scholarly journals Graphical diagnostics to check model misspecification for the proportional odds regression model

2009 ◽  
Vol 28 (3) ◽  
pp. 412-429 ◽  
Author(s):  
Ivy Liu ◽  
Bhramar Mukherjee ◽  
Thomas Suesse ◽  
David Sparrow ◽  
Sung Kyun Park
Keyword(s):  
Regression Model ◽  
Model Misspecification ◽  
Proportional Odds ◽  
Graphical Diagnostics

scholarly journals Bias correction for the proportional odds logistic regression model with application to a study of surgical complications

2012 ◽  
Vol 62 (2) ◽  
pp. 233-250 ◽  
Author(s):  
Stuart R. Lipsitz ◽  
Garrett M. Fitzmaurice ◽  
Scott E. Regenbogen ◽  
Debajyoti Sinha ◽  
Joseph G. Ibrahim ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Bias Correction ◽  
Surgical Complications ◽  
Logistic Regression Model ◽  
Proportional Odds

A goodness-of-fit test for the proportional odds regression model

2012 ◽  
Vol 32 (13) ◽  
pp. 2235-2249 ◽  
Author(s):  
Morten W. Fagerland ◽  
David W. Hosmer
Keyword(s):  
Regression Model ◽  
Goodness Of Fit ◽  
Proportional Odds ◽  
Goodness Of Fit Test

scholarly journals Regression-Based Expected Shortfall Backtesting

2020 ◽  
Author(s):  
Sebastian Bayer ◽  
Timo Dimitriadis
Keyword(s):  
Regression Model ◽  
Value At Risk ◽  
Asymptotic Theory ◽  
Risk Measure ◽  
Model Misspecification ◽  
Expected Shortfall ◽  
Test Specifications ◽  
Covariance Estimator ◽  
Input Variables ◽  
Empirical Illustration

Abstract This article introduces novel backtests for the risk measure Expected Shortfall (ES) following the testing idea of Mincer and Zarnowitz (1969). Estimating a regression model for the ES stand-alone is infeasible and thus, our tests are based on a joint regression model for the Value at Risk (VaR) and the ES, which allows for different test specifications. These ES backtests are the first which solely backtest the ES in the sense that they only require ES forecasts as input variables. As the tests are potentially subject to model misspecification, we provide asymptotic theory under misspecification for the underlying joint regression. We find that employing a misspecification robust covariance estimator substantially improves the tests’ performance. We compare our backtests to existing joint VaR and ES backtests and find that our tests outperform the existing alternatives throughout all considered simulations. In an empirical illustration, we apply our backtests to ES forecasts for 200 stocks of the S&P 500 index.

Discrete Postprocessing of Total Cloud Cover Ensemble Forecasts

2016 ◽  
Vol 144 (7) ◽  
pp. 2565-2577 ◽  
Author(s):  
Stephan Hemri ◽  
Thomas Haiden ◽  
Florian Pappenberger
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Cloud Cover ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Total Cloud Cover ◽  
Proportional Odds ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Medium Range

Abstract This paper presents an approach to postprocess ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical postprocessing of ensemble predictions are tested: the first approach is based on multinomial logistic regression and the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on stationwise postprocessing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model.

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