linear rank procedures on litter matched data in this communication we illustrate the use of some recent results on the application of linear rank procedures to censored matched data we re analyze the time to tumor littermatched data of mantel bohidar and ciminera 1977 discussing our choice of procedure and comparing our approach with the analysis of the same data by mantel and ciminera 1979 we also demonstrate the use of a convenient algorithm for computing efficient scores on censored data the data of mantel et al 1977 which we shall refer to as the rat litter data are timeto tumor arbitrarily right censored litter matched data for 50 rat litters for each litter there are observations on three female litter mates one received a treatment and the other two served as controls the goal of this analysis is to determine whether the treatment has had an effect on the uncensored distributions of the time to tumor

generalized means as model of compensative connectives abstract empirical investigations have shown that instead of strong logical and and or connectives there is often some degree of compensation in between in this paper the generalized means are proposed as connective operators for fuzzy set theory which easily allow for modelling the degree of compensation in a natural manner including the arithmetic and geometric means as well as the maximum and minimum operators as special cases numerical examples with empirical data form an illustration of this concept some aspects of applications to decision making are discussed

an accurate fractional brownian motion generator abstract a new algorithm for the simulation of fractional brownian motion is suggested this algorithm has been tested and compared with previous ones clear improvements in the statistical and scaling properties of the process built with our algorithm are shown large improvements have been achieved for short times and for hurst s exponents less then 0 5 i e for path with high fractal dimension a new strategy to generate fractional brownian motions based on the use of moments of increasing size blocks is then described this numerical method proved to be fast and accurate

fast and exact simulation of stationary gaussian processes through circulant embedding of the covariance matrix geostatistical simulations often require the generation of numerous realizations of a stationary gaussian process over a regularly meshed sample grid omega this paper shows that for many important correlation functions in geostatistics realizations of the associated process over m 1 equispaced points on a line can be produced at the cost of an initial fft of length 2m with each new realization requiring an additional fft of the same length in particular the paper first notes that if an m 1 times m 1 toeplitz correlation matrix r can be embedded in a nonnegative definite 2m times2m circulant matrix s exact realizations of the normal multivariate y sim cal n 0 r can be generated via ffts of length 2m theoretical results are then presented to demonstrate that for many commonly used correlation structures the minimal embedding in which m m is nonnegative definite extensions to simulations of stationary fields in higher dimensions are also provided and illustrated

on the dispersion measure of owa operators we describe some basic features of the owa operator we turn to the problem of determining the weights associated with this operator and particularly the maximal dispersion entropy approach we consider the possibility of using minimization of dispersion after discussing concerns with both maximization and minimization of dispersion we investigate the possibility of finding an optimal solution intermediate to these extremes we next consider alternative measures of dispersion we introduce a fundamental requirement for a measure of dispersion called the preference for equal division a number of general classes of dispersion measures are provided notable among these are those based on t norm and t conorm operators

aggregation of infinite sequences infinitary aggregation functions acting on sequences and possessing some a priori given properties as additivity comonotone additivity symmetry etc are investigated on the other side we discuss infinitary aggregation functions related to given extended aggregation functions where special attention is given to triangular norms triangular conorms and weighted means

repeated significance testing in survival analysis using a litter matched design this paper shows that asymptotically a general class of linear rank procedures in a litter matched design form a gauss markov process over time this result is used to develop a procedure for repeated significance testing for time to response differences in litter matched studies in addition an important class of two sample linear rank procedures is shown to have uncorrelated increments over time an example is given using a litter matched cancer study empirical indications of the accuracy of a method for approximating multivariate normal critical values are also given

