A new measure of indeterminacy for uncertain variables with application to portfolio selection

2021 ◽  
pp. 1-5
Author(s):  
Jin Liu ◽  
Jinsheng Xie ◽  
Hamed Ahmadzade ◽  
Mehran Farahikia
Keyword(s):  
Probability Theory ◽  
Portfolio Selection ◽  
Uncertainty Theory ◽  
Random Variable ◽  
Uncertain Variable ◽  
Uncertainty Distribution ◽  
Uncertain Variables ◽  
Selection Problems ◽  
Exponential Entropy

Entropy is a measure for characterizing indeterminacy of a random variable or an uncertain variable with respect to probability theory and uncertainty theory, respectively. In order to characterize indeterminacy of uncertain variables, the concept of exponential entropy for uncertain variables is proposed. For computing the exponential entropy for uncertain variables, a formula is derived via inverse uncertainty distribution. As an application of exponential entropy, portfolio selection problems for uncertain returns are optimized via exponential entropy-mean models. For better understanding, several examples are provided.

scholarly journals A New Formula for Calculating Uncertainty Distribution of Function of Uncertain Variables

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2429
Author(s):  
Yuxing Jia ◽  
Yuer Lv ◽  
Zhigang Wang
Keyword(s):  
Uncertainty Theory ◽  
Monotone Function ◽  
Uncertain Variable ◽  
Mathematical Tool ◽  
Human Beings ◽  
Uncertainty Distribution ◽  
Uncertain Variables ◽  
New Ideas ◽  
New Formula ◽  
Theory Uncertainty

As a mathematical tool to rationally handle degrees of belief in human beings, uncertainty theory has been widely applied in the research and development of various domains, including science and engineering. As a fundamental part of uncertainty theory, uncertainty distribution is the key approach in the characterization of an uncertain variable. This paper shows a new formula to calculate the uncertainty distribution of strictly monotone function of uncertain variables, which breaks the habitual thinking that only the former formula can be used. In particular, the new formula is symmetrical to the former formula, which shows that when it is too intricate to deal with a problem using the former formula, the problem can be observed from another perspective by using the new formula. New ideas may be obtained from the combination of uncertainty theory and symmetry.

UNCERTAIN DECISION MAKING AND ITS APPLICATION TO PORTFOLIO SELECTION PROBLEM

2014 ◽  
Vol 22 (01) ◽  
pp. 113-123 ◽  
Author(s):  
KAI YAO ◽  
XIAOYU JI
Keyword(s):  
Decision Making ◽  
Decision Theory ◽  
Utility Function ◽  
Portfolio Selection ◽  
Expected Utility ◽  
Random Variable ◽  
Uncertain Variable ◽  
Selection Problem ◽  
Portfolio Selection Problem ◽  
Objective Uncertainty

In the traditional decision theory, choice with undetermined consequence is usually regarded as random variable, which usually describes objective uncertainty. This paper first considers the human uncertainty in making decisions, and employs uncertain variable to describe the choice. Utility function is also employed in the paper, and expected utility is introduced as a criterion to rank the choices. At last, in order to illustrate the uncertain decision making method, a portfolio selection problem is considered.

SOME PROPERTIES OF CONTINUOUS UNCERTAIN MEASURE

2009 ◽  
Vol 17 (03) ◽  
pp. 419-426 ◽  
Author(s):  
XIN GAO
Keyword(s):  
Uncertainty Theory ◽  
Uncertain Variable ◽  
Expected Value ◽  
Critical Values ◽  
Uncertain Measure ◽  
Uncertainty Distribution ◽  
Convergence Theorems ◽  
Basic Properties

In this paper, we discuss some properties in uncertainty theory when uncertain measure is continuous. Firstly, the judgement conditions of continuous uncertain measure are proposed. Secondly, basic properties of uncertainty distribution and critical values of uncertain variable are proved. Finally, the convergence theorems for expected value are discussed.

(s, S) POLICY FOR UNCERTAIN SINGLE PERIOD INVENTORY PROBLEM

2013 ◽  
Vol 21 (06) ◽  
pp. 945-953 ◽  
Author(s):  
YUAN GAO ◽  
MEILIN WEN ◽  
SIBO DING
Keyword(s):  
Uncertainty Theory ◽  
Random Variable ◽  
Uncertain Variable ◽  
Market Demand ◽  
Inventory Problem ◽  
Setup Cost ◽  
Inventory Policy ◽  
Single Period ◽  
Subjective Estimation

The traditional single period inventory problem assumes that the market demand is a random variable. However, as an empirical or subjective estimation, market demand is better to be regarded as an uncertain variable. This paper is concerning with single period inventory problem under two main assumptions that (i) the market demand is an uncertain variable and (ii) a setup cost and an initial stock exist. Under the framework of uncertainty theory, the optimal inventory policy for uncertain single period inventory problem with an initial stock and a setup cost is derived, which is of (s,S) type. Also, some expansions are obtained.

