scholarly journals Optimal Weighting Method for Reducing Digital Satellite TV Differential Timing Error

2020 ◽  
Vol 29 (2) ◽  
pp. 322-326
Author(s):  
Shanhe Wang ◽  
Yu Hua ◽  
Yu Xiang ◽  
Changjiang Huang ◽  
Yuanyuan Gao ◽  
...  
Keyword(s):  
Timing Error ◽  
Weighting Method ◽  
Optimal Weighting ◽  
Differential Timing

Improving the Predictive Power of Conjoint Analysis: The use of Factor Analysis and Cluster Analysis

1985 ◽  
Vol 22 (2) ◽  
pp. 168-184 ◽  
Author(s):  
Michael R. Hagerty
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Conjoint Analysis ◽  
Predictive Accuracy ◽  
Real Data ◽  
Weighting Method ◽  
Individual Level ◽  
Optimal Weighting ◽  
Type Factor ◽  
And Cluster Analysis

A method is derived to improve the predictive accuracy of conjoint analysis by grouping respondents with similar preferences. The often-used model of cluster analysis is shown to be inadequate because real respondents do not form densely packed clusters in preference space. The author then derives the best method of weighting respondents in the sense of maximizing predictive accuracy in conjoint analysis. This method turns out to be a form of Q-type factor analysis. This “optimal weighting” method is shown to perform better than cluster analysis and individual-level analysis in Monté Carlo examples and in real data. Optimal weighting is contrasted with other methods for improving conjoint analysis, and recommendations on their use are made.

scholarly journals Optimal weighting method for interval-valued intuitionistic fuzzy opinions

2018 ◽  
Vol 24 (3) ◽  
pp. 106-110 ◽  
Author(s):  
M. El Alaoui ◽  
◽  
H. Ben-Azza ◽  
K. El Yassini ◽  
◽  
...  
Keyword(s):  
Weighting Method ◽  
Intuitionistic Fuzzy ◽  
Optimal Weighting ◽  
Interval Valued

scholarly journals Linear Optimal Runoff Aggregate (LORA): A global gridded synthesis runoff product

2018 ◽  
Author(s):  
Sanaa Hobeichi ◽  
Gab Abramowitz ◽  
Jason Evans ◽  
Hylke E. Beck
Keyword(s):  
Hydrological Models ◽  
Multiple Sources ◽  
Weighting Method ◽  
Error Covariance ◽  
Out Of Sample ◽  
Streamflow Data ◽  
Optimal Weighting ◽  
Gauged Basins ◽  
Climatic Characteristics ◽  
Better Than

Abstract. No synthesized global gridded runoff product, derived from multiple sources, is available despite such a product being useful to meet the needs of many global water initiatives. We apply an optimal weighting approach to merge runoff estimates from hydrological models constrained with observational streamflow records. The weighting method is based on the ability of the models to match observed streamflow data while accounting for error covariance between the participating products. To address the lack of observed streamflow for many regions, a dissimilarity method was applied to transfer the weights of the participating products to the ungauged basins from the closest gauged basins using dissimilarity between basins in physiographic and climatic characteristics as a proxy for distance. We perform out-of-sample tests to examine the success of the dissimilarity approach and we confirm that the weighted product performs better than its 11 constituents products in a range of metrics. Our resulting synthesized global gridded runoff product is available at monthly time scales, and includes time variant uncertainty, for the period 1980–2012 on a 0.5° grid. The synthesized global gridded runoff product broadly agrees with published runoff estimates at many river basins, and represents well the seasonal runoff cycle for most of the globe. The new product, called Linear Optimal Runoff Aggregate (LORA), is a valuable synthesis of existing runoff products and will be freely available for download on https://geonetwork.nci.org.au/.

scholarly journals Linear Optimal Runoff Aggregate (LORA): a global gridded synthesis runoff product

2019 ◽  
Vol 23 (2) ◽  
pp. 851-870 ◽  
Author(s):  
Sanaa Hobeichi ◽  
Gab Abramowitz ◽  
Jason Evans ◽  
Hylke E. Beck
Keyword(s):  
Hydrological Models ◽  
Multiple Sources ◽  
Weighting Method ◽  
Error Covariance ◽  
Out Of Sample ◽  
Streamflow Data ◽  
Optimal Weighting ◽  
Gauged Basins ◽  
Climatic Characteristics ◽  
Better Than

Abstract. No synthesized global gridded runoff product, derived from multiple sources, is available, despite such a product being useful for meeting the needs of many global water initiatives. We apply an optimal weighting approach to merge runoff estimates from hydrological models constrained with observational streamflow records. The weighting method is based on the ability of the models to match observed streamflow data while accounting for error covariance between the participating products. To address the lack of observed streamflow for many regions, a dissimilarity method was applied to transfer the weights of the participating products to the ungauged basins from the closest gauged basins using dissimilarity between basins in physiographic and climatic characteristics as a proxy for distance. We perform out-of-sample tests to examine the success of the dissimilarity approach, and we confirm that the weighted product performs better than its 11 constituent products in a range of metrics. Our resulting synthesized global gridded runoff product is available at monthly timescales, and includes time-variant uncertainty, for the period 1980–2012 on a 0.5∘ grid. The synthesized global gridded runoff product broadly agrees with published runoff estimates at many river basins, and represents the seasonal runoff cycle for most of the globe well. The new product, called Linear Optimal Runoff Aggregate (LORA), is a valuable synthesis of existing runoff products and will be freely available for download on https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f9617_9854_8096_5291 (last access: 31 January 2019).

Sign in / Sign up

Export Citation Format

Share Document