a new version of 2 tuple fuzzy linguistic representation model for computing with words in this paper we provide a new proportional 2 tuple fuzzy linguistic representation model for computing with words cw which is based on the concept of symbolic proportion this concept motivates us to represent the linguistic information by means of 2 tuples which are composed by two proportional linguistic terms for clarity and generality we first study proportional 2 tuples under ordinal contexts then under linguistic contexts and based on canonical characteristic values ccvs of linguistic labels we define many aggregation operators to handle proportional 2 tuple linguistic information in a computational stage for cw without any loss of information our approach for this proportional 2 tuple fuzzy linguistic representation model deals with linguistic labels which do not have to be symmetrically distributed around a medium label and without the traditional requirement of having equal distance between them moreover this new model not only provides a space to allow a continuous interpolation of a sequence of ordered linguistic labels but also provides an opportunity to describe the initial linguistic information by members of a continuous linguistic scale domain which does not necessarily require the ordered linguistic terms of a linguistic variable being equidistant meanwhile under the assumption of equally informative which is defined by a condition based on the concept of ccv we show that our model reduces to herrera and mart spl inodot spl acute nez s translational 2 tuple fuzzy linguistic representation model

on the relation between compromise programming and ordered weighted averaging operator this paper will demonstrate a strong relationship between the compromise programming cp method and the ordered weighted averaging owa operator we will mathematically prove that the application of the cp method reflects the pessimistic view of the decision maker dm if the distance from the ideal point is minimized it will however present an optimistic dm if the distance from the nadir is maximized the owa operator may cover all of the optimistic neutral and pessimistic views a practical relationship will be developed between the power value of the cp method and the optimism degree of the dm based on fuzzy quantifiers the theoretical results will be illustrated by a real case study of a water resources management problem

characterization of the ordered weighted averaging operators this paper deals with the characterization of two classes of monotonic and neutral mn aggregation operators the first class corresponds to mn aggregators which are stable for the same positive linear transformations and presents the ordered linkage property the second class deals with mn idempotent aggregators which are stable for positive linear transformations with the same unit independent zeroes and ordered values these two classes correspond to the weighted ordered averaging operator owa introduced by yager in 1988 it is also shown that the owa aggregator can be expressed as a choquet integral

multivariate winning probabilities abstract stochastic dominance and statistical preference are two important tools for the pairwise comparison of random variables however pairwise methods are not always appropriate in the case of more than two alternatives in this work we generalize the notion of winning probability to the notion of multivariate winning probability the latter allows to establish a ranking with ties on any set of random variables and naturally leads to the notion of probabilistic preference we investigate the relationship between the latter notion and the classical notions of stochastic dominance and statistical preference

a karhunen loeve like expansion for 1 f processes via wavelets while so called 1 f or scaling processes emerge regularly in modeling a wide range of natural phenomena as yet no entirely satisfactory framework has been described for the analysis of such processes orthonormal wavelet bases are used to provide a new construction for nearly 1 f processes from a set of uncorrelated random variables

local stationarity and simulation of self affine intrinsic random functions gaussian intrinsic random functions with power law generalized covariance functions which in one dimension essentially correspond to fractional and integrated fractional brownian motions form a class of self affine models for random fields with a wide range of smoothness properties these random fields are nonstationary but appropriately filtered versions of them are stationary this work proves that most such random functions are locally stationary in a certain well defined sense this result yields an efficient and exact method of simulating all fractional and integrated fractional brownian motions

estimating the fractal dimension of a locally self similar gaussian process by using increments summary consider the problem of estimating the parameter ca of a stationary gaussian process with

spectral representation of fractional brownian motion in n dimensions and its properties fractional brownian motion fbm provides a useful model for processes with strong long term dependence such as 1 f sup spl beta spectral behavior however fbm s are nonstationary processes so that the interpretation of such a spectrum is still a matter of speculation to facilitate the study of this problem another model is provided for the construction of fbm from a white noise like process by means of a stochastic or ito integral in frequency of a stationary uncorrelated random process also a generalized power spectrum of the nonstationary fbm process is defined this new approach to fbm can be used to compute all of the correlations power spectra and other properties of fbm in this paper a number of these fbm properties are developed from this model such as the t sup h law of scaling the power law of fractional order the correlation of two arbitrary fbm s and the evaluation of the fractal dimension under various transformations this new treatment of fbm using a spectral representation is extended also for the first time to two or more topological dimensions in order to analyze the features of isotropic n dimensional fbm

