scholarly journals Robustness of model averaging methods for the violation of standard linear regression assumptions

2021 ◽  
Vol 28 (2) ◽  
pp. 189-204
Author(s):  
Yongsu Lee ◽  
Juwon Song
Keyword(s):  
Linear Regression ◽  
Model Averaging ◽  
Averaging Methods ◽  
Standard Linear

scholarly journals The Neural Network Assisted Land Use Regression

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 452
Author(s):  
Jan Bitta ◽  
Vladislav Svozilík ◽  
Aneta Svozilíková Krakovská
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Linear Regression ◽  
Dispersion Model ◽  
Land Use Regression ◽  
Emission Data ◽  
Pollution Dispersion ◽  
Factors Affecting ◽  
Standard Linear

Land Use Regression (LUR) is one of the air quality assessment modelling techniques. Its advantages lie mainly in a much simpler mathematical apparatus, quicker and simpler calculations, and a possibility to incorporate more factors affecting pollutant concentration than standard dispersion models. The goal of the study was to perform the LUR model in the Polish-Czech-Slovakian Tritia region, to test two sets of pollution data input factors, i.e., factors based on emission data and pollution dispersion model results, to test regression via neural networks and compare it with standard linear regression. Both input datasets, emission data and pollution dispersion model results, provided a similar quality of results in the case when standard linear regression was used, the R2 of the models was 0.639 and 0.652. Neural network regression provided a significantly higher quality of the models, their R2 was 0.937 and 0.938 for the factors based on emission data and pollution dispersion model results respectively.

Sequential Model Averaging for High Dimensional Linear Regression Models

2017 ◽  
Author(s):  
Wei Lan ◽  
Yingying Ma ◽  
Junlong Zhao ◽  
Hansheng Wang ◽  
Chih-Ling Tsai
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Model Averaging ◽  
High Dimensional ◽  
Linear Regression Models ◽  
Sequential Model

scholarly journals Inferring cellular regulatory networks with Bayesian model averaging for linear regression (BMALR)

2014 ◽  
Vol 10 (8) ◽  
pp. 2023-2030 ◽  
Author(s):  
Xun Huang ◽  
Zhike Zi
Keyword(s):  
Linear Regression ◽  
Molecular Interactions ◽  
Computational Efficiency ◽  
Bayesian Model ◽  
Prediction Accuracy ◽  
Regulatory Networks ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
High Prediction ◽  
High Computational Efficiency

A new method that uses Bayesian model averaging for linear regression to infer molecular interactions in biological systems with high prediction accuracy and high computational efficiency.

scholarly journals Ensemble Regression

2009 ◽  
Vol 137 (7) ◽  
pp. 2365-2379 ◽  
Author(s):  
David A. Unger ◽  
Huug van den Dool ◽  
Edward O’Lenic ◽  
Dan Collins
Keyword(s):  
Linear Regression ◽  
Error Variance ◽  
Ensemble Member ◽  
Ensemble Prediction ◽  
Ensemble Spread ◽  
Ensemble Forecasts ◽  
Ensemble Mean ◽  
Standard Linear ◽  
Environmental Prediction ◽  
Ensemble Regression

A regression model was developed for use with ensemble forecasts. Ensemble members are assumed to represent a set of equally likely solutions, one of which will best fit the observation. If standard linear regression assumptions apply to the best member, then a regression relationship can be derived between the full ensemble and the observation without explicitly identifying the best member for each case. The ensemble regression equation is equivalent to linear regression between the ensemble mean and the observation, but is applied to each member of the ensemble. The “best member” error variance is defined in terms of the correlation between the ensemble mean and the observations, their respective variances, and the ensemble spread. A probability density function representing the ensemble prediction is obtained from the normalized sum of the best-member error distribution applied to the regression forecast from each ensemble member. Ensemble regression was applied to National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) forecasts of seasonal mean Niño-3.4 SSTs on historical forecasts for the years 1981–2005. The skill of the ensemble regression was about the same as that of the linear regression on the ensemble mean when measured by the continuous ranked probability score (CRPS), and both methods produced reliable probabilities. The CFS spread appears slightly too high for its skill, and the CRPS of the CFS predictions can be slightly improved by reducing its ensemble spread to about 0.8 of its original value prior to regression calibration.

scholarly journals Mapping QTLs for binary traits in backcross and F2 populations

1996 ◽  
Vol 68 (1) ◽  
pp. 55-63 ◽  
Author(s):  
P. M. Visscher ◽  
C. S. Haley ◽  
S. A. Knott
Keyword(s):  
Quantitative Trait Loci ◽  
Linear Regression ◽  
Quantitative Trait ◽  
Generalized Linear Model ◽  
Model Parameters ◽  
Qtl Detection ◽  
Binary Traits ◽  
Standard Linear ◽  
Marker Spacing ◽  
Qtl Effects

SummaryMapping quantitative trait loci (QTLs) for binary traits in backcross and F2 populations was investigated using stochastic stimulation. Data were analysed using either linear regression or a generalized linear model. Parameters which were varied in the simulations were the population size (200 and 500), heritability in the backcross or F2 population (0·01, 0·05, 0·10), marker spacing (10 and 20 cM) and the incidence of the trait (0·50, 0·25, 0·10). The methods gave very similar results in terms of estimates of the QTL location and QTL effects and power of QTL detection, and it was concluded that in practice treating the zero-one data as continuous and using standard linear regression was efficient.

scholarly journals Barrier Option Pricing with Model Averaging Methods under Local Volatility Models

2011 ◽  
Vol 10 (1) ◽  
pp. 84-94 ◽  
Author(s):  
Nam-Hyoung Kim ◽  
Kyu-Hwan Jung ◽  
Jae-Wook Lee ◽  
Gyu-Sik Han
Keyword(s):  
Option Pricing ◽  
Model Averaging ◽  
Barrier Option ◽  
Local Volatility ◽  
Averaging Methods ◽  
Volatility Models ◽  
Barrier Option Pricing

scholarly journals Forecast Bitcoin Volatility with Least Squares Model Averaging

Econometrics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 40 ◽  
Author(s):  
Tian Xie
Keyword(s):  
Least Squares ◽  
Regression Models ◽  
Model Averaging ◽  
Realized Volatility ◽  
Model Specification ◽  
Asset Market ◽  
Averaging Methods ◽  
Empirical Results ◽  
Conventional Regression

In this paper, we study forecasting problems of Bitcoin-realized volatility computed on data from the largest crypto exchange—Binance. Given the unique features of the crypto asset market, we find that conventional regression models exhibit strong model specification uncertainty. To circumvent this issue, we suggest using least squares model-averaging methods to model and forecast Bitcoin volatility. The empirical results demonstrate that least squares model-averaging methods in general outperform many other conventional regression models that ignore specification uncertainty.

Model Uncertainty

2019 ◽  
pp. 175-206
Author(s):  
Jeffrey S. Racine
Keyword(s):  
Model Selection ◽  
Model Uncertainty ◽  
Model Averaging ◽  
Quadratic Program ◽  
Selection Methods ◽  
Averaging Methods

This chapter covers model selection methods and model averaging methods. It relies on knowledge of solving a quadratic program which is outlined in an appendix.

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