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94
Updating and Estimating a Social Accounting Matrix Using Cross Entropy Methods
 Economic Systems Research
, 2001
"... TMD Discussion Papers contain preliminary material and research results, and are circulated prior to a full peer review in order to stimulate discussion and critical comment. It is expected that most Discussion Papers will eventually be published in some other form, and that their content may also b ..."
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Cited by 22 (3 self)
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TMD Discussion Papers contain preliminary material and research results, and are circulated prior to a full peer review in order to stimulate discussion and critical comment. It is expected that most Discussion Papers will eventually be published in some other form, and that their content may also be revised. This paper is available at
Estimating a social accounting matrix using cross entropy methods
 Discussion Paper 33, Trade and Macroeconomics Division, International Food Policy Research Institute
, 1998
"... TMD Discussion Papers contain preliminary material and research results, and are circulated prior to a full peer review in order to stimulate discussion and critical comment. It is expected that most Discussion Papers will eventually be published in some other form, and that their content may also b ..."
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Cited by 17 (5 self)
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TMD Discussion Papers contain preliminary material and research results, and are circulated prior to a full peer review in order to stimulate discussion and critical comment. It is expected that most Discussion Papers will eventually be published in some other form, and that their content may also be revised.
The Latent Maximum Entropy Principle
 In Proc. of ISIT
, 2002
"... We present an extension to Jaynes' maximum entropy principle that handles latent variables. The principle of latent maximum entropy we propose is di#erent from both Jaynes' maximum entropy principle and maximum likelihood estimation, but often yields better estimates in the presence of hidden vari ..."
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Cited by 17 (3 self)
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We present an extension to Jaynes' maximum entropy principle that handles latent variables. The principle of latent maximum entropy we propose is di#erent from both Jaynes' maximum entropy principle and maximum likelihood estimation, but often yields better estimates in the presence of hidden variables and limited training data. We first show that solving for a latent maximum entropy model poses a hard nonlinear constrained optimization problem in general. However, we then show that feasible solutions to this problem can be obtained e#ciently for the special case of loglinear modelswhich forms the basis for an e#cient approximation to the latent maximum entropy principle. We derive an algorithm that combines expectationmaximization with iterative scaling to produce feasible loglinear solutions. This algorithm can be interpreted as an alternating minimization algorithm in the information divergence, and reveals an intimate connection between the latent maximum entropy and maximum likelihood principles.
Pricing American options by simulation using a stochastic mesh with optimized weights
 in Probabilistic Constrained Optimization: Methodology and Applications
, 2000
"... This paper develops a simulation method for pricing pathdependent American options, and American options on a large number of underlying assets, such as basket options. Standard numerical procedures (lattice methods and nite difference methods) are generally inapplicable to such highdimensional pr ..."
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Cited by 15 (4 self)
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This paper develops a simulation method for pricing pathdependent American options, and American options on a large number of underlying assets, such as basket options. Standard numerical procedures (lattice methods and nite difference methods) are generally inapplicable to such highdimensional problems, and this has motivated research into simulationbased methods. The optimal stopping problem embedded in the pricing of American options makes this a nonstandard problem for simulation. This paper extends the stochastic mesh introduced in Broadie and Glasserman [5]. In its original form, the stochastic mesh method required knowledge of the transition density of the underlying process of asset prices and other state variables. This paper extends the method to settings in which the transition density is either unknown or fails to exist. We avoid the need for a transition density by choosing mesh weights through a constrained optimization problem. If the weights are constrained to correctly price su ciently many simple instruments, they can be expected to work well in pricing a more complex American option. We investigate two criteria for use in the optimization  maximum entropy and least squares. The methods are illustrated through numerical examples. 32 1
Generating plausible crop distribution and performance maps for SubSaharan Africa using a spatially disaggregated data fusion and optimization approach. Liangzhi
 FOOD POLICY RESEARCH INSTITUTE www.ifpri.org IFPRI HEADQUARTERS 2033 K Street, NW Washington, DC 200061002 USA Tel.: +12028625600 Fax: +12024674439 Email: ifpri@cgiar.org IFPRI ADDIS ABABA P. O. Box 5689 Addis Ababa, Ethiopia Tel.: +251 11 6463215
, 2007
"... of 15 agricultural research centers that receive principal funding from governments, private foundations, and international and regional organizations, most of which are members of the ..."
