extending fuzzy logics with many hedges abstract fuzzy logic aims at modeling logical reasoning with vague or imprecise statements which may contain linguistic hedges in fact many hedges e g very highly rather and slightly can be used simultaneously to express different levels of emphasis moreover each hedge might have a dual one e g slightly can be seen as a dual hedge of very thus it is necessary to extend systems of fuzzy logic with multiple hedges this work proposes two axiomatizations for multiple hedges as an expansion of a core fuzzy logic in one axiomatization hedges do not have any dual one while in the other each hedge can have its own dual one it is shown that the proposed logics not only cover a large class of hedge functions but also have all completeness properties as the underlying logic w r t the class of their chains as well as distinguished subclasses of their chains including standard completeness the axiomatizations are also extended to the first order level furthermore we present a method to build linguistic fuzzy logics based on the axiomatizations and a hedge algebra whose corresponding algebras use a linguistic truth domain taken from the hedge algebra for representing and reasoning with linguistically expressed human knowledge where truth values of vague sentences are given in linguistic terms

the decomposition of frontier cost function deviations into measures of technical and allocative efficiency abstract this paper presents a method for decomposing the deviations from a full frontier cost function into farrell 1957 measures of technical and allocative efficiency the method draws heavily on duality theory and requires no direct knowledge of the primal production frontier specification or its parameters thus the method is applicable to a broad class of cost functions including flexible functions such as the translog which do not possess analytically derivable underlying production functions the method easily generalizes to joint output production technologies where the decomposition of deviations from frontier profit functions would provide measures of technical allocative output mix and scale efficiency

adding similarity based reasoning capabilities to a horn fragment of possibilistic logic with fuzzy constants plfc is a first order possibilistic logic dealing with fuzzy constants and fuzzily restricted quantifiers the refutation proof method in plfc is mainly based on a generalized resolution rule which allows an implicit graded unification among fuzzy constants however unification for precise object constants is classical in order to use plfc for similarity based reasoning in this paper we extend a horn rule sublogic of plfc with similarity based unification of object constants the horn rule sublogic of plfc we consider deals only with disjunctive fuzzy constants and it is equipped with a simple and efficient version of plfc proof method at the semantic level it is extended by equipping each sort with a fuzzy similarity relation and at the syntactic level by fuzzily enlarging each non fuzzy object constant in the antecedent of a horn rule by means of a fuzzy similarity relation

estimating a changepoint boundary or frontier in the presence of observation error a range of problems in economics and statistics involve calculation of the boundary or frontier of the support of a distribution several practical and attractive solutions exist if the sampled distribution has a sharp discontinuity at the frontier but accuracy can be greatly diminished if the data are observed with error indeed if the error is additive and has variance 2 then inaccuracies are usually of order for small in this article we suggest an elementary method for reducing the effect of error to o 2 and show that refinements can improve accuracy still further to o 3 or less the problem is inherently ill posed however to such an extent that the frontier is generally not even identifiable unless the error distribution is known the latter assumption is unreasonable in most practical settings not in the least because the error is often asymmetrically distributed for example in the context of productivity analysis the error distribution tends to have a longer tail in the directi

fuzzy logic programming via multilattices we investigate the use of multilattices as the set of truth values underlying a general fuzzy logic programming framework on the one hand some theoretical results about ideals of a multilattice are presented in order to provide an ideal based semantics on the other hand a restricted semantics in which interpretations assign elements of a multilattice to each propositional symbol is presented and analysed

fuzzy dea a perceptual evalution method abstract dea data envelopment analysis is a non parametric technique for measuring and evaluating the relative efficiencies of a set of entities with common crisp inputs and outputs in fact in a real evaluation problem input and output data of entities evaluated often fluctuate these fluctuating data can be represented as linguistic variables characterized by fuzzy numbers for reflecting a kind of general feeling or experience of experts based on the fundamental ccr model a fuzzy dea model is proposed to deal with the efficiency evaluation problem with the given fuzzy input and output data furthermore an extension of the fuzzy dea model to a more general form is also proposed with considering the relationship between dea and ra regression analysis using the proposed fuzzy dea models the crisp efficiency in ccr model is extended to be a fuzzy number to reflect the inherent uncertainty in real evaluation problems

