some kinds of view the fuzzy filters of bl algebras the concepts of view the mathml source fuzzy implicative positive implicative and fantastic filters of bl algebras are introduced and some related properties are investigated some characterizations of these generalized fuzzy filters are derived in particular we describe the relationships among ordinary fuzzy implicative positive implicative and fantastic filters view the mathml source fuzzy implicative positive implicative and fantastic filters and view the mathml source fuzzy implicative positive implicative and fantastic filters of bl algebras finally we prove that a fuzzy set f of a bl algebra l is an view the mathml source fuzzy implicative filter of l if and only if it is both an view the mathml source fuzzy positive implicative filter and an view the mathml source fuzzy fantastic filter

application of fuzzy soft set in decision making problems based on grey theory there are many uncertain problems in practical production and life which need decisions made with soft sets and fuzzy soft sets however the basis of evaluation of the decision method is single and simple the same decision problem can obtain different results from using a different evaluation basis in this paper in order to obtain the right result we discuss fuzzy soft set decision problems a new algorithm based on grey relational analysis is presented the evaluation bases of the new algorithm are multiple there is more information in a decision result based on multiple evaluation bases which is more easily accepted and logical to one s thinking for the two cases examined the results show that the new algorithm is efficient for solving decision problems

applications of soft sets in ideal theory of bck bci algebras the notion of soft ideals and idealistic soft bck bci algebras is introduced and several examples are given relations between soft bck bci algebras and idealistic soft bck bci algebras are provided the intersection union and operation and or operation of soft ideals and idealistic soft bck bci algebras are established

soft sets and soft groups molodtsov introduced the concept of soft set theory which can be used as a generic mathematical tool for dealing with uncertainty in this paper we introduce the basic properties of soft sets and compare soft sets to the related concepts of fuzzy sets and rough sets we then give a definition of soft groups and derive their basic properties using molodtsov s definition of the soft sets

fuzzy filters of mtl algebras characterizations of fuzzy filters in mtl algebras are given the fuzzy filter generated by a fuzzy set is considered the notion of boolean fuzzy filters and mv fuzzy filters are introduced and related properties are investigated a condition for a fuzzy filter to be boolean is provided a characterization of a boolean fuzzy filter is given a congruence relation on a mtl algebra induced by a fuzzy filter is established and we show that the set of all congruence relations induced by a fuzzy filter is a completely distributive lattice

fuzzy filters of bl algebras the aim of this paper is to introduce the notions of fuzzy filters fuzzy prime filters and the cosets of a fuzzy filter in bl algebras and investigate some of their properties the fuzzy filter generated by a fuzzy set is discussed some characterizations of fuzzy filters and fuzzy prime filters are derived the extension theorem of fuzzy prime filters and the fuzzy prime filters theorem are established finally we prove that the algebra l f which is the set of all cosets of f is a bl algebra and is isomorphic to the bl algebra l ff 1 where ff 1 x l f x f 1 moreover each bl algebra l is a subdirect product of linearly ordered bl algebras l fi i where fi i are fuzzy prime filters of l

fuzzy boolean and positive implicative filters of bl algebras the aim of this paper is to introduce the notions of fuzzy boolean filters and fuzzy positive implicative filters in bl algebras and to investigate their properties several characterizations of fuzzy boolean filters and fuzzy positive implicative filters are derived the extension theorems of fuzzy boolean filters and fuzzy positive implicative filters are obtained the relation between fuzzy boolean filters and fuzzy positive implicative filters is investigated and it is proved that every fuzzy boolean filter is a fuzzy positive implicative filter but the converse may not be true furthermore the conditions under which a fuzzy positive implicative filter is a fuzzy boolean filter are established

