Construction of Fuzzy Control Charts Based on Weighted Possibilistic Mean

The problem of constructing control charts for fuzzy data has been considered in literature. The proposed transformation approaches and direct fuzzy approaches have their advantages and disadvantages. The representative values charts based on transformation methods are often recommended in practical application. For representing a fuzzy set by a crisp value, the weight of importance of the members assigned with some membership levels in a fuzzy set should be considered, and the possibility theory can be employed to deal with such problem. In this article, we propose to employ the weighted possibilistic mean (WPM), weighted interval valued possibilistic mean (WIVPM) of fuzzy number as a sort of representative values for the fuzzy attribute data, and establish new fuzzy control charts with WPM and WIVPM. The performance of the charts is compared to the existing fuzzy charts with a fuzzy c-chart example via newly defined average number of inspection for variation of control state.     

Perspective of practical biological control and population theories

We have not yet had sufficient theoretical explanation for successful biological control in which a key pest is controlled after an introduction of natural enemies. I compare here real features of successful biological control and theoretical host–parasitoid population models to reduce the gap between theory and practice. I first review the historical interaction between classical biological control projects and theoretical population models. Second, I consider the importance of host refuges in host–parasitoid population dynamics as concerns the mechanisms of low and stable host density. The importance of density–dependent parasitism through parasitoid reproduction in multivoltine host–parasitoid systems and supplemental generalist natural enemies are also discussed. Finally, I consider the difference in tactics for classical biological control and for augmentation of natural enemies in annual crop systems. Received: December 20, 1998 / Accepted: January 15, 1999     

An impossibility theorem for matching problems

This paper studies the possibility of strategy-proof rules yielding satisfactory solutions to matching problems. Alcalde and Barberá (Econ Theory 4:417–435, 1994) and Sönmez (Econ Des 1:365–380, 1994) show that efficient and individually rational matching rules are manipulable. We pursue the possibility of strategy-proof matching rules by relaxing efficiency to the weaker condition of respect for unanimity. First, we prove that a strategy-proof rule exists that is individually rational and respects unanimity. However, this rule is unreasonable in the sense that mutually best pairs of agents are matched on only rare occasions. In order to explore the possibility of better matching rules, we introduce a natural condition of “respect for 2-unanimity.” Respect for 2-unanimity states that a mutually best pair of agents should be matched, and an agent wishing to being unmatched should be unmatched. Our second result is negative. Secondly, we prove that no strategy-proof rule exists that respects 2-unanimity. This result implies Roth (Math Oper Res 7:617–628, 1962; J Econ Theory 36:277–288, 1985) showing that stable rules are manipulable.     

Second-order asymptotic comparison of estimators under φ-divergence loss

Our main concern is on the second-order asymptotic optimality problem of estimators. The φ-divergence loss is used as a criterion for evaluating the performance of estimators. In the comparison problem of any two estimators, the condition that one estimator dominates another estimator under the φ-divergence risk is given by evaluating the second-order term in the difference between the risks. As a result, it is proved that the condition is characterized by a peculiar value of the φ-divergence loss, which is called the divergence-loss coefficient. Furthermore, it is shown that the comparison based on the φ-divergence loss does not correspond with that based on any standard loss functions including the mean squared error, the absolute loss and the 0-1 loss. In addition, a necessary and sufficient condition for an estimator to be second-order admissible is derived.     

SECOND ORDER ASYMPTOTIC COMPARISON OF ESTIMATORS UNDER UNIVERSAL DOMINATION CRITERION

The main purpose of this paper is to formulate theories of universal optimality, in the sense that some criteria for performances of estimators are considered over a class of loss functions. It is shown that the difference of the second order terms between two estimators in any risk functions is expressed as a form which is characterized by a peculiar value associated with the loss functions, which is referred to as the loss coefficient. This means that the second order optimal problem is completely characterized by the value of the loss coefficient. Furthermore, from the viewpoint of change of the loss coefficient, the relationship between two estimators is classified into six types. On the basis of this classification, the concept of universal second order admissibility is introduced. Some sufficient conditions are given to determine whether any estimators are universally admissible or not.     

ATTRIBUTION ERROR IN ECONOMIC VOTING: EVIDENCE FROM TRADE SHOCKS

This article exploits the international transmission of business cycles to examine the prevalence of attribution error in economic voting in a large panel of countries from 1990 to 2009. We find that voters, on average, exhibit a strong tendency to oust the incumbent governments during an economic downturn, regardless of whether the recession is home‐grown or merely imported from trading partners. However, we find important heterogeneity in the extent of attribution error. A split sample analysis shows that countries with more experienced voters, more educated voters, and possibly more informed voters—all conditions that have been shown to mitigate other voter agency problems—do better in distinguishing imported from domestic growth. (JEL E3, E6)     

Covariate selection for estimating the causal effect of control plans by using causal diagrams

Summary. Consider a case where cause–effect relationships between variables can be described by a causal path diagram and the corresponding linear structural equation model. The paper proposes a graphical selection criterion for covariates to estimate the causal effect of a control plan. For designing the control plan, it is essential to determine both covariates that are used for control and covariates that are used for identification. The selection of covariates used for control is only constrained by the requirement that the covariates be non-descendants of a treatment variable. However, the selection of covariates used for identification is dependent on the selection of covariates used for control and is not unique. In the paper, the difference between covariates that are used for identification is evaluated on the basis of the asymptotic variance of the estimated causal effect of an effective control plan. Furthermore, the results can be also described in terms of a graph structure.     

Universal admissibility in second order asymptotics

Some sufficient conditions for an estimator to be universally second order admissible are derived. Those sufficient conditions consist of the elementary integrals with respect to the Fisher information and the limits of some functions characterized by the dealt statistical model, and thus can be checked with comparative ease. In location model and scale model, the sufficient condition for the linear estimator with respect to the maximum likelihood estimator (MLE) to be universally second order admissible is given. Furthermore, a guide for classifying any estimator into either the universal admissibility or the non-universal admissibility is proposed.     

On Second Order Admissibilities in Two-Parameter Logistic Regression Model

In two-parameter family of distribution, conditions for a modified maximum likelihood estimator to be second-order admissible are given. Applying these results to two-parameter logistic regression model, it is shown that the maximum likelihood estimator is always second-order inadmissible and the Rao-Blackwellized minimum logit chi-squared estimator is second-order admissible if and only if the number of the doses is greater than or equal to 6.     

Asymptotics of Likelihood Ratio Tests for General One-sided Hypotheses in the Two-sample Normal Model

We consider the general one-sided hypotheses testing problem expressed as H₀: θ₁ ? h(θ₂) versus H₁: θ₁ < h(θ₂), where h( · ) is not necessary differentiable. The values of the right and the left differential coefficients, h′_?( · ) and h′₊( · ), at nondifferentiable points play an essential role in constructing the appropriate testing procedures with asymptotic size α on the basis of the likelihood ratio principle. The likelihood ratio testing procedure is related to an intersection–union testing procedure when h′_?(θ₂) ? h′₊(θ₂) for all θ₂, and to a union–intersection testing procedure when there exists a θ₂ such that h′_?(θ₂) < h′₊(θ₂).