a comparative study of adjoint triples adjoint triples are helpful as basic operators used in several domains for example adjoint triples play an important role in two important frameworks multi adjoint logic programming and multi adjoint concept lattices this paper shows that adjoint triples are an interesting generalization of t norms and their residuated implications since they preserve their main properties as well as they help to increase the flexibility of the operators used for computation in the considered framework furthermore these operators will be related to other important general triples specifically implication triples and the definition of these latter operators will be improved

on the use of panel data in stochastic frontier models with improper priors abstract we consider a bayesian analysis of the stochastic frontier model with composed error under a commonly used class of partly noninformative prior distributions the existence of the posterior distribution and of posterior moments is examined viewing this model as a normal linear regression model with regression parameters corresponding to both the frontier and the inefficiency terms generates the insights used to derived results in a very wide framework it is found that in pure cross section models posterior inference is precluded under this usual class of priors existence of a well defined posterior distribution then crucially hinges upon the structure imposed on the inefficiency terms exploiting panel data naturally suggests the use of more structured models where bayesian inference can be conducted

on fixed points of multivalued functions on complete lattices and their application to generalized logic programs unlike monotone single valued functions multivalued mappings may have zero one or possibly infinitely many minimal fixed points the contribution of this work is twofold first we overview and investigate the existence and computation of minimal fixed points of multivalued mappings whose domain is a complete lattice and whose range is its power set second we show how these results are applied to a general form of logic programs where the truth space is a complete lattice we show that a multivalued operator can be defined whose fixed points are in one to one correspondence with the models of the logic program

fuzzy data envelopment analysis and its application to location problems in this paper fuzzy dea data envelopment analysis models are proposed for evaluating the efficiencies of objects with fuzzy input and output data the obtained efficiencies are also fuzzy numbers that reflect the inherent ambiguity in evaluation problems under uncertainty an aggregation model for integrating fuzzy attribute values is provided in order to rank objects objectively using the proposed method a case study involving a restaurant location problem is analyzed in detail rent of establishment traffic amount level of security consumer consumption level and competition level are identified as the primary factors in determining an ideal location for a japanese style rotisserie restaurant based on field investigation the uncertain information on primary factors is represented by fuzzy numbers using the fuzzy aggregation model the best location of restaurant is determined the case study shows that fuzzy dea models can be quite useful for solving business problems under uncertainty

on estimation of monotone and concave frontier functions abstract when analyzing the productivity of firms one may want to compare how the firms transform a set of inputs x typically labor energy or capital into an output y typically a quantity of goods produced the economic efficiency of a firm is then defined in terms of its ability to operate close to or on the production frontier the boundary of the production set the frontier function gives the maximal level of output attainable by a firm for a given combination of its inputs the efficiency of a firm may then be estimated via the distance between the attained production level and the optimal level given by the frontier function from a statistical viewpoint the frontier function may be viewed as the upper boundary of the support of the population of firms density in the input and output space it is often reasonable to assume that the production frontier is a concave monotone function then a famous estimator in the univariate input and output case is the data envelopment analysis dea estimato

entropic perturbation method for solving a system of linear inequalities the problem of finding an x rn such that ax b and x 0 arises in numerous contexts we propose a new optimization method for solving this feasibility problem after converting ax b into a system of equations by introducing a slack variable for each of the linear inequalities the method imposes an entropy function over both the original and the slack variables as the objective function the resulting entropy optimization problem is convex and has an unconstrained convex dual if the system is consistent and has an interior solution then a closed form formula converts the dual optimal solution to the primal optimal solution which is a feasible solution for the original system of linear inequalities an algorithm based on the newton method is proposed for solving the unconstrained dual problem the proposed algorithm enjoys the global convergence property with a quadratic rate of local convergence however if the system is inconsistent the unconstrained dual is shown to be unbounded moreover the same algorithm can detect possible inconsistency of the system our numerical examples reveal the insensitivity of the number of iterations to both the size of the problem and the distance between the initial solution and the feasible region the performance of the proposed algorithm is compared to that of the surrogate constraint algorithm recently developed by yang and murty our comparison indicates that the proposed method is particularly suitable when the number of constraints is larger than that of the variables and the initial solution is not close to the feasible region

a gamma distributed stochastic frontier model abstract we modify the stochastic frontier model of aigner lovell and schmidt to allow the one sided part of the disturbance to have a two parameter gamma distribution rather than the less flexible half normal distribution maximum likelihood estimation and the estimation of firm specific inefficiency estimates require the evaluation of integrals which have no closed form and for which there are no polynomial approximations available we consider two methods of computing these integrals we also present a corrected ols estimator based on the methods of moments an application is presented for illustration we find that for these data the gamma distribution produces results which differ noticeably from those of three alternative formulations