sieve semiparametric two step gmm under weak dependence this paper considers semiparametric two step gmm estimation and inference with weakly dependent data where unknown nuisance functions are estimated via sieve extremum estimation in the first step we show that although the asymptotic variance of the second step gmm estimator may not have a closed form expression it can be well approximated by sieve variances that have simple closed form expressions we present consistent or robust variance estimation wald tests and hansen s 1982 over identification tests for the second step gmm that properly reflect the first step estimated functions and the weak dependence of the data our sieve semiparametric two step gmm inference procedures are shown to be numerically equivalent to the ones computed as if the first step were parametric a new consistent random perturbation estimator of the derivative of the expectation of the non smooth moment function is also provided

boolean products of bl algebras the representation of algebras by boolean products is a very general problem in universal algebra in this paper we shall characterize the boolean products of bl chains the weak boolean products of local bl algebras and the weak boolean products of perfect bl algebras

on the logic foundation of fuzzy reasoning abstract fuzzy reasoning and fuzzy formal deduction theory are two closely related subjects and each of them has been investigated by many researchers unfortunately it seems that the two branches have not been successfully connected the aim of this paper is to establish a so called quasi propositional deductive system syntactically as well as semantically and then set a logic foundation for developing the theory of fuzzy reasoning therefrom

some types of generalized fuzzy filters of bl algebras the aim of this paper is to introduce the notions of interval valued q fuzzy boolean implicative positive implicative and fantastic filters in bl algebras and to investigate some related properties some characterizations of these generalized fuzzy filters are derived finally the relations among these generalized fuzzy filters are discussed it is proved that an interval valued q fuzzy filter in bl algebras is an interval valued q fuzzy boolean implicative filter if and only if it is both an interval valued q fuzzy positive implicative filter and an interval valued q fuzzy fantastic filter

diagnostic testing and evaluation of maximum likelihood models abstract the paper develops a unified theory of likelihood specification testing based on m estimators of auxiliary parameters the theory is sufficiently general to encompass a wide class of specification tests including moment based tests pearson type goodness of fit tests the information matrix test and the cox test the paper also presents a framework based on frechet differentiation for determining the effects of misspecification on the almost sure limits of parameter estimates and specification test statistics

bayesian estimation of state space models using moment conditions abstract we consider bayesian estimation of state space models when the measurement density is not available but estimating equations for the parameters of the measurement density are available from moment conditions the most common applications are partial equilibrium models involving moment conditions that depend on dynamic latent variables e g time varying parameters stochastic volatility and dynamic general equilibrium models when moment equations from the first order conditions are available but computing an accurate approximation to the measurement density is difficult

economic implications of nonlinear pricing kernels based on a family of discrepancy functions we derive nonparametric stochastic discount factor bounds that naturally generalize variance entropy and higher moment bounds these bounds are especially useful to identify how parameters affect pricing kernel dispersion in asset pricing models in particular they allow us to distinguish between models where dispersion comes mainly from skewness from models where kurtosis is the primary source of dispersion we analyze the admissibility of disaster disappointment aversion and long run risk models with respect to these bounds this paper was accepted by jerome detemple finance

vague soft hemirings abstract xu et al introduced the concept of vague soft sets which is an extension to the soft set and the vague set in this paper we apply the concept of vague soft sets to hemiring theory the notion of q vague soft left h ideals of a hemiring is introduced and some related properties are investigated

qualitative and asymptotic performance of snp density estimators abstract the snp estimator is the most convenient nonparametric method for simultaneously estimating the parameters of a nonlinear model and the density of a latent process by maximum likelihood to determine if this convenience comes at a price we assess the qualitative behavior of snp in finite samples using the marron wand test suite and verify theoretical convergence rates by monte carlo simulation our results suggest that there is no price for convenience because the snp estimator is both qualitatively and asymptotically similar to the kernel estimator which is optimal

semiparametric efficiency bound for models of sequential moment restrictions containing unknown functions abstract this paper computes the semiparametric efficiency bound for finite dimensional parameters identified by models of sequential moment restrictions containing unknown functions our results extend those of chamberlain 1992b and ai and chen 2003 for semiparametric conditional moment restrictions with identical information sets to the case of nested information sets and those of chamberlain 1992a and brown and newey 1998 for models of sequential moment restrictions without unknown functions to cases with unknown functions of possibly endogenous variables our results are applicable to semiparametric panel data models and two stage plug in problems as an important example we compute the efficiency bound for a weighted average derivative of a nonparametric instrumental variables regression npiv and find that simple plug in npiv estimators are not efficient we present an optimally weighted orthogonalized sieve minimum distance estimator that achieves the semiparametric efficiency bound