Uncertainty Distribution of Some Composite Uncertain Variables

2017 ◽  
Vol 25 (04) ◽  
pp. 545-555
Author(s):  
Chongshuang Chen ◽  
Jiayin Tang ◽  
Jianbo Xiao ◽  
Lei Huang
Keyword(s):  
Lower Bound ◽  
Upper Bound ◽  
Measurable Function ◽  
Uncertain Variable ◽  
Uncertainty Distribution ◽  
Uncertain Variables ◽  
Convex Upward

In this paper, we named the composition by a real-valued measurable function and an uncertain variable as a composite uncertain variable. We focused on the uncertainty distribution for two kinds of composite uncertain variables. The conclusions show: (1) it exists a lower bound when the composed function is continuous and strictly monotonically decreasing at first and then strictly monotonically increasing (e.g. convex downward functions); (2) it exists an upper bound when the composed function is continuous and strictly monotonically increasing at first and then strictly monotonically decreasing (e.g. convex upward functions).

On inequalities of the Tchebychev type

1963 ◽  
Vol 59 (1) ◽  
pp. 135-146 ◽  
Author(s):  
J. F. C. Kingman
Keyword(s):  
Probability Theory ◽  
Euclidean Space ◽  
Random Variable ◽  
Dimensional Euclidean Space ◽  
Real Random Variable ◽  
Finite Dimensional

1. A type of problem which frequently occurs in probability theory and statistics can be formulated in the following way. We are given real-valued functions f(x), gi(x) (i = 1, 2, …, k) on a space (typically finite-dimensional Euclidean space). Then the problem is to set bounds for Ef(X), where X is a random variable taking values in , about which all we know is the values of Egi(X). For example, we might wish to set bounds for P(X > a), where X is a real random variable with some of its moments given.

SINE ENTROPY FOR UNCERTAIN VARIABLES

2013 ◽  
Vol 21 (05) ◽  
pp. 743-753 ◽  
Author(s):  
KAI YAO ◽  
JINWU GAO ◽  
WEI DAI
Keyword(s):  
Maximum Entropy ◽  
Uncertainty Theory ◽  
Maximum Entropy Principle ◽  
Expected Value ◽  
Uncertain Variables ◽  
Entropy Principle

Entropy is a measure of the uncertainty associated with a variable whose value cannot be exactly predicated. In uncertainty theory, it has been quantified so far by logarithmic entropy. However, logarithmic entropy sometimes fails to measure the uncertainty. This paper will propose another type of entropy named sine entropy as a supplement, and explore its properties. After that, the maximum entropy principle will be introduced, and the arc-cosine distributed variables will be proved to have the maximum sine entropy with given expected value and variance.

An entropy-based global sensitivity analysis for the structures with both fuzzy variables and random variables

2012 ◽  
Vol 227 (2) ◽  
pp. 195-212 ◽  
Author(s):  
Tang Zhangchun ◽  
Lu Zhenzhou ◽  
Pan Wang ◽  
Zhang Feng
Keyword(s):  
Failure Probability ◽  
Uncertainty Propagation ◽  
Random Variables ◽  
Uncertain Variable ◽  
Importance Measure ◽  
Similar Measure ◽  
Practical Applications ◽  
Fuzzy Variables ◽  
Global Sensitivity ◽  
Uncertain Variables

Based on the entropy of the uncertain variable, a novel importance measure is proposed to identify the effect of the uncertain variables on the system, which is subjected to the combination of random variables and fuzzy variables. For the system with the mixture of random variables and fuzzy variables, the membership function of the failure probability can be obtained by the uncertainty propagation theory first. And then the effect of each input variable on the output response of the system can be evaluated by measuring the shift between entropies of two membership functions of the failure probability, obtained before and after the uncertainty elimination of the input variable. The intersecting effect of the multiple input variables can be calculated by the similar measure. The mathematical properties of the proposed global sensitivity indicators are investigated and proved in detail. A simple example is first employed to demonstrate the procedure of solving the proposed global sensitivity indicators and then the influential variables of four practical applications are identified by the proposed global sensitivity indicators.

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