pavelka style fuzzy logic in retrospect and prospect we trace the origin and development of pavelka style fuzzy logic discuss its significance and clarify some related misconceptions

on reductants in the framework of multi adjoint logic programming reductants are a special kind of fuzzy rules which constitute an essential theoretical tool for proving correctness properties as it has been reported when interpreted on a partially ordered structure a multi adjoint logic program has to include all its reductants in order to preserve the approximate completeness property after a short survey of the different notions of reductant that have been developed for multi adjoint logic programs we introduce a new and more adequate notion of reductant in the multi adjoint framework we study some of its properties and its relationships with other notions of reductants and provide an algorithm for computing all the reductants associated with a multi adjoint logic program

a top k query answering procedure for fuzzy logic programming we present a top k query answering procedure for fuzzy logic programming in which arbitrary computable functions may appear in the rule bodies to manipulate truth values the top k ranking problem i e determining the top k answers to a query becomes important as soon as the set of facts becomes quite large

valid tests of whether technical inefficiency depends on firm characteristics abstract we wish to test whether technical inefficiency depends on observable characteristics of the firm we consider a two step procedure in which the second step is a regression of estimated inefficiency on firm characteristics a valid test of the hypothesis of no effect requires an adjustment to the variance matrix of the estimates unfortunately the adjustment is not distribution free we show that this test is the lm test in the exponential case we also consider tests based on nonlinear least squares which do not require a distributional assumption the size and power of these tests are examined in simulations

semiparametric bayesian inference for stochastic frontier models in this paper we propose a semiparametric bayesian framework for the analysis of stochastic frontiers and efficiency measurement the distribution of inefficiencies is modelled nonparametrically through a dirichlet process prior we suggest prior distributions and implement a bayesian analysis through an efficient markov chain monte carlo sampler which allows us to deal with practically relevant sample sizes we also consider the case where the efficiency distribution varies with firm characteristics the methodology is applied to a cost frontier estimated from a panel data set on 382 u s hospitals

an energy function based design method for discrete hopfield associative memory with attractive fixed points an energy function based autoassociative memory design method to store a given set of unipolar binary memory vectors as attractive fixed points of an asynchronous discrete hopfield network dhn is presented the discrete quadratic energy function whose local minima correspond to the attractive fixed points of the network is constructed via solving a system of linear inequalities derived from the strict local minimality conditions the weights and the thresholds are then calculated using this energy function if the inequality system is infeasible we conclude that no such asynchronous dhn exists and extend the method to design a discrete piecewise quadratic energy function which can be minimized by a generalized version of the conventional dhn also proposed herein in spite of its computational complexity computer simulations indicate that the original method performs better than the conventional design methods in the sense that the memory can store and provide the attractiveness for almost all memory sets whose cardinality is less than or equal to the dimension of its elements the overall method together with its extension guarantees the storage of an arbitrary collection of memory vectors which are mutually at least two hamming distances away from each other in the resulting network

multiary α resolution principle for a lattice valued logic this paper focuses on resolution based automated reasoning theory in a lattice valued logic system with truth values that are defined in a lattice valued logical algebraic structure lattice implication algebras lias which essentially aims to extend the classical logic to handle automated deduction under an uncertain environment concretely we investigate a generalization of the known conjunctive normal form cnf in the classical logic representation that we call the generalized conjunctive normal form gcnf which aims to characterize the constants and implication connectives that are essentially different from the ones in classical logic we then extend the established resolution principle at a certain truth value level called resolution in this lattice valued logic to a more general form i e from binary resolution to multiary resolution the extension to multiary resolution starts from lattice valued propositional logic lp x while its theorems of both soundness and completeness are proved multiary resolution principle is then further established in the corresponding lattice valued first order logic lf x along with its soundness theorem lifting lemma and completeness theorem meanwhile an important result that multiary resolution principle in lf x can be equivalently transformed into that in lp x to some extent is obtained all these works will theoretically support the establishment of an automated reasoning algorithm and its implementation with further applications into automated deduction and decision making problems under uncertainty

