SSRN Author: Warren J. HahnWarren J. Hahn SSRN Content
http://www.ssrn.com/author=2464650
http://www.ssrn.com/rss/en-usSat, 23 Jan 2016 03:29:23 GMTeditor@ssrn.com (Editor)Sat, 23 Jan 2016 03:29:23 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: Sensitivity Analysis of Model Input Dependencies Using CopulasMany important decision and risk analysis problems are complicated by dependencies between input variables. In such cases, standard one-variable-at-a-time sensitivity analysis methods are typically eschewed in favor of fully probabilistic, or n-way, analysis techniques which simultaneously model all n input variables and their interdependencies. Unfortunately, much of the intuition provided by one-way sensitivity analysis and its associated graphical output displays such as Tornado diagrams may not be available in fully probabilistic methods. It is also difficult or impossible to isolate the marginal effects of variables in an n-way analysis. In this paper, we present a dependence-adjusted approach for identifying and analyzing the impact of the input variables in a model through the use of probabilistic sensitivity analysis based on copulas. This approach provides insights about the influence of both the input variables and the dependence relationships between the input variables. A ...
http://www.ssrn.com/abstract=2676316
http://www.ssrn.com/1462636.htmlFri, 22 Jan 2016 06:13:06 GMTREVISION: Sensitivity Analysis of Model Input Dependencies Using CopulasMany important decision and risk analysis problems are complicated by dependencies between input variables. In such cases, standard one-variable-at-a-time sensitivity analysis methods are typically eschewed in favor of fully probabilistic, or n-way, analysis techniques which simultaneously model all n input variables and their interdependencies. Unfortunately, much of the intuition provided by one-way sensitivity analysis and its associated graphical output displays such as Tornado diagrams may not be available in fully probabilistic methods. It is also difficult or impossible to isolate the marginal effects of variables in an n-way analysis. In this paper, we present a dependence-adjusted approach for identifying and analyzing the impact of the input variables in a model through the use of probabilistic sensitivity analysis based on copulas. This approach provides insights about the influence of both the input variables and the dependence relationships between the input variables. A ...
http://www.ssrn.com/abstract=2676316
http://www.ssrn.com/1462209.htmlWed, 20 Jan 2016 15:41:27 GMTUpdate: Sensitivity Analysis of Model Input Dependencies Using CopulasMany important decision and risk analysis problems are complicated by dependencies between input variables. In such cases, standard one-variable-at-a-time sensitivity analysis methods are typically eschewed in favor of fully probabilistic, or n-way, analysis techniques which simultaneously model all n input variables and their interdependencies. Unfortunately, much of the intuition provided by one-way sensitivity analysis and its associated graphical output displays such as Tornado diagrams may not be available in fully probabilistic methods. It is also difficult or impossible to isolate the marginal effects of variables in an n-way analysis. In this paper, we present a dependence-adjusted approach for identifying and analyzing the impact of the input variables in a model through the use of probabilistic sensitivity analysis based on copulas. This approach provides insights about the influence of both the input variables and the dependence relationships between the input variables. ...<br/><i>The Paper was removed</i>
http://www.ssrn.com/abstract=2676316
http://www.ssrn.com/1461697.htmlTue, 19 Jan 2016 11:52:11 GMTREVISION: Sensitivity Analysis of Model Input Dependencies Using CopulasMany important decision and risk analysis problems are complicated by dependencies between input variables. In such cases, standard one-variable-at-a-time sensitivity analysis methods are typically eschewed in favor of fully probabilistic, or n-way, analysis techniques which simultaneously model all n input variables and their interdependencies. Unfortunately, much of the intuition provided by one-way sensitivity analysis and its associated graphical output displays such as Tornado diagrams may not be available in fully probabilistic methods. It is also difficult or impossible to isolate the marginal effects of variables in an n-way analysis. In this paper, we present a dependence-adjusted approach for identifying and analyzing the impact of the input variables in a model through the use of probabilistic sensitivity analysis based on copulas. This approach provides insights about the influence of both the input variables and the dependence relationships between the input variables. ...
http://www.ssrn.com/abstract=2676316
http://www.ssrn.com/1438250.htmlWed, 21 Oct 2015 10:52:32 GMT