new decision making techniques and their application in the selection of financial products we develop a new approach that uses the ordered weighted averaging owa operator in the selection of financial products in doing so we introduce the ordered weighted averaging distance owad operator and the ordered weighted averaging adequacy coefficient owaac operator these aggregation operators are very useful for decision making problems because they establish a comparison between an ideal alternative and available options in order to find the optimal choice the objective of this new model is to manipulate the attitudinal character of previous methods based on distance measures so that the decision maker can select financial products according to his or her degree of optimism which is also known as the orness measure the main advantage of using the owa operator is that we can generate a parameterized family of aggregation operators between the maximum and the minimum thus the analysis developed in the decision process by the decision maker is much more complete because he or she is able to select the particular case in accordance with his or her interests in the aggregation process the paper ends with an illustrative example that shows results obtained by using different types of aggregation operators in the selection of financial products

nonparametric identification of anisotropic elliptic correlations in spatially distributed data sets random fields are useful models of spatially variable quantities such as those occurring in environmental processes and medical imaging the fluctuations obtained in most natural data sets are typically anisotropic the parameters of anisotropy are often determined from the data by means of empirical methods or the computationally expensive method of maximum likelihood in this paper we propose a systematic method for the identification of geometric elliptic anisotropy parameters of scalar fields the proposed method is computationally efficient nonparametric noniterative and it applies to differentiable random fields with normal or lognormal probability density functions our approach uses sample based estimates of the random field spatial derivatives that we relate through closed form expressions to the anisotropy parameters this paper focuses on two spatial dimensions we investigate the performance of the method on synthetic samples with gaussian and matern correlations both on regular and irregular lattices the systematic anisotropy detection provides an important preprocessing stage of the data knowledge of the anisotropy parameters followed by suitable rotation and rescaling transformations restores isotropy thus allowing classical interpolation and signal processing methods to be applied

subjective and objective information in linguistic multi criteria group decision making abstract linguistic decision making systems represent situations that cannot be assessed with numerical information but it is possible to use linguistic variables this paper introduces new linguistic aggregation operators in order to develop more efficient decision making systems the linguistic probabilistic weighted average lpwa is presented its main advantage is that it considers subjective and objective information in the same formulation and considering the degree of importance that each concept has in the aggregation a key feature of the lpwa operator is that it considers a wide range of linguistic aggregation operators including the linguistic weighted average the linguistic probabilistic aggregation and the linguistic average further generalizations are presented by using quasi arithmetic means and moving averages an application in linguistic multi criteria group decision making under subjective and objective risk is also presented in the context of the european union law

a visibility graph averaging aggregation operator abstract the problem of aggregation is of considerable importance in many disciplines in this paper a new type of operator called visibility graph averaging vga aggregation operator is proposed this proposed operator is based on the visibility graph which can convert a time series into a graph the weights are obtained according to the importance of the data in the visibility graph finally the vga operator is used in the analysis of the taiex database to illustrate that it is practical and compared with the classic aggregation operators it shows its advantage that it not only implements the aggregation of the data purely but also conserves the time information meanwhile the determination of the weights is more reasonable

linguistic multiperson decision making based on the use of multiple preference relations in multiperson decision making situations it is quite natural that different decision makers who may have different background and knowledge will provide their preferences by different kinds of preference relations this paper proposes a linguistic multiperson decision making model lmdmm based on linguistic preference relations integrating fuzzy preference relations different types of multiplicative preference relations and multigranular linguistic preference relations in the lmdmm several transformation functions are first obtained to relate fuzzy preference relations and different types of multiplicative preference relations with multigranular linguistic preference relations then we design the selection process of the lmdmm based on the use of the fuzzy majority and the extended ordered weighted averaging operator or the 2 tuple ordered weighted averaging operator and discuss conditions under which the proposed selection process satisfies the social choice axioms at last we analyze the internal consistency of the proposed transformation functions the results in this paper are helpful to complete chiclana et al s fuzzy decision model integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations fuzzy sets and systems 97 1998 33 48