on expansions of wnm t norm based logics with truth constants this paper focuses on completeness results about generic expansions of propositional weak nilpotent minimum wnm logics with truth constants indeed we consider algebraic semantics for expansions of these logics with a set of truth constants r r c for a suitable countable c 0 1 and provide a full description of completeness results when i the t norm is a weak nilpotent minimum satisfying the finite partition property and ii the set of truth constants covers all the unit interval in the sense that each interval of the partition contains values of c in its interior

adding truth constants to logics of continuous t norms axiomatization and completeness results in this paper we study generic expansions of logics of continuous t norms with truth constants taking advantage of previous results for lukasiewicz logic and more recent results for godel and product logics indeed we consider algebraic semantics for expansions of logics of continuous t norms with a set of truth constants r r c for a suitable countable c 0 1 and provide a full description of completeness results when i the t norm is a finite ordinal sum of lukasiewicz godel and product components ii the set of truth constants covers all the unit interval in the sense that each component of the t norm contains at least one value of c different from the bounds of the component and iii the truth constants in lukasiewicz components behave as rational numbers

a zero inefficiency stochastic frontier model abstract traditional stochastic frontier models impose inefficient behavior on all firms in the sample of interest if the data under investigation represent a mixture of both fully efficient and inefficient firms then off the shelf frontier models are statistically inadequate we introduce the zero inefficiency stochastic frontier model which can accommodate the presence of both efficient and inefficient firms in the sample we derive the corresponding log likelihood function conditional mean of inefficiency to estimate observation specific inefficiency and discuss testing for the presence of fully efficient firms we provide both simulated evidence as well as an empirical example which demonstrates the applicability of the proposed method

a zero inefficiency stochastic frontier estimator traditional stochastic frontier models impose inefficient behavior on all firms in the sample of interest if the data under investigation represent a mixture of both fully efficient and inefficient firms then off the shelf frontier models are statistically inadequate we introduce the zero inefficiency stochastic frontier model which can accommodate the presence of both efficient and inefficient firms in the sample we derive the corresponding log likelihood function conditional mean of inefficiency to estimate observation specific inefficiency and discuss testing for the presence of fully efficient firms we provide both simulated evidence as well as an empirical example which demonstrates the applicability of the proposed method jel classification no c13 c23 c33

stochastic frontier models a bayesian perspective abstract a bayesian approach to estimation prediction and model comparison in composed error production models is presented a broad range of distributions on the inefficiency term define the contending models which can either be treated separately or pooled posterior results are derived for the individual efficiences as well as for the parameters and the differences with the usual sampling theory approach are highlighted the required numerical integrations are handled by monte carlo methods with importance sampling and an empirical example illustrates the procedures

maximum likelihood estimation of stochastic frontier models by the fourier transform the paper is concerned with several kinds of stochastic frontier models whose likelihood function is not available in closed form first with output oriented stochastic frontier models whose one sided errors have a distribution other than the standard ones exponential or half normal the gamma and beta distributions are leading examples second with input oriented stochastic frontier models which are common in theoretical discussions but not in econometric applications third with two tiered stochastic frontier models when the one sided error components follow gamma distributions fourth with latent class models with gamma distributed one sided error terms fifth with models whose two sided error component is distributed as stable paretian and the one sided error is gamma the principal aim is to propose approximations to the density of the composed error based on the inversion of the characteristic function which turns out to be manageable using the fourier transform procedures that are based on the asymptotic normal form of the log likelihood function and have arbitrary degrees of asymptotic efficiency are also proposed implemented and evaluated in connection with output oriented stochastic frontiers the new methods are illustrated using data for us commercial banks electric utilities and a sample from the national youth longitudinal survey

a fuzzy datalog deductive database system this paper describes a proposal for a deductive database system with fuzzy small mathsf datalog as its query language concepts supporting the fuzzy logic programming system small mathsf bousi sim small mathsf prolog are tailored to the needs of the deductive database system small mathsf des we develop a version of fuzzy small mathsf datalog where programs and queries are compiled to the small mathsf des core small mathsf datalog language weak unification and weak sld resolution are adapted for this setting and extended to allow rules with truth degree annotations we provide a public implementation in small mathsf prolog which is open source multiplatform portable and in memory featuring a graphical user interface a distinctive feature of this system is that unlike others we have formally demonstrated that our implementation techniques fit the proposed operational semantics we also study the efficiency of these implementation techniques through a series of detailed experiments moreover a database example for a recommender system is used to illustrate some of the features of the system and its usefulness

