scholarly journals Bayesian Forecasting with Highly Correlated Predictors

2012 ◽  
Author(s):  
Dimitris Korobilis
Keyword(s):  
Bayesian Forecasting ◽  
Highly Correlated ◽  
Correlated Predictors

scholarly journals A Comparative Study of the Lasso-type and Heuristic Model Selection Methods

2013 ◽  
Vol 233 (4) ◽  
pp. 526-549 ◽  
Author(s):  
Ivan Savin
Keyword(s):  
Model Selection ◽  
Search Space ◽  
Information Criteria ◽  
Adaptive Lasso ◽  
Heuristic Model ◽  
Selection Methods ◽  
Monte Carlo Simulation Study ◽  
Discrete Search ◽  
Highly Correlated ◽  
Correlated Predictors

Summary This study presents a first comparative analysis of Lasso-type (Lasso, adaptive Lasso, elastic net) and heuristic subset selection methods. Although the Lasso has shown success in many situations, it has some limitations. In particular, inconsistent results are obtained for pairwise highly correlated predictors. An alternative to the Lasso is constituted by model selection based on information criteria (IC), which remain consistent in the situation mentioned. However, these criteria are hard to optimize due to a discrete search space. To overcome this problem, an optimization heuristic (Genetic Algorithm) is applied. To this end, results of a Monte-Carlo simulation study together with an application to an actual empirical problem are reported to illustrate the performance of the methods.

Bayesian variable selection and shrinkage strategies in a complicated modelling setting with missing data: A case study using multistate models

2020 ◽  
pp. 1471082X2092097
Author(s):  
Lauren J Beesley ◽  
Jeremy MG Taylor
Keyword(s):  
Missing Data ◽  
Variable Selection ◽  
Bayesian Variable Selection ◽  
Multistate Models ◽  
Time To Event ◽  
Shrinkage Methods ◽  
Highly Correlated ◽  
Correlated Predictors ◽  
Insight Into

Multistate modelling is a strategy for jointly modelling related time-to-event outcomes that can handle complicated outcome relationships, has appealing interpretations, can provide insight into different aspects of disease development and can be useful for making individualized predictions. A challenge with using multistate modelling in practice is the large number of parameters, and variable selection and shrinkage strategies are needed in order for these models to gain wider adoption. Application of existing selection and shrinkage strategies in the multistate modelling setting can be challenging due to complicated patterns of data missingness, inclusion of highly correlated predictors and hierarchical parameter relationships. In this article, we discuss how to modify and implement several existing Bayesian variable selection and shrinkage methods in a general multistate modelling setting. We compare the performance of these methods in terms of parameter estimation and model selection in a multistate cure model of recurrence and death in patients treated for head and neck cancer. We can view this work as a case study of variable selection and shrinkage in a complicated modelling setting with missing data.

scholarly journals Regression with Highly Correlated Predictors: Variable Omission Is Not the Solution

2021 ◽  
Vol 18 (8) ◽  
pp. 4259
Author(s):  
Mariella Gregorich ◽  
Susanne Strohmaier ◽  
Daniela Dunkler ◽  
Georg Heinze
Keyword(s):  
Public Health ◽  
Regression Models ◽  
Research Question ◽  
Diagnostic Tools ◽  
Environmental Sciences ◽  
Independent Variables ◽  
Bivariate Correlation ◽  
Highly Correlated ◽  
Correlated Predictors ◽  
Epidemiology And Public Health

Regression models have been in use for decades to explore and quantify the association between a dependent response and several independent variables in environmental sciences, epidemiology and public health. However, researchers often encounter situations in which some independent variables exhibit high bivariate correlation, or may even be collinