Regularization for Continuously Observed Ordinal Response Variables with Piecewise‐constant Functional Covariates. (16th June 2016)
- Record Type:
- Journal Article
- Title:
- Regularization for Continuously Observed Ordinal Response Variables with Piecewise‐constant Functional Covariates. (16th June 2016)
- Main Title:
- Regularization for Continuously Observed Ordinal Response Variables with Piecewise‐constant Functional Covariates
- Authors:
- Avery, Matthew
Orndorff, Mark
Robinson, Timothy
Freeman, Laura - Other Names:
- Vicario Grazia guestEditor.
Tort‐Martorell Xavier guestEditor. - Abstract:
- Abstract : This paper investigates regularization for continuously observed covariates that resemble step functions. The motivating examples come from operational test data from a recent US Department of Defense test of the Shadow Tactical Unmanned Aircraft system. The response variable, quality of video provided by the Shadow to friendly ground units, was measured on an ordinal scale continuously over time. Functional covariates, altitude and distance, can be well approximated by step functions. Two approaches for regularizing these covariates are considered, including a thinning approach commonly used within the Department of Defense to address autocorrelated time series data, and a novel 'smoothing' approach, which first approximates the covariates as step functions and then treats each 'step' as a uniquely observed data point. Datasets resulting from both approaches are fit using a mixed model cumulative logistic regression, and we compare their results. While the thinning approach identifies altitude as having a significant impact on video quality, the smoothing approach finds no evidence of an effect. This difference is attributable to the larger effective sample size produced by thinning. System characteristics make it unlikely that video quality would degrade at higher altitudes, suggesting that the thinning approach has produced a Type 1 error. By accounting for the functional characteristics of the covariates, the novel smoothing approach has produced a moreAbstract : This paper investigates regularization for continuously observed covariates that resemble step functions. The motivating examples come from operational test data from a recent US Department of Defense test of the Shadow Tactical Unmanned Aircraft system. The response variable, quality of video provided by the Shadow to friendly ground units, was measured on an ordinal scale continuously over time. Functional covariates, altitude and distance, can be well approximated by step functions. Two approaches for regularizing these covariates are considered, including a thinning approach commonly used within the Department of Defense to address autocorrelated time series data, and a novel 'smoothing' approach, which first approximates the covariates as step functions and then treats each 'step' as a uniquely observed data point. Datasets resulting from both approaches are fit using a mixed model cumulative logistic regression, and we compare their results. While the thinning approach identifies altitude as having a significant impact on video quality, the smoothing approach finds no evidence of an effect. This difference is attributable to the larger effective sample size produced by thinning. System characteristics make it unlikely that video quality would degrade at higher altitudes, suggesting that the thinning approach has produced a Type 1 error. By accounting for the functional characteristics of the covariates, the novel smoothing approach has produced a more accurate characterization of the Shadow's ability to provide full motion video to supported units. Copyright © 2016 John Wiley & Sons, Ltd. … (more)
- Is Part Of:
- Quality and reliability engineering international. Volume 32:Number 6(2016:Oct.)
- Journal:
- Quality and reliability engineering international
- Issue:
- Volume 32:Number 6(2016:Oct.)
- Issue Display:
- Volume 32, Issue 6 (2016)
- Year:
- 2016
- Volume:
- 32
- Issue:
- 6
- Issue Sort Value:
- 2016-0032-0006-0000
- Page Start:
- 2033
- Page End:
- 2042
- Publication Date:
- 2016-06-16
- Subjects:
- ordinal response -- functional data -- regularization -- knot selection -- defense
Reliability (Engineering) -- Periodicals
Quality control -- Periodicals
High technology -- Periodicals
620.00452 - Journal URLs:
- http://www3.interscience.wiley.com/cgi-bin/jhome/3680 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/qre.2037 ↗
- Languages:
- English
- ISSNs:
- 0748-8017
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 7168.137300
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 463.xml