formal theories and linguistic descriptions abstract in this contribution we discuss properties which a formal logical theory we call it the theory of evaluating expressions should have to serve us as a tool for modeling of the meaning of a certain class of natural language expressions called evaluating linguistic expressions we further construct a formal theory for modeling of the meaning of sets of fuzzy if then rules called linguistic descriptions and finally present a basic schema of fuzzy logic deduction the basis for our study is first order fuzzy logic with evaluated syntax

a stochastic production frontier model with group specific temporal variation in technical efficiency abstract the purpose of this study was to develop a stochastic frontier production model that relaxes the assumption of identical temporal patterns of technical inefficiency and the restriction of specific forms of temporal pattern without imposing other restrictions specifically the proposed model allows group specific patterns of temporal change in technical inefficiency this is a direct generalization of the model of lee and schmidt 1993 lee y h schmidt p 1993 a production frontier model with flexible temporal variation in technical inefficiency in fried h lovell c a k schmidt p eds the measurement of productive efficiency techniques and applications oxford university press oxford which imposed an identical temporal pattern of technical inefficiency on all firms when applied to indonesian rice farms the model is shown to be an improvement on the earlier model of lee and schmidt 1993 and the changes in the efficiency rankings of farms over time demonstrate the model s flexibility

recurrent correlation associative memories a feature space perspective in this paper we analyze a model of recurrent kernel associative memory rkam recently proposed by garcia and moreno we show that this model consists in a kernelization of the recurrent correlation associative memory rcam of chiueh and goodman in particular using an exponential kernel we obtain a generalization of the well known exponential correlation associative memory ecam while using a polynomial kernel we obtain a generalization of higher order hopfield networks with hebbian weights we show that the rkam can outperform the aforementioned associative memory models becoming equivalent to them when a dominance condition is fulfilled by the kernel matrix to ascertain the dominance condition we propose a statistical measure which can be easily computed from the probability distribution of the interpattern hamming distance or directly estimated from the memory vectors the rkam can be used below saturation to realize associative memories with reduced dynamic range with respect to the ecam and with reduced number of synaptic coefficients with respect to higher order hopfield networks

a comparison of fuzzy and annotated logic programming the aim of this paper is to contribute to the study of relationships between different formalism for handling uncertainty in logic programming knowledge based systems and deductive databases generalized annotated programs with restricted semantics rga programs are well suited to fit real world data we show that rga programs with constant annotations in body are equivalent to programs with left discontinuous annotation and with possibly non computable semantics our model of fuzzy logic programming flp can well handle recursive programs we show that flp has the same expressive power as rga programs without constant annotations in body of rules we introduce several syntactical transformations of programs and study their models and production operators we introduce a new efficient procedural semantics for rga programs and show connections between different sorts of computed answers

a generalized convergence theorem for neural networks a neural network model is presented in which each neuron performs a threshold logic function the model always converges to a stable state when operating in a serial mode and to a cycle of length at most 2 when operating in a fully parallel mode this property is the basis for the potential applications of the model such as associative memory devices and combinatorial optimization the two convergence theorems for serial and fully parallel modes of operation are reviewed and a general convergence theorem is presented that unifies the two known cases new relations between the neural network model and the problem of finding a minimum cut in a graph are obtained

a method of simulated moments for estimation of discrete response models without numerical integration this paper proposes a simple modification of a conventional method of moments estimator for a discrete response model replacing response probabilities that require numerical integration with estimators obtained by monte carlo simulation this method of simulated moments msm does not require precise estimates of these probabilities for consistency and asymptotic normality relying instead on the law of large numbers operating across observations to control simulation error and hence can use simulations of practical size the method is useful for models such as high dimensional multinomial probit mnp where computation has restricted applications

on the estimation of technical inefficiency in the stochastic frontier production function model abstract the error term in the stochastic frontier model is of the form v u where v is a normal error term representing pure randomness and u is a non negative error term representing technical inefficiency the entire v u is easily estimated for each observation but a previously unsolved problem is how to separate it into its two components v and u this paper suggests a solution to this problem by considering the expected value of u conditional on v u an explicit formula is given for the half normal and exponential cases

