34,893 research outputs found
Problem-driven scenario generation: an analytical approach for stochastic programs with tail risk measure
Scenario generation is the construction of a discrete random vector to
represent parameters of uncertain values in a stochastic program. Most
approaches to scenario generation are distribution-driven, that is, they
attempt to construct a random vector which captures well in a probabilistic
sense the uncertainty. On the other hand, a problem-driven approach may be able
to exploit the structure of a problem to provide a more concise representation
of the uncertainty.
In this paper we propose an analytic approach to problem-driven scenario
generation. This approach applies to stochastic programs where a tail risk
measure, such as conditional value-at-risk, is applied to a loss function.
Since tail risk measures only depend on the upper tail of a distribution,
standard methods of scenario generation, which typically spread their scenarios
evenly across the support of the random vector, struggle to adequately
represent tail risk. Our scenario generation approach works by targeting the
construction of scenarios in areas of the distribution corresponding to the
tails of the loss distributions. We provide conditions under which our approach
is consistent with sampling, and as proof-of-concept demonstrate how our
approach could be applied to two classes of problem, namely network design and
portfolio selection. Numerical tests on the portfolio selection problem
demonstrate that our approach yields better and more stable solutions compared
to standard Monte Carlo sampling
Aggregation without the aggravation? Nonparametric analysis of the representative consumer.
In the tradition of Afriat (1967), Diewert (1973) and Varian (1982), we provide a revealed preference characterisation of the representative consumer. Our results are simple and complement those of Gorman (1953, 1961), Samuelson (1956) and others. They can also be applied to data very readily and without the need for auxilliary parametric or statistical assumptions. We investigate the application of our characterisation by means of a balanced microdata panel survey. Our findings provide robust evidence against the existence of a representative consumer for our data.
Aggregation without the Aggravation? Nonparametric Analysis of the Representative Consumer
Abstract: In the tradition of Afriat (1967), Diewert (1973) and Varian (1982), we provide a revealed preference characterisation of the representative consumer. Our results are simple and complement those of Gorman (1953, 1961), Samuelson (1956) and others. They can also be applied to data very readily and without the need for auxilliary parametric or statistical assumptions. We investigate the application of our characterisation by means of a balanced microdata panel survey. Our findings provide robust evidence against the existence of a representative consumer for our data.revealed preference;aggregation;Gorman Polar Form;GARP
Aggregation without the Aggravation? Nonparametric Analysis of the Representative Consumer
In the tradition of Afriat (1967), Diewert (1973) and Varian (1982), we provide a revealed preference characterisation of the representative consumer. Our results are simple and complement those of Gorman (1953, 1961), Samuelson (1956) and others. They can also be applied to data very readily and without the need for auxilliary parametric or statistical assumptions. We investigate the application of our characterisation by means of a balanced microdata panel survey. Our findings provide robust evidence against the existence of a representative consumer for our data.revealed preference, aggregation, Gorman polar form, GARP
Location models in the public sector
The past four decades have witnessed an explosive growth in the field of networkbased facility location modeling. This is not at all surprising since location policy is one of the most profitable areas of applied systems analysis in regional science and ample theoretical and applied challenges are offered. Location-allocation models seek the location of facilities and/or services (e.g., schools, hospitals, and warehouses) so as to optimize one or several objectives generally related to the efficiency of the system or to the allocation of resources. This paper concerns the location of facilities or services in discrete space or networks, that are related to the public sector, such as emergency services (ambulances, fire stations, and police units), school systems and postal facilities. The paper is structured as follows: first, we will focus on public facility location models that use some type of coverage criterion, with special emphasis in emergency services. The second section will examine models based on the P-Median problem and some of the issues faced by planners when implementing this formulation in real world locational decisions. Finally, the last section will examine new trends in public sector facility location modeling.Location analysis, public facilities, covering models
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