Difference between revisions of "Koushanfar2009"
From ACES
(Import from BibTeX) |
m (Default pdf) |
||
| (One intermediate revision by the same user not shown) | |||
| Line 3: | Line 3: | ||
|abstract=We introduce a new methodology for determining the difficulty of selecting the modeling objective function and estimating the parameters for an ad-hoc network data set. The method utilizes formulation of the underlying optimization problem instance that consists of an objective function and a set of constraints. The method is illustrated on real distance measurement data used for estimating the locations of wireless nodes that is the most studied and a representative problem for ad-hoc networks estimation. The properties of the data set that could affect the quality of optimization are categorized. In large optimization problems with multiple properties (characteristics) that contribute to the solution quality, it is practically impossible to analytically study the effect of each property. A number of metrics for evaluating the effectiveness of the optimization on each data set are proposed. Using the well known Plackett and Burmann fast simulation methodology, for each metric, the impact of the categorized properties of the data are determined for the specified optimization. A new approach for combining the impacts resulting from different properties on various metrics is described. We emphasize that the method is generic and has the potential to be more broadly applicable to other parameter estimation problems. | |abstract=We introduce a new methodology for determining the difficulty of selecting the modeling objective function and estimating the parameters for an ad-hoc network data set. The method utilizes formulation of the underlying optimization problem instance that consists of an objective function and a set of constraints. The method is illustrated on real distance measurement data used for estimating the locations of wireless nodes that is the most studied and a representative problem for ad-hoc networks estimation. The properties of the data set that could affect the quality of optimization are categorized. In large optimization problems with multiple properties (characteristics) that contribute to the solution quality, it is practically impossible to analytically study the effect of each property. A number of metrics for evaluating the effectiveness of the optimization on each data set are proposed. Using the well known Plackett and Burmann fast simulation methodology, for each metric, the impact of the categorized properties of the data are determined for the specified optimization. A new approach for combining the impacts resulting from different properties on various metrics is described. We emphasize that the method is generic and has the potential to be more broadly applicable to other parameter estimation problems. | ||
|pages=332 - 345 | |pages=332 - 345 | ||
|year=2009 | |||
|volume=57 | |volume=57 | ||
|journal=Institute of Mathematical Statistics (IMS) Lecture Notes-Monograph Series (LNMS) | |journal=Institute of Mathematical Statistics (IMS) Lecture Notes-Monograph Series (LNMS) | ||
|title=The challenges of model objective selection and estimation for ad-hoc network data sets | |title=The challenges of model objective selection and estimation for ad-hoc network data sets | ||
|entry=article | |entry=article | ||
| | |pdf=Koushanfar2009.pdf | ||
}} | }} | ||
Latest revision as of 18:36, 9 November 2021
| Koushanfar2009 | |
|---|---|
| entry | article |
| address | |
| annote | |
| author | F. Koushanfar and D. Shamsi |
| booktitle | |
| chapter | |
| edition | |
| editor | |
| howpublished | |
| institution | |
| journal | Institute of Mathematical Statistics (IMS) Lecture Notes-Monograph Series (LNMS) |
| month | |
| note | |
| number | |
| organization | |
| pages | 332 - 345 |
| publisher | |
| school | |
| series | |
| title | The challenges of model objective selection and estimation for ad-hoc network data sets |
| type | |
| volume | 57 |
| year | 2009 |
| doi | |
| issn | |
| isbn | |
| url | |
| Koushanfar2009.pdf | |
Email:
farinaz@ucsd.edu
farinaz@ucsd.edu
Address:
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Lab Location: EBU1-2514
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093