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Cited by 14 (2 self)
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of 15 agricultural research centers that receive principal funding from governments, private foundations, and international and regional organizations, most of which are members of the
The Maximum Entropy Approach and Probabilistic IR Models
 ACM TRANSACTIONS ON INFORMATION SYSTEMS
, 1998
"... The Principle of Maximum Entropy is discussed and two classic probabilistic models of information retrieval, the Binary Independence Model of Robertson and Sparck Jones and the Combination Match Model of Croft and Harper are derived using the maximum entropy approach. The assumptions on which the cl ..."
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Cited by 12 (0 self)
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The Principle of Maximum Entropy is discussed and two classic probabilistic models of information retrieval, the Binary Independence Model of Robertson and Sparck Jones and the Combination Match Model of Croft and Harper are derived using the maximum entropy approach. The assumptions on which the classical models are based are not made. In their place, the probability distribution of maximum entropy consistent with a set of constraints is determined. It is argued that this subjectivist approach is more philosophically coherent than the frequentist conceptualization of probability that is often assumed as the basis of probabilistic modeling and that this philosophical stance has important practical consequences with respect to the realization of information retrieval research.
Maximum entropy for collaborative filtering
 In Proceedings of 20th International Conference on Uncertainty in Artificial Intelligence (UAI’04
, 2004
"... Within the task of collaborative filtering two challenges for computing conditional probabilities exist. First, the amount of training data available is typically sparse with respect to the size of the domain. Thus, support for higherorder interactions is generally not present. Second, the variable ..."
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Cited by 8 (0 self)
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Within the task of collaborative filtering two challenges for computing conditional probabilities exist. First, the amount of training data available is typically sparse with respect to the size of the domain. Thus, support for higherorder interactions is generally not present. Second, the variables that we are conditioning upon vary for each query. That is, users label different variables during each query. For this reason, there is no consistent input to output mapping. To address these problems we purpose a maximum entropy approach using a nonstandard measure of entropy. This approach can be simplified to solving a set of linear equations that can be efficiently solved. 1
Structural Adjustment and Intersectoral Shifts in Tanzania  A Computable General Equilibrium Analysis
, 2001
"... ..."
2000) Estimating Coke and Pepsi’s Price and Advertising Strategies
 Journal of Business & Economic Statistics
"... We benefitted greatly from George Judge’s comments about econometrics and Leo Simon’s comments on game theory. We are very grateful to Jean Jacques Laffont, Quang Vuong, and especially Farid Gasmi for generously providing us with the data used in this study and for advice. We received useful suggest ..."
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Cited by 7 (2 self)
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We benefitted greatly from George Judge’s comments about econometrics and Leo Simon’s comments on game theory. We are very grateful to Jean Jacques Laffont, Quang Vuong, and especially Farid Gasmi for generously providing us with the data used in this study and for advice. We received useful suggestions from participants at the "Industrial Organization and FoodProcessing Industry" conference at the Institute D’Economie Industrielle at the Université des Sciences Sociales de Toulouse and anonymous referees and the associate editor. We thank Gary Casterline and Dana Keil for substantial help with computer programs.
Bayesian Exponentially Tilted Empirical Likeliood
 Biometrika
, 2005
"... Newey and Smith (2001) have recently shown that Empirical Likelihood (EL) exhibits desirable higherorder asymptotic properties, namely, that its O ¡ n −1 ¢ bias is particularly small and that biascorrected EL is higherorder efficient. Although EL possesses these properties when the model is correc ..."
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Cited by 7 (0 self)
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Newey and Smith (2001) have recently shown that Empirical Likelihood (EL) exhibits desirable higherorder asymptotic properties, namely, that its O ¡ n −1 ¢ bias is particularly small and that biascorrected EL is higherorder efficient. Although EL possesses these properties when the model is correctly specified, this paper shows that the asymptotic variance of EL in the presence of model misspecification may become undefined when the functions defining the moment conditions are unbounded. In contrast, the Exponential Tilting (ET) estimator avoids this problem under mild regularity conditions. Since ET does not share the higherorder asymptotic properties of EL, there is a need for an estimator that combines the qualities of both estimators. This paper introduces a new estimator called Exponentially Tilted Empirical Likelihood (ETEL) that is shown to have the same O ¡ n −1 ¢ bias and the same O ¡ n −2¢ variance as EL, while maintaining a welldefined asymptotic variance under model misspecification.