correlation structure of the discrete wavelet coefficients of fractional brownian motion it is shown that the discrete wavelet coefficients of fractional brownian motion at different scales are correlated and that their auto and cross correlation functions decay hyperbolically fast at a rate much faster than that of the autocorrelation of the fractional brownian motion itself the rate of decay of the correlation function in the wavelet domain is primarily determined by the number of vanishing moments of the analyzing wavelet

soft learning vector quantization and clustering algorithms based on ordered weighted aggregation operators this paper presents the development and investigates the properties of ordered weighted learning vector quantization lvq and clustering algorithms these algorithms are developed by using gradient descent to minimize reformulation functions based on aggregation operators an axiomatic approach provides conditions for selecting aggregation operators that lead to admissible reformulation functions minimization of admissible reformulation functions based on ordered weighted aggregation operators produces a family of soft lvq and clustering algorithms which includes fuzzy lvq and clustering algorithms as special cases the proposed lvq and clustering algorithms are used to perform segmentation of magnetic resonance mr images of the brain the diagnostic value of the segmented mr images provides the basis for evaluating a variety of ordered weighted lvq and clustering algorithms

an aligned rank test for censored data from randomized block designs summary the aligned rank test of hodges lehmann 1962 is extended to censored survival data collected in matched pairs or randomized blocks the test statistic for matched pairs is a direct extension of the original aligned rank test whereas the test statistic for use with block size k 2 is constructed from the paired data statistics comparing all possible pairs of treatments the efficiency of recovery of interblock information with censored matched pairs is examined by calculating the asymptotic efficiency of the proposed test relative to the sign test under a weibull model with a single overall censoring time the powers of these two tests and two other tests are compared under a more general model in a monte carlo study a numerical example illustrating the technique is included

the optimal group continuous logarithm compatibility measure for interval multiplicative preference relations based on the cowga operator the calculation of compatibility measures is an important technique employed in group decision making with interval multiplicative preference relations in this paper a new compatibility measure called the continuous logarithm compatibility which considers risk attitudes in decision making based on the continuous ordered weighted geometric averaging cowga operator is introduced we also develop a group continuous compatibility model gcc model by minimizing the group continuous logarithm compatibility measure between the synthetic interval multiplicative preference relation and the continuous characteristic preference relation furthermore theoretical foundations are established for the proposed model such as the sufficient and necessary conditions for the existence of an optimal solution the conditions for the existence of a superior optimal solution and the conditions for the existence of redundant preference relations in addition we investigate certain conditions for which the optimal objective function of the gcc model guarantees its efficiency as the number of decision makers increases finally practical illustrative examples are examined to demonstrate the model and compare it with previous methods

on the performance of box counting estimators of fractal dimension summary box counting estimators are popular for estimating fractal dimension however very little is known of their stochastic properties despite increasing statistical interest in their application we show that if the irregular curve to which the estimators are applied is modelled by a gaussian process concise formulae may be developed for asymptotic bias and variance of box counting estimators these formulae point to critical differences between a naive form of the box counting estimator based directly on the capacity definition of fractal dimension and a regression inspired version of that estimator