proximity based unification theory similarity based logic programming has been proposed to enhance the logic programming paradigm with similarity relations in order to represent and manage vague or imprecise information a similarity relation is a reflexive symmetric transitive fuzzy binary relation extending the standard notion of equivalence relation with the purpose of weakening the concept of equality as is reported similarity relations have significant limitations due to the transitivity restriction that can lead to representing fuzzy information incorrectly in some situations recently we have proposed the use of proximity relations i e reflexive symmetric fuzzy binary relations in the context of a logic programming system called bousi prolog as a way of solving this problem and generalizing later approaches exclusively based on similarity relations however a naive combination of proximity relations and the existing unification algorithms may cause incompleteness problems hence in this paper we introduce an accurate definition of proximity between expressions terms or atomic formulas and a new unification algorithm able to manage proximity relations properly the so called weak unification algorithm is an extension of martelli and montanari s unification algorithm supported by the new notion of proximity as major relevant properties we prove that the weak unification algorithm terminates and it is sound i e it produces a weak unifier of two expressions if they are unifiable and complete i e it is able to compute a weak most general unifier for two unifiable expressions

rbf based neurodynamic nearest neighbor classification in real pattern space superposition of radial basis functions centered at given prototype patterns constitutes one of the most suitable energy forms for gradient systems that perform nearest neighbor classification with real valued static prototypes it is shown in this paper that a continuous time dynamical neural network model employing a radial basis function and a sigmoid multi layer perceptron sub networks is capable of maximizing such an energy form locally thus performing almost perfectly nearest neighbor classification when initiated by a distorted pattern the proposed design scheme allows for explicit representation of prototype patterns as network parameters as well as augmenting additional or forgetting existing memory patterns the dynamical classification scheme implemented by the network eliminates all comparisons which are the vital steps of the conventional nearest neighbor classification process the performance of the proposed network model is demonstrated on binary and gray scale image reconstruction applications

solving linear inequalities in a least squares sense in 1980 s p han least squares solution of linearlnequalities tech report tr 2141 mathematics research center university of wisconsin madison 1980 described a finitely terminating algorithm for solving a system ax leqslant b of linear inequalities in a least squares sense the algorithm uses a singular value decomposition of a submatrix of a on each iteration making it impractical for all but the smallest problems this paper shows that a modification of han s algorithm allows the iterates to be computed using qr factorization with column pivoting which significantly reduces the computational cost and allows efficient updating downdating techniques to be used the effectiveness of this modification is demonstrated implementation details are given and the behaviour of the algorithm discussed theoretical and numerical results are shown from the application of the algorithm to linear separability problems

extended multi adjoint logic programming abstract extended multi adjoint logic programming arises as an extension of multi adjoint normal logic programming where constraints and a special type of aggregator operator have been included the use of this general aggregator operator permits to consider for example different negation operators in the body of the rules of a logic program we have introduced the syntax and the semantics of this new paradigm as well as an interesting mechanism for obtaining a multi adjoint normal logic program from an extended multi adjoint logic program this mechanism will allow us to establish technical properties relating the different stable models of both logic programming frameworks moreover it makes possible that the already developed and future theory associated with stable models of multi adjoint normal logic programs can be applied to extended multi adjoint logic programs

fuzzy logic programming reduced to reasoning with attribute implications abstract we present a link between two types of logic systems for reasoning with graded if then rules the system of fuzzy logic programming flp in sense of vojtas and the system of fuzzy attribute logic fal in sense of belohlavek and vychodil we show that each finite theory consisting of formulas of fal can be represented by a definite program so that the semantic entailment in fal can be characterized by correct answers for the program conversely we show that for each definite program there is a collection of formulas of fal so that the correct answers can be represented by the entailment in fal using the link we can transport results from fal to flp and vice versa which gives us e g a syntactic characterization of correct answers based on pavelka style armstrong like axiomatization of fal we further show that entailment in flp is reducible to reasoning with boolean attribute implications and elaborate on related issues including properties of least models

a smooth nonparametric conditional quantile frontier estimator abstract traditional estimators for nonparametric frontier models dea fdh are very sensitive to extreme values outliers recently aragon et al 2005 nonparametric frontier estimation a conditional quantile based approach econometric theory 21 358 389 proposed a nonparametric frontier model and estimator based on a suitably defined conditional quantile which is more robust to extreme values outliers their estimator is based on a nonsmooth empirical conditional distribution in this paper we propose a new smooth nonparametric conditional quantile estimator for the frontier model our estimator is a kernel based conditional quantile estimator that builds on early work of azzalini 1981 a note on the estimation of a distribution function and quantiles by a kernel method biometrika 68 326 328 it is computationally simple resistant to outliers and extreme values and smooth in addition the estimator is shown to be consistent and n asymptotically normal under mild regularity conditions we also show that our estimator s variance is smaller than that of the estimator proposed by aragon et al a simulation study confirms the asymptotic theory predictions and contrasts our estimator with that of aragon et al

