Process-monitoring-for-quality—A robust model selection criterion for the logistic regression algorithm

2019 ◽  
Vol 22 ◽  
pp. 6-10 ◽  
Author(s):  
Carlos A. Escobar ◽  
Ruben Morales-Menendez
Keyword(s):  
Logistic Regression ◽  
Model Selection ◽  
Process Monitoring ◽  
Selection Criterion ◽  
Model Selection Criterion ◽  
Robust Model ◽  
Robust Model Selection ◽  
Logistic Regression Algorithm

scholarly journals Robust Model Selection Criteria Based on Pseudodistances

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 304 ◽  
Author(s):  
Aida Toma ◽  
Alex Karagrigoriou ◽  
Paschalini Trentou
Keyword(s):  
Model Selection ◽  
Regression Models ◽  
Selection Criteria ◽  
Selection Criterion ◽  
Superior Performance ◽  
Linear Regression Models ◽  
Limit Laws ◽  
Model Selection Criteria ◽  
Robust Model ◽  
Robust Model Selection

In this paper, we introduce a new class of robust model selection criteria. These criteria are defined by estimators of the expected overall discrepancy using pseudodistances and the minimum pseudodistance principle. Theoretical properties of these criteria are proved, namely asymptotic unbiasedness, robustness, consistency, as well as the limit laws. The case of the linear regression models is studied and a specific pseudodistance based criterion is proposed. Monte Carlo simulations and applications for real data are presented in order to exemplify the performance of the new methodology. These examples show that the new selection criterion for regression models is a good competitor of some well known criteria and may have superior performance, especially in the case of small and contaminated samples.

scholarly journals Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 56-90
Author(s):  
Monica Defend ◽  
Aleksey Min ◽  
Lorenzo Portelli ◽  
Franz Ramsauer ◽  
Francesco Sandrini ◽  
...  
Keyword(s):  
Panel Data ◽  
Model Selection ◽  
Estimation Method ◽  
Selection Criterion ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Mixed Frequency ◽  
Model Selection Criterion ◽  
Mixed Frequency Data

This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses conditional factor moments in closed form. To determine the unknown factor dimension and autoregressive order, we propose a two-step information-based model selection criterion. The performance of our estimation procedure and the model selection criterion is investigated within a Monte Carlo study. Finally, we apply the Approximate Dynamic Factor Model to real-economy vintage data to support investment decisions and risk management. For this purpose, an autoregressive model with the estimated factor span of the mixed-frequency data as exogenous variables maps the behavior of weekly S&P500 log-returns. We detect the main drivers of the index development and define two dynamic trading strategies resulting from prediction intervals for the subsequent returns.

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