using trapezoids for representing granular objects applications to learning and owa aggregation we discuss the role and benefits of using trapezoidal representations of granular information we focus on the use of level sets as a tool for implementing many operations on trapezoidal sets we point out the simplification that the linearity of the trapezoid brings by requiring us to perform operations on only two level sets we investigate the classic learning algorithm in the case when our observations are granule objects represented as trapezoidal fuzzy sets an important issue that arises is the adverse effect that very uncertain observations have on the quality of our estimates we suggest an approach to addressing this problem using the specificity of the observations to control its effect we next consider the owa aggregation of information represented as trapezoids an important problem that arises here is the ordering of the trapezoidal fuzzy sets needed for the owa aggregation we consider three approaches to accomplish this ordering based on the location specificity and fuzziness of the trapezoids from these three different approaches three fundamental methods of ordering are developed one based on the mean of the 0 5 level sets another based on the length of the 0 5 level sets and a third based on the difference in lengths of the core and support level sets throughout this work particular emphasis is placed on the simplicity of working with trapezoids while still retaining a rich representational capability

induced 2 tuple linguistic generalized aggregation operators and their application in decision making the induced 2 tuple linguistic generalized ordered weighted averaging 2 tilgowa operator is presented this new aggregation operator extends previous approaches by using generalized means order inducing variables in the reordering of the arguments and linguistic information represented with the 2 tuple linguistic approach its main advantage is that it includes a wide range of linguistic aggregation operators thus its analyses can be seen from different perspectives and we obtain a much more complete picture of the situation considered and are able to select the alternative that best fits with our interests or beliefs we further generalize the 2 tilgowa by using quasi arithmetic means and choquet integrals the result is the quasi 2 tilowa operator and the 2 tuple linguistic induced quasi arithmetic choquet integral aggregation we conclude this paper by analysing the applicability of this new approach in a multi person linguistic decision making problem concerning product management

multiple stages grey target decision making method with incomplete weight based on multi granularity linguistic label the method of grey target decision making based on a multiple stages linguistic label is extended in this research firstly the multi granularity linguistic term sets are transformed into the same linguistic set next the choice rule of a target is put forward and the calculation of a target distance is developed furthermore weight models of criteria and of stages which are based on the requirement of maximum difference of alternatives and restriction of stage harmony are suggested in addition a model of lower and upper value of a target distance is put forward moreover the method is extended to the group decision making environment this model can help to avoid faulty decision making due to uncertainty the suggested method is applied to vendor evaluation of a commercial airplane in china

generalized ordered weighted utility proportional averaging hyperbolic absolute risk aversion operators and their applications to group decision making under the general utility we provide a new operator called gowupa operator under hara utility we present a new operator called the gowupa hara operator we put forward an orness measure of the gowupa hara operator we construct a new model for determining the weight of the gowupa hara operator we develop a new approach for multiple attributes group decision making this paper develops a new class of aggregation operator based on utility function which introduces the risk attitude of decision makers dms in the aggregation process first under the general framework of utility function we provide a new operator called the generalized ordered weighted utility proportional averaging gowupa operator and study its properties which are suitable for any utility function then under the hyperbolic absolute risk aversion hara utility function we propose another new operator named as the generalized ordered weighted utility proportional averaging hyperbolic absolute risk aversion gowupa hara operator and further investigate its families including a wide range of aggregation operators to determine the gowupa hara operator weights we put forward an orness measure of the gowupa hara operator and analyze its properties considering that different dms may have different opinions towards decision making and their opinions can be characterized by different orness measures we construct a new nonlinear optimization model to determine the optimal weights which can aggregate all the individual sets of weights into an overall set of weights furthermore based on the gowupa hara operator a method for multiple attribute group decision making magdm is developed finally an example is given to illustrate the application of the gowupa hara operator to the magdm