kernels and closures of soft set relations and soft set relation mappings in this paper the notions of anti reflexive kernel symmetric kernel reflexive closure and symmetric closure of a soft set relation are first introduced respectively then their accurate calculation formulae and some properties are obtained finally soft set relation mappings and inverse soft set relation mappings are proposed and some related properties are discussed

soft bck bci algebras molodtsov d molodtsov soft set theory first results comput math appl 37 1999 19 31 introduced the concept of soft set as a new mathematical tool for dealing with uncertainties that is free from the difficulties that have troubled the usual theoretical approaches in this paper we apply the notion of soft sets by molodtsov to the theory of bck bci algebras the notion of soft bck bci algebras and soft subalgebras are introduced and their basic properties are derived

soft bl algebras based on fuzzy sets by means of soft sets and q soft sets some characterizations of implicative positive implicative and fantastic filteristic soft bl algebras are investigated finally we prove that a soft set is an implicative filteristic soft bl algebra if and only if it is both a positive implicative filteristic soft bl algebra and a fantastic filteristic soft bl algebra

fundamentals of statistical signal processing estimation theory minimum variance unbiased estimation cramer rao lower bound linear models general minimum variance unbiased estimation best linear unbiased estimators maximum likelihood estimation least squares method of moments the bayesian philosophy general bayesian estimators linear bayesian estimators kalman filters summary of estimators extension for complex data and parameters

interference decoding for deterministic channels an inner bound to the capacity region of a class of deterministic interference channels with three user pairs is presented the key idea is to simultaneously decode the combined interference signal and the intended message at each receiver it is shown that this interference decoding inner bound is tight under certain strong interference conditions the inner bound is also shown to strictly contain the inner bound obtained by treating interference as noise which includes interference alignment for deterministic channels the gain comes from judicious analysis of the number of combined interference sequences in different regimes of input distributions and message rates finally the inner bound is generalized to the case where each channel output is observed through a noisy channel

application of level soft sets in decision making based on interval valued fuzzy soft sets molodtsov s soft set theory was originally proposed as a general mathematical tool for dealing with uncertainty research on fuzzy soft set based decision making has received much attention in recent years this paper aims to give deeper insights into decision making involving interval valued fuzzy soft sets a hybrid model combining soft sets with interval valued fuzzy sets the concept called reduct fuzzy soft sets of interval valued fuzzy soft sets is introduced using reduct fuzzy soft sets and level soft sets flexible schemes for decision making based on weighted interval valued fuzzy soft sets are proposed and some illustrative examples are employed to show that the proposals presented here are not only more reasonable but more efficient in practical applications

soft subsets and soft product operations molodtsov s soft set theory provides a general mathematical framework for dealing with uncertainty it is known that soft subsets and soft equal relations are of vital importance in soft set theory this paper aims to give a systematic study on several types of soft subsets and various soft equal relations induced by them we give some equivalent characterizations of different soft subsets and endeavor to ascertain the interrelations among these notions illustrated by a number of concrete examples we also consider ontology based soft sets and show that soft l subsets generalize both soft m subsets and ontology based soft subsets moreover by means of soft l subsets and some related notions we give a theoretical study concerning soft product operations such as products and products we consummate some incomplete results concerning soft product operations existing in the literature and investigate the algebraic properties of soft product operations in detail finally we consider free soft algebras associated with soft product operations it is shown that soft l equal relations are congruence relations over free soft algebras and the corresponding quotient structures form commutative semigroups

