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Multivariate Shrinkage for Optimal Portfolio Weights
"... The improvement of portfolio selection by means of multivariate shrinkage estimator for the optimal portfolio weights is a subject of this paper. The estimated classical Markowitz weights are shrunk to the vector of current portfolio weights, which is chosen as a shrinkage target. Assuming log asset ..."
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The improvement of portfolio selection by means of multivariate shrinkage estimator for the optimal portfolio weights is a subject of this paper. The estimated classical Markowitz weights are shrunk to the vector of current portfolio weights, which is chosen as a shrinkage target. Assuming log
Decomposition of Optimal Portfolio Weight in a JumpDiffusion Model and Its Applications
"... This article solves the portfolio choice problem in a multiasset jumpdiffusion model. We decompose the optimal portfolio weight into components that correspond to a collection of fictitious economies, one of which is a standard diffusion economy, and the others of which are purejump economies. We ..."
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This article solves the portfolio choice problem in a multiasset jumpdiffusion model. We decompose the optimal portfolio weight into components that correspond to a collection of fictitious economies, one of which is a standard diffusion economy, and the others of which are purejump economies
Loss aversion, large deviation preferences and optimal portfolio weights for some classes
, 2006
"... of return processes ..."
Optimal Brain Damage
, 1990
"... We have used informationtheoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural network. By removing unimportant weights from a network, several improvements can be expected: better generalization, fewer training examples required, and improved sp ..."
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Cited by 510 (5 self)
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We have used informationtheoretic ideas to derive a class of practical and nearly optimal schemes for adapting the size of a neural network. By removing unimportant weights from a network, several improvements can be expected: better generalization, fewer training examples required, and improved
Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
 Evolutionary Computation
, 1994
"... In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about t ..."
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Cited by 539 (5 self)
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In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about
Fibonacci Heaps and Their Uses in Improved Network optimization algorithms
, 1987
"... In this paper we develop a new data structure for implementing heaps (priority queues). Our structure, Fibonacci heaps (abbreviated Fheaps), extends the binomial queues proposed by Vuillemin and studied further by Brown. Fheaps support arbitrary deletion from an nitem heap in qlogn) amortized tim ..."
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Cited by 739 (18 self)
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time and all other standard heap operations in o ( 1) amortized time. Using Fheaps we are able to obtain improved running times for several network optimization algorithms. In particular, we obtain the following worstcase bounds, where n is the number of vertices and m the number of edges
Insiders and Outsiders: The Choice between Informed and Arm'sLength Debt
, 1991
"... While the benefits of bank financing are relatively well understood, the costs are not. This paper argues that while informed banks make flexible financial decisions which prevent a firm's projects from going awry, the cost of this credit is that banks have bargaining power over the firm's ..."
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Cited by 868 (16 self)
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's profits, once projects have begun. The firm's portfolio choice of borrowing source and the choice of priority for its debt claims attempt to optimally circumscribe the powers of banks.
Financial Intermediation and Delegated Monitoring
 Review of Economic Studies
, 1984
"... This paper develops a theory of financial intermediation based on minimizing the cost of monitoring information which is useful for resolving incentive problems between borrowers and lenders. It presents a characterization of the costs of providing incentives for delegated monitoring by a financial ..."
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Cited by 1433 (18 self)
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are shown to be optimal. The analysis has implications for the portfolio structure and capital structure of intermediaries.
Active Learning with Statistical Models
, 1995
"... For manytypes of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992# Cohn, 1994]. We then showhow the same principles may be used to select data for two alternative, statist ..."
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Cited by 679 (10 self)
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For manytypes of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992# Cohn, 1994]. We then showhow the same principles may be used to select data for two alternative
Markov Logic Networks
 MACHINE LEARNING
, 2006
"... We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the ..."
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Cited by 816 (39 self)
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We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects
Results 1  10
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21,011