a fuzzy linguistic methodology to deal with unbalanced linguistic term sets many real problems dealing with qualitative aspects use linguistic approaches to assess such aspects in most of these problems a uniform and symmetrical distribution of the linguistic term sets for linguistic modeling is assumed however there exist problems whose assessments need to be represented by means of unbalanced linguistic term sets i e using term sets that are not uniformly and symmetrically distributed the use of linguistic variables implies processes of computing with words cw different computational approaches can be found in the literature to accomplish those processes the 2 tuple fuzzy linguistic representation introduces a computational model that allows the possibility of dealing with linguistic terms in a precise way whenever the linguistic term set is uniformly and symmetrically distributed in this paper we present a fuzzy linguistic methodology in order to deal with unbalanced linguistic term sets to do so we first develop a representation model for unbalanced linguistic information that uses the concept of linguistic hierarchy as representation basis and afterwards an unbalanced linguistic computational model that uses the 2 tuple fuzzy linguistic computational model to accomplish processes of cw with unbalanced term sets in a precise way and without loss of information

improving minimax disparity model to determine the owa operator weights determining the ordered weighted averaging owa operator weights is important in decision making applications several approaches have been proposed in the literature to obtain the associated weights this paper provides an alternative disparity model to identify the owa operator weights the proposed mathematical model extends the existing disparity approaches by minimizing the sum of the deviation between two distinct owa weights the proposed disparity model can be used for a preference ranking aggregation a numerical example in preference ranking and an application in search engines prove the usefulness of the generated owa weights

fitting time series models to nonstationary processes a general minimum distance estimation procedure is presented for nonstationary time series models that have an evolutionary spectral representation the asymptotic properties of the estimate are derived under the assumption of possible model misspecification for autoregressive processes with time varying coefficients the estimate is compared to the least squares estimate furthermore the behavior of estimates is explained when a stationary model is fitted to a nonstationary process

some induced ordered weighted averaging operators and their use for solving group decision making problems based on fuzzy preference relations abstract in r r yager d p filev operations for granular computing mixing words and numbers in proceedings of the fuzz ieee world congress on computational intelligence anchorage 1998 pp 123 128 yager and filev introduced the induced ordered weighted averaging iowa operator in this paper we provide some iowa operators to aggregate fuzzy preference relations in group decision making gdm problems these iowa operators when guided by fuzzy linguistic quantifiers allow the introduction of some semantics or meaning in the aggregation and therefore allow for a better control over the aggregation stage developed in the resolution process of the gdm problems in particular we present the importance iowa i iowa operator which applies the ordering of the argument values based upon the importance of the information sources the consistency iowa c iowa operator which applies the ordering of the argument values based upon the consistency of the information sources and the preference iowa p iowa operator which applies the ordering of the argument values based upon the relative preference values associated to each one of them we provide a procedure to deal with ties in respect to the ordering induced by the application of one of these iowa operators it consists of a sequential application of the above iowa operators we also present a selection process for gdm problems based on the concept of fuzzy majority and the above three iowa operators finally we analyse the reciprocity and consistency properties of the collective fuzzy preference relations obtained using iowa operators

aggregation functions means this two part state of the art overview on aggregation theory summarizes the essential information concerning aggregation issues an overview of aggregation properties is given including the basic classification on aggregation functions in this first part the stress is put on means i e averaging aggregation functions both with fixed arity n ary means and with multiple arities extended means

synthesis of accurate fractional gaussian noise by filtering this paper describes a method for generating long sample paths of accurate fractional gaussian noise fgn the increment process of fractional brownian motion fbm the method is based on a wold decomposition in which fgn is expressed as the output of a finite impulse response filter with discrete white gaussian noise as input the form of the ideal filter is derived analytically in the continuous time case for the finite length discrete time case an iterative projection algorithm incorporating a newton raphson step is described for computing the coefficients of a length n filter in a time approximately proportional to n fast convolution of discrete white gaussian noise with the computed filter impulse response yields arbitrarily long sequences which exactly match the correlation structure of fgn over a finite range of lags for values of the hurst parameter h smaller than a critical value hcritap0 85 and large n the finite length autocorrelation sequence of fgn is positive definite and this range of lags can be as large as the filter autocorrelation length when h h crit the finite length autocorrelation sequence of fgn is no longer positive definite and a modification is made to allow a wold decomposition the generated sequences then exactly match the autocorrelation structure of fgn over a more restricted range of lags which becomes smaller as h approaches unity