using conditional moments of asset payoffs to infer the volatility of intertemporal marginal rates of substitution previously hansen and jagannathan 1990a derived and computed mean standard deviation frontiers for intertemporal marginal rates of substitution imrs implied by asset market data these frontiers give the lower bounds on the standard deviations as a function of the mean in this paper we develop a strategy for utilizing conditioning information efficiently and hence improve on the standard deviation bounds computed by hansen and jagannathan we implement this strategy empirically by using the seminonparametric snp methodology suggested by gallant and tauchen 1989 to estimate the conditional distribution of a vector of monthly asset payoffs we use the fitted conditional distributions to calculate both conditional and unconditional standard deviation bounds for the imrs the unconditional bounds are as sharp as possible subject to robustness considerations we also use the fitted distributions to compute the moments of various candidate marginal rates of substitution suggested by economic theory and in particular the time nonseparable preferences of dunn and singleton 1986 and eichenbaum and hansen 1990 for these preferences our findings suggest that habit persistence will put the moments of the imrs inside the frontier at reasonable values of the curvature parameter at the same time we uncover evidence that the implied imrs fails to satisfy all of the restrictions inherent in the euler equation the findings help explain why euler equation estimation methods typically find evidence in favor of local durability instead of habit persistence for monthly data

spanning tests in return and stochastic discount factor mean variance frontiers a unifying approach abstract we propose new spanning tests that assess if the initial and additional assets share the economically meaningful cost and mean representing portfolios we prove their asymptotic equivalence to existing tests under local alternatives we also show that unlike two step or iterated procedures single step methods such as continuously updated gmm yield numerically identical overidentifying restrictions test so there is arguably a single spanning test to prove these results we extend optimal gmm inference to deal with singularities in the long run second moment matrix of the influence functions finally we test for spanning using size and book to market sorted us stock portfolios

semi nonparametric maximum likelihood estimation on developpe une approche basee sur une innovation due a phillips 1983 sur les approximations des fonctions de densite

monoidal t norm based logic towards a logic for left continuous t norms hajek s bl logic is the fuzzy logic capturing the tautologies of continuous t norms and their residua in this paper we investigate a weaker logic mtl which is intended to cope with the tautologies of left continuous t norms and their residua the corresponding algebraic structures mtl algebras are defined and completeness of mtl with respect to linearly ordered mtl algebras is proved besides several schematic extensions of mtl are also considered as well as their corresponding predicate calculi

performance of multi cell massive mimo systems with interference decoding we consider a multi cell massive mimo system where a time division duplex protocol is used to estimate the channel state information via uplink pilots when maximum ratio combining mrc is used at the bss the re use of pilots across cells causes the pilot contamination effect which yields interference components that do not vanish as the number of base station bs antennas m rightarrow infty when treating interference as noise tin this phenomenon limits the performance of multi cell massive mimo systems in this paper we analyze more advanced schemes based on simultaneous unique decoding sd as well as simultaneous non unique decoding snd of the interference that can provide unbounded rate as m rightarrow infty we also establish a worst case uncorrelated noise technique for multiple access channels to derive achievable rate expressions for finite m furthermore we study a much simpler subset of snd called s snd which provides a lower bound to snd and achieves unbounded rate as m rightarrow infty and also outperforms sd for finite m for the special cases of two cell and three cell systems using a maximum symmetric rate allocation policy we compare the performance of different interference decoding schemes with that of tin finally we numerically illustrate the improved performance of the proposed schemes

interference networks with point to point codes the paper establishes the capacity region of the gaussian interference channel with many transmitter receiver pairs constrained to use point to point codes the capacity region is shown to be strictly larger in general than the achievable rate regions when treating interference as noise using successive interference cancellation decoding and using joint decoding the gains in coverage and achievable rate using the optimal decoder are analyzed in terms of ensemble averages using stochastic geometry in a spatial network where the nodes are distributed according to a poisson point process and the channel path loss exponent is 2 it is shown that the density of users that can be supported by treating interference as noise can scale no faster than b2 as the bandwidth b grows while the density of users can scale linearly with b under optimal decoding