scholarly journals Additive monotone regression in high and lower dimensions

2019 ◽  
Vol 13 (0) ◽  
pp. 1-51
Author(s):  
Solveig Engebretsen ◽  
Ingrid K. Glad
Keyword(s):  
Monotone Regression

Penalized wavelet monotone regression

2007 ◽  
Vol 77 (16) ◽  
pp. 1608-1621 ◽  
Author(s):  
Anestis Antoniadis ◽  
Jéremie Bigot ◽  
Irène Gijbels
Keyword(s):  
Monotone Regression

Bayesian Nonparametric Monotone Regression of Dynamic Latent Traits in Item Response Theory Models

2019 ◽  
Vol 45 (3) ◽  
pp. 274-296
Author(s):  
Yang Liu ◽  
Xiaojing Wang
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Nonlinear Effects ◽  
Real Data ◽  
Monotone Regression ◽  
Response Theory ◽  
Growth Trend ◽  
Nonparametric Prior ◽  
Latent Traits ◽  
Item Response Theory Models

Parametric methods, such as autoregressive models or latent growth modeling, are usually inflexible to model the dependence and nonlinear effects among the changes of latent traits whenever the time gap is irregular and the recorded time points are individually varying. Often in practice, the growth trend of latent traits is subject to certain monotone and smooth conditions. To incorporate such conditions and to alleviate the strong parametric assumption on regressing latent trajectories, a flexible nonparametric prior has been introduced to model the dynamic changes of latent traits for item response theory models over the study period. Suitable Bayesian computation schemes are developed for such analysis of the longitudinal and dichotomous item responses. Simulation studies and a real data example from educational testing have been used to illustrate our proposed methods.

About monotone regression quantiles

2000 ◽  
Vol 48 (1) ◽  
pp. 101-104 ◽  
Author(s):  
Sandrine Poiraud-Casanova ◽  
Christine Thomas-Agnan
Keyword(s):  
Monotone Regression ◽  
Regression Quantiles

Residents' Learning Curve of Lumbar Transforaminal Epidural Steroid Injections

2017 ◽  
Vol 78 (05) ◽  
pp. 460-466 ◽  
Author(s):  
Sarah Haile ◽  
Gerhard Hildebrandt ◽  
Martin Stienen ◽  
Holger Joswig
Keyword(s):  
Learning Curve ◽  
Radiation Exposure ◽  
Learning Curves ◽  
Outcome Analysis ◽  
Monotone Regression ◽  
Procedure Time ◽  
Steroid Injections ◽  
Epidural Steroid Injections ◽  
Operative Notes ◽  
Epidural Steroid

Background and Study Aims There is a paucity of literature on beginners' training and on its connection with patient safety for transforaminal epidural steroid injections (TFESIs). This study retrospectively assessed the learning curves and associated complications of neurosurgery residents never previously exposed to TFESI and compared them with experienced board-certified faculty neurosurgeons (BCFNs). Material and Methods Procedure time in minutes, dose-area product (DAP) in cGy*cm2, periprocedural observations, and complications in 354 TFESIs for radicular pain secondary to lumbar disk herniation or lumbar spinal stenosis were extracted from operative notes and the electronic infiltration logbook in the per-injection format. Learning curves for 238 residents and 116 BCFN TFESIs in terms of procedure time and DAP were estimated using monotone regression. Results Residents' TFESI procedure time and DAP reached BCFN level (4.7 minutes and 140.2 Gy*cm2) after 67 and 68 cases, respectively. Residents' TFESIs were unsuccessful in 1.7%, mostly for severe obesity and hypertrophied facet joints, but no severe complications were noted. Obesity, however, did not result in increased procedure times or radiation exposure in general. Residents were faster and required less fluoroscopy in TFESI of the upper lumbar nerve roots than for L5 or S1 in particular. Conclusion The residents' learning curve for TFESIs in terms of procedure time and radiation exposure can be overcome safely after < 70 TFESIs. An outcome analysis correlating to the interventionalist's training level would be worth investigating in future studies.

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