individual preferences are explicitly given by a complete transitive relation R on a set of reference actions. The modelling of the decision-maker's preferences is obtained by means of fuzzy outranking relations. These fuzzy relations are based on a system of additive utility functions which are estimated by means of ordinal regression methods analysing the preference relation R.This is followed by a presentation of two real multicriteria problems which the proposed methodology has been applied to, i.e. a highway plan choice problem and a problem in marketing research dealing with the launching of a new product. In each application we tried to specify this method according to the specific structure of the problem considered.     

Exercisability Randomization of the American Option

Abstract

The valuation of American options is an optimal stopping time problem which typically leads to a free boundary problem. We introduce here the randomization of the exercisability of the option. This method considerably simplifies the problematic by transforming the free boundary problem into an evolution equation. This evolution equation can be transformed in a way that decomposes the value of the randomized option into a European option and the present value of continuously paid benefits. This yields a new binomial approximation for American options. We prove that the method is accurate and numerical results illustrate that it is computationally efficient.     

Optimal Estimator for Distributed Anonymous Observers

In this paper, we consider a distributed estimation problem in which multiple observations of a signal process are combined via the maximum function for the decision making. A key result established is that, under suitable technical conditions, the optimal decision function can be implemented by means of thresholds. A natural question is how to determine the optimal threshold value. We propose here an algorithm for threshold adjustment by means of training sequences. The algorithm is a variation of the Kiefer-Wolfowitz algorithm with expanding truncations and randomized differences. A result of the paper is to establish the convergence of the algorithm if the variance of observation noises is small enough.     

Application of ODT to constant volume autoignition problems

The One-Dimensional Turbulence (ODT) model is applied to a constant volume configuration by means of a periodic, one-dimensional domain subject to randomized ensemble members with initial inhomogeneous temperature fields and homogeneous mass fraction profiles. The multidimensional turbulent interactions in the flow are modeled by the separate implementation of turbulent advection and the diffusion-reaction processes, neglecting the mean advection of the system. On one hand, turbulent advection is modeled by means of the eddy events defined within the framework of ODT; on the other hand, the diffusion-reaction system is solved by means of the Zero-Mach limit conservation equations discretized with a 1D Finite Volume Method (FVM). The treatment is specialized in this work to constant volume systems. Due to the inherent stiffness of the diffusion-reaction system, an operator splitting approach is also included in the formulation. Results for n-Heptane chemistry comprising the temporal evolution of the heat release rate, pressure and normalized density-weighted displacement speed are shown and compared to DNS results from Yoo et al. [Combust. Flame 158 (2011) 1727-1741], in terms of individual ensemble members and mean ensemble behavior. The results show that it is possible to obtain reasonably good results in comparison to the DNS if an appropriate set of initial conditions is used. Furthermore, it is shown that the model uncertainty is negligible in comparison to the ensemble standard deviation introduced by randomized initial conditions. Overall, this work introduces the framework for constant volume autoignition in ODT and shows its efficiency for complex chemistry simulations. (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Evaluation of the genetic algorithm parameters on the optimization performance: a case study on pump-and-treat remediation design

In this study, the impacts of different crossover and encoding schemes on the performance of a genetic algorithm (GA) in finding optimal pump-and-treat (P&T) remediation designs are investigated. For this purpose, binary and Gray encodings of the decision variables are tested. Uniform and two-point crossover schemes are evaluated for two different crossover probabilities. Analysis is performed for two P&T system optimization scenarios. Results show that uniform crossover operator with Gray encoding outperforms the other alternatives for the complex problem with higher number of decision variables. On the other hand, when a simpler problem, which had a lower number of decision variables, is solved, the efficiency of GA is independent of the encoding and crossover schemes.     

Worst Case Complexity of Problems with Random Information Noise

We study the worst case complexity of solving problems for which information is partial and contaminated by random noise. It is well known that if information is exact then adaption does not help for solving linear problems, i.e., for approximating linear operators over convex and symmetric sets. On the other hand, randomization can sometimes help significantly. It turns out that for noisy information, adaption may lead to much better approximations than nonadaption, even for linear problems. This holds because, in the presence of noise, adaption is equivalent to randomization. We present sharp bounds on the worst case complexity of problems with random noise in terms of the randomized complexity with exact information. The results obtained are applied to thed-variate integration andL_∞-approximation of functions belonging to Hölder and Sobolev classes. Information is given by function evaluations with Gaussian noise of variance σ². For exact information, the two problems are intractable since the complexity is proportional to (1/ε)^qwhereqgrows linearly withd. For noisy information the situation is different. For integration, the ε-complexity is of order σ²/ε²as ε goes to zero. Hence the curse of dimensionality is broken due to random noise. for approximation, the complexity is of order σ²(1/ε)^q+2ln(1/ε), and the problem is intractable also with random noise.     

The classical decision problem and partial functions

We consider the decision problem for sets of sentences of first-order logic when instead of interpreting function symbols as total functions over the universe of a model (henceforth referred to as the usual interpretation) we interpret them as partial functions.We consider only standard classes, which are certain sets of prenex sentences specified by restrictions on the prefix and on the numbers ofk-place predicate and function symbols for eachk1. Standard classes are introduced in [1] and it is proved there that the decision problem for any set of prenex sentences specified by such restrictions reduces to that for the standard classes.We solve the decision problem completely for standard classes with at least one function symbol and both with and without equality.This problem was suggested to me by my supervisor, Professor Yuri Gurevich who was confident that the results would be very similar to those for the usual interpretation and could be achieved by similar techniques.     

Modification of two-step Monte Carlo algorithms based on the symmetry of the first step

Two-step Monte Carlo algorithms are modified taking into account the symmetry (i.e., invariance) of the first step about some initial vector parameter of the modeled trajectory. In the modification, the modeling of this parameter is formally transferred to the second step of the algorithm. In the “splitting method,” this means the randomization of the initial points of auxiliary trajectories. It is shown that the randomization can be improved by applying the Bellman principle.     

Existence of Exact Penalty and Its Stability for Nonconvex Constrained Optimization Problems in Banach Spaces

In this paper we use the penalty approach in order to study two constrained minimization problems. A penalty function is said to have the generalized exact penalty property if there is a penalty coefficient for which approximate solutions of the unconstrained penalized problem are close enough to approximate solutions of the corresponding constrained problem. In this paper we show that the generalized exact penalty property is stable under perturbations of cost functions, constraint functions and the right-hand side of constraints.     

Randomized distributions in event recording models for multiple processes

We consider the problem of allowing for fluctuations in "recording intensity," which is reducible to randomization by an appropriate parameter. The properties (including asymptotic properties) of randomized convolutions with uniform and gamma distributions are considered. Constructive algorithms are developed for computing randomized distributions. Numerical implementation of these algorithms has made it possible to compare randomized and nonrandomized distribution for some characteristic parameter values.Translated from Vychislitel'naya Matematika i Matematicheskoe Obespechenie EVM, pp. 84–93, 1985.     

Sufficiency and Jensen's inequality for conditional expectations

For finite sets of probability measures, sufficiency is characterized by means of certain positively homogeneous convex functions. The essential tool is a discussion of equality in Jensen's inequality for conditional expectations. In particular, it is shown that characterizations of sufficiency by Csiszár's f-divergence (1963, Publ. Math. Inst. Hung. Acad. Sci. Ser. A, 8, 85–107) and by optimal solutions of a Bayesian decision problem used by Morse and Sacksteder (1966, Ann. Math. Statist., 37, 203–214) can be proved by the same method.