choquet like integrals abstract integrals based on a pseudo addition and a pseudo multiplication with properties similar to those of the choquet and the sugeno integral respectively are introduced the structure of convenient pseudo arithmetical operations leading to a choquet like integral is studied

a sequential selection process in group decision making with a linguistic assessment approach in this paper a sequential selection process in group decision making under linguistic assessments is presented where a set of linguistic preference relations represents individuals preferences a collective linguistic preference is obtained by means of a defined linguistic ordered weighted averaging operator whose weights are chosen according to the concept of fuzzy majority specified by a fuzzy linguistic quantifier then we define the concepts of linguistic non dominance linguistic dominance and strict dominance degrees as parts of the sequential selection process the solution alternative s is obtained by applying these concepts

method of multi criteria group decision making based on cloud aggregation operators with linguistic information abstract the cloud model which can synthetically describe the randomness and fuzziness of qualitative concepts and implement uncertain transformations between a qualitative concept and its quantitative instantiations has attracted considerable attention from researchers studying multi criteria group decision making problems involving linguistic information in this paper some operations of clouds and several new aggregation operators are proposed these include the cloud weighted arithmetic averaging cwaa operator cloud ordered weighted arithmetic averaging cowa operator and cloud hybrid arithmetic cha operator the conversion between linguistic variables and clouds is introduced based on this conversion a linguistic multi criteria group decision making method is developed in this method linguistic variables are first converted into clouds and then aggregated using cloud aggregation operators the proposed method is then compared to the existing methods to confirm its feasibility and rationality

wavelet analysis and synthesis of fractional brownian motion fractional brownian motion fbm offers a convenient modeling for nonstationary stochastic processes with long term dependencies and 1 f type spectral behavior over wide ranges of frequencies statistical self similarity is an essential feature of fbm and makes natural the use of wavelets for both its analysis and its synthesis a detailed second order analysis is carried out for wavelet coefficients of fbm it reveals a stationary structure at each scale and a power law behavior of the coefficients variance from which the fractal dimension of fbm can be estimated conditions for using orthonormal wavelet decompositions as approximate whitening filters are discussed consequences of discretization are considered and some connections between the wavelet point of view and previous approaches based on length measurements analysis or dyadic interpolation synthesis are briefly pointed out

induced aggregation operators we introduce the induced ordered weighted averaging iowa operator in these operators the argument ordering process is guided by a variable called the order inducing value a procedure for learning the weights from data is described we suggest a number of applications of these induced owa aggregation operators first we show its possibilities in modeling nearest neighbor rules next it is applied to the aggregation of complex objects such as matrices it is also used to establish a new class of information fusion models called best yesterday models finally we extend the idea of order induced aggregation to the choquet aggregation resulting in what we call the induced choquet ordered averaging i coa operator

generalized ordered weighted utility averaging hyperbolic absolute risk aversion operators and their applications to group decision making abstract this paper develops a new class of aggregation operator based on utility function which introduces the risk attitude of decision makers dms in the aggregation process first under the general framework of utility function we provide a new operator called the generalized ordered weighted utility averaging gowua operator and study its properties which are suitable for any utility function then under the hyperbolic absolute risk aversion hara utility function we propose another new operator named as the generalized ordered weighted utility averaging hyperbolic absolute risk aversion gowua hara operator and further investigate its families including a wide range of aggregation operators to determine the gowua hara operator weights we put forward an orness measure of the gowua hara operator and analyze its properties considering that different dms may have different opinions toward decision making and their opinions can be characterized by different orness measures we construct a new optimization model to determine the optimal weights which can aggregate all the individual sets of weights into an overall set of weights furthermore based on the gowua hara operator a method for the multiple attribute group decision making magdm is developed finally an example is given to illustrate the application of the gowua hara operator to the magdm