scholarly journals Testing-Based Forward Model Selection

2018 ◽  
Author(s):  
Damian Kozbur
Keyword(s):  
Model Selection ◽  
Forward Model

Compact Face Representation via Forward Model Selection

2016 ◽  
pp. 112-120
Author(s):  
Weiyuan Shao ◽  
Hong Wang ◽  
Yingbin Zheng ◽  
Hao Ye
Keyword(s):  
Model Selection ◽  
Forward Model ◽  
Face Representation

scholarly journals Bayesian uncertainty quantification in aerosol optical depth retrieval applied to TROPOMI measurements

2021 ◽  
Author(s):  
Anu Kauppi ◽  
Antti Kukkurainen ◽  
Antti Lipponen ◽  
Marko Laine ◽  
Antti Arola ◽  
...  
Keyword(s):  
Model Selection ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Bayesian Model Averaging ◽  
Forward Model ◽  
Selection Problem ◽  
Aerosol Model ◽  
Model Discrepancy ◽  
Aerosol Types ◽  
Model Selection Problem

Abstract. We present here an aerosol model selection based statistical method in Bayesian framework for retrieving atmospheric aerosol optical depth (AOD) and pixel-level uncertainty. Especially, we focus on to provide more realistic uncertainty estimate by taking into account a model selection problem when searching for the solution by fitting look-up table (LUT) models to a satellite measured top-of-atmosphere reflectance. By means of Bayesian model averaging over the best-fitting aerosol models we take into account an aerosol model selection uncertainty and get also a shared inference about AOD. Moreover, we acknowledge model discrepancy, i.e. forward model error, arising from approximations and assumptions done in forward model simulations. We have estimated the model discrepancy empirically by a statistical approach utilizing residuals of model fits. We use the measurements from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor in ultraviolet and visible bands, and in one wavelength band 675 nm in near-infrared, in order to study the functioning of the retrieval in a broad wavelength range. We exploit a fundamental classification of the aerosol models as weakly absorbing, biomass burning and desert dust aerosols. For experimental purpose we have included some dust type of aerosols having non-spherical particle shapes. For this study we have created the aerosol model LUTs with radiative transfer simulations using the libRadtran software package. It is reasonably straightforward to experiment with different aerosol types and evaluate the most probable aerosol type by the model selection method. We demonstrate the method in wildfire and dust events in a pixel level. In addition, we have evaluated in detail the results against ground-based remote sensing data from the AErosol RObotic NETwork (AERONET). Based on the case studies the method has ability to identify the appropriate aerosol types, but in some wildfire cases the AOD is overestimated compared to the AERONET result. The resulting uncertainty when accounting for the model selection problem and the imperfect forward modelling is higher compared to uncertainty when only measurement error is included in an observation model, as can be expected.

scholarly journals Analysis of Testing‐Based Forward Model Selection

Econometrica ◽  
2020 ◽  
Vol 88 (5) ◽  
pp. 2147-2173 ◽  
Author(s):  
Damian Kozbur
Keyword(s):  
Model Selection ◽  
Predictive Power ◽  
Convergence Rates ◽  
Forward Model ◽  
Statistical Hypothesis ◽  
Standard Errors ◽  
Regularity Conditions ◽  
Regression Problems ◽  
Heteroscedastic Data

This paper analyzes a procedure called Testing‐Based Forward Model Selection (TBFMS) in linear regression problems. This procedure inductively selects covariates that add predictive power into a working statistical model before estimating a final regression. The criterion for deciding which covariate to include next and when to stop including covariates is derived from a profile of traditional statistical hypothesis tests. This paper proves probabilistic bounds, which depend on the quality of the tests, for prediction error and the number of selected covariates. As an example, the bounds are then specialized to a case with heteroscedastic data, with tests constructed with the help of Huber–Eicker–White standard errors. Under the assumed regularity conditions, these tests lead to estimation convergence rates matching other common high‐dimensional estimators including Lasso.

Automatic Forward Model Selection Based on Leave-One-Out Cross-Validation

2009 ◽  
Vol 42 (10) ◽  
pp. 874-879
Author(s):  
Dajun Du ◽  
Kang li ◽  
Minrui Fei ◽  
George W. Irwin
Keyword(s):  
Model Selection ◽  
Cross Validation ◽  
Forward Model ◽  
Leave One Out

scholarly journals Testing-Based Forward Model Selection

2017 ◽  
Vol 107 (5) ◽  
pp. 266-269 ◽  
Author(s):  
Damian Kozbur
Keyword(s):  
Model Selection ◽  
Predictive Accuracy ◽  
Selection Procedure ◽  
Testing Procedure ◽  
Forward Model ◽  
Statistical Hypothesis ◽  
High Dimensional ◽  
Statistical Regression ◽  
Finite Sample ◽  
Variable Selection Procedure

This paper defines and studies a variable selection procedure called Testing-Based Forward Model Selection. The procedure inductively selects covariates which increase predictive accuracy into a working statistical regression model until a stopping criterion is met. The stopping criteria and selection criteria are defined using statistical hypothesis tests. The paper explicitly describes a testing procedure in the context of high-dimensional linear regression with heteroskedastic disturbances. Finally, a simulation study examines finite sample performance of the proposed procedure and shows that it behaves favorably in high-dimensional sparse settings in terms of prediction error and size of selected model.

ON SUITABLE MODEL SELECTION APPROACH FOR ANALYSIS OF MENTAL HEALTH SERVICE USE DATA

2019 ◽  
Vol 43 (2) ◽  
pp. 212-222
Author(s):  
DULAL K. BHAUMIK ◽  
◽  
SUBHASH ARYAL ◽  
SCOTT HUR ◽  
◽  
...  
Keyword(s):  
Mental Health ◽  
Mental Health Service ◽  
Model Selection ◽  
Health Service ◽  
Service Use ◽  
Mental Health Service Use ◽  
Health Service Use ◽  
Suitable Model ◽  
Model Selection Approach ◽  
Selection Approach

A Teaching Note for Model Selection and Validation

2013 ◽  
Vol 1 (2) ◽  
pp. 55-62 ◽  
Author(s):  
K. Muralidharan
Keyword(s):  
Model Selection

A Model Selection Approach to the Two-Phase Regression Estimation and The Human Sensitivity Analysis in Urban Ecosystem

1983 ◽  
Vol 10 (13) ◽  
pp. 1-18
Author(s):  
Masashi Itoh ◽  
Kazuo Noda ◽  
Yutaka Shinada ◽  
Naomi Tachibana
Keyword(s):  
Sensitivity Analysis ◽  
Model Selection ◽  
Urban Ecosystem ◽  
Regression Estimation ◽  
Two Phase ◽  
Model Selection Approach ◽  
Selection Approach
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