195,719 research outputs found
Multi-Period Asset Allocation: An Application of Discrete Stochastic Programming
The issue of modeling farm financial decisions in a dynamic framework is addressed in this paper. Discrete stochastic programming is used to model the farm portfolio over the planning period. One of the main issues of discrete stochastic programming is representing the uncertainty of the data. The development of financial scenario generation routines provides a method to model the stochastic nature of the model. In this paper, two approaches are presented for generating scenarios for a farm portfolio problem. The approaches are based on copulas and optimization. The copula method provides an alternative to the multivariate normal assumption. The optimization method generates a number of discrete outcomes which satisfy specified statistical properties by solving a non-linear optimization model. The application of these different scenario generation methods is then applied to the topic of geographical diversification. The scenarios model the stochastic nature of crop returns and land prices in three separate geographic regions. The results indicate that the optimal diversification strategy is sensitive to both scenario generation method and initial acreage assumptions. The optimal diversification results are presented using both scenario generation methods.Agribusiness, Agricultural Finance, Farm Management,
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
Tracing the Scenarios in Scenario-Based Product Design: a study to support scenario generation
Scenario-based design originates from the human-computer interaction and\ud
software engineering disciplines, and continues to be adapted for product development. Product development differs from software development in the former’s more varied context of use, broader characteristics of users and more tangible solutions. The possible use of scenarios in product design is therefore broader and more challenging. Existing design methods that involve scenarios can be employed in many different stages of the product design process. However, there is no proficient overview that discusses a\ud
scenario-based product design process in its full extent. The purposes of creating scenarios and the evolution of scenarios from their original design data are often not obvious, although the results from using scenarios are clearly visible. Therefore, this paper proposes to classify possible scenario uses with their purpose, characteristics and supporting design methods. The classification makes explicit different types of scenarios and their relation to one another. Furthermore, novel scenario uses can be referred or added to the classification to develop it in parallel with the scenario-based design\ud
practice. Eventually, a scenario-based product design process could take inspiration for creating scenarios from the classification because it provides detailed characteristics of the scenario
One-sample aggregate data meta-analysis of medians
An aggregate data meta-analysis is a statistical method that pools the
summary statistics of several selected studies to estimate the outcome of
interest. When considering a continuous outcome, typically each study must
report the same measure of the outcome variable and its spread (e.g., the
sample mean and its standard error). However, some studies may instead report
the median along with various measures of spread. Recently, the task of
incorporating medians in meta-analysis has been achieved by estimating the
sample mean and its standard error from each study that reports a median in
order to meta-analyze the means. In this paper, we propose two alternative
approaches to meta-analyze data that instead rely on medians. We systematically
compare these approaches via simulation study to each other and to methods that
transform the study-specific medians and spread into sample means and their
standard errors. We demonstrate that the proposed median-based approaches
perform better than the transformation-based approaches, especially when
applied to skewed data and data with high inter-study variance. In addition,
when meta-analyzing data that consists of medians, we show that the
median-based approaches perform considerably better than or comparably to the
best-case scenario for a transformation approach: conducting a meta-analysis
using the actual sample mean and standard error of the mean of each study.
Finally, we illustrate these approaches in a meta-analysis of patient delay in
tuberculosis diagnosis
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