scholarly journals Model Selection in a Composite Likelihood Framework Based on Density Power Divergence

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 270
Author(s):  
Elena Castilla ◽  
Nirian Martín ◽  
Leandro Pardo ◽  
Konstantinos Zografos
Keyword(s):  
Model Selection ◽  
Simulation Study ◽  
Asymptotic Properties ◽  
Selection Criterion ◽  
Composite Likelihood ◽  
Tuning Parameter ◽  
Density Power Divergence ◽  
Numerical Examples ◽  
Model Selection Criterion ◽  
Power Divergence

This paper presents a model selection criterion in a composite likelihood framework based on density power divergence measures and in the composite minimum density power divergence estimators, which depends on an tuning parameter α . After introducing such a criterion, some asymptotic properties are established. We present a simulation study and two numerical examples in order to point out the robustness properties of the introduced model selection criterion.

scholarly journals On Selection Criteria for the Tuning Parameter in Robust Divergence

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1147
Author(s):  
Shonosuke Sugasawa ◽  
Shouto Yonekura
Keyword(s):  
Linear Regression ◽  
Asymptotic Approximation ◽  
Selection Criteria ◽  
Selection Criterion ◽  
Tuning Parameter ◽  
Density Power Divergence ◽  
Normal Distributions ◽  
Numerical Studies ◽  
Power Divergence ◽  
Derivatives Of

Although robust divergence, such as density power divergence and γ-divergence, is helpful for robust statistical inference in the presence of outliers, the tuning parameter that controls the degree of robustness is chosen in a rule-of-thumb, which may lead to an inefficient inference. We here propose a selection criterion based on an asymptotic approximation of the Hyvarinen score applied to an unnormalized model defined by robust divergence. The proposed selection criterion only requires first and second-order partial derivatives of an assumed density function with respect to observations, which can be easily computed regardless of the number of parameters. We demonstrate the usefulness of the proposed method via numerical studies using normal distributions and regularized linear regression.

scholarly journals Composite Likelihood Methods Based on Minimum Density Power Divergence Estimator

2017 ◽  
Author(s):  
Elena Castilla ◽  
Nirian Martín ◽  
Leandro Pardo ◽  
Konstantinos Zografos
Keyword(s):  
Asymptotic Properties ◽  
Wald Test ◽  
Composite Likelihood ◽  
Test Statistics ◽  
Test Statistic ◽  
Density Power Divergence ◽  
Minimum Density ◽  
New Family ◽  
Robust Version ◽  
Power Divergence

In this paper a robust version of the Wald test statistic for composite likelihood is considered by using the composite minimum density power divergence estimator instead of the composite maximum likelihood estimator. This new family of test statistics will be called Wald-type test statistics. The problem of testing a simple and a composite null hypothesis is considered and the robustness is studied on the basis of a simulation study. Previously, the composite minimum density power divergence estimator is introduced and its asymptotic properties are studied.

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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