syntax and semantics of multi adjoint normal logic programming abstract multi adjoint logic programming is a general framework with interesting features which involves other positive logic programming frameworks such as monotonic and residuated logic programming generalized annotated logic programs fuzzy logic programming and possibilistic logic programming one of the most interesting extensions of this framework is the possibility of considering a negation operator in the logic programs which will improve its flexibility and the range of real applications this paper introduces multi adjoint normal logic programming which is an extension of multi adjoint logic programming including a negation operator in the underlying lattice beside the introduction of the syntax and semantics of this paradigm we will provide sufficient conditions for the existence of stable models defined on a convex compact set of an euclidean space finally we will consider a particular algebraic structure in which sufficient conditions can be given in order to ensure the unicity of stable models of multi adjoint normal logic programs

fuzzy prolog a new approach using soft constraints propagation we present a definition of a fuzzy prolog language that models b 0 1 valued fuzzy logic and subsumes former approaches because it uses a truth value representation based on a union of sub intervals on 0 1 and is defined using general operators that can model different logics this extension to prolog is implemented by interpreting fuzzy reasoning as a set of constraints that are propagated through the rules by means of aggregation operators declarative and procedural semantics for fuzzy logic programs are given and their equivalence is proven in addition we present the implementation of an interpreter for this conceived language using constraint logic programming over real numbers

fuzzy turing machines normal form and limitative theorems abstract a normal form for fuzzy turing machines is proposed and examined this normal form is arithmetical in nature since the truth values are substituted by n ples of natural numbers and the operation interpreting the conjunction becomes a sort of truncated sum also some of the results in the paper enable us to emphasize the inadequacy of the notion of fuzzy turing machine for fuzzy computability i e that this notion is not a good candidate for a church thesis in the fuzzy mathematics framework

finitary coalgebraic multisemilattices and multilattices in this paper we continue the coalgebraization of the structure of multilattice specifically we introduce a coalgebraic characterization of the notion of finitary multi semi lattice a generalization of that of semilattice which arises naturally in several areas of computer science and provides the possibility of handling non determinism

a spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers abstract by blending seminal literature on non spatial stochastic frontier models with key contributions to spatial econometrics we develop a spatial autoregressive sar stochastic frontier for panel data the specification of the sar frontier allows efficiency to vary over time and across the cross sections efficiency is calculated from a composed error structure by assuming a half normal distribution for inefficiency the sar frontier is estimated using maximum likelihood methods taking into account the endogenous sar variable the application of the estimator to an aggregate production frontier for european countries highlights among other things the asymmetry between efficiency spillovers to and from a country

a simplification of the kopp diewert method of decomposing cost efficiency and some implications abstract earlier papers by kopp and diewert 1982 and zieschang 1983 have considered ways of decomposing the overall productive efficiency into technical and allocative components given the parameters of a frontier cost function this paper presents a simplified approach to the same problem and some implications for a possible solution of the greene problem greene 1980 a

fuzzy logic programming in this paper we consider the theory of fuzzy logic programming without negation our results cover logical systems with a wide variety of connectives ranging from t norm and conorms through conjunctors and disjunctors and their residuals to aggregation operators rules of our programs are many valued implications we emphasize that in contrast to other approaches our logic is truth functional i e according to p hajek we work in fuzzy logic in narrow sense we prove the soundness and the completeness of our formal model we deal with applications to threshold computation abduction fuzzy unification based on similarity we show that fuzzy unification based on similarities has applications to fuzzy databases and flexible querying

on the distribution of estimated technical efficiency in stochastic frontier models abstract we consider a stochastic frontier model with error e v u where v is normal and u is half normal we derive the distribution of the usual estimate of u e u e we show that as the variance of v approaches zero e u e u converges to zero while as the variance of v approaches infinity e u e converges to e u we graph the density of e u e for intermediate cases to show that e u e is a shrinkage of u towards its mean we derive and graph the distribution of e u e conditional on u we also consider the distribution of estimated inefficiency in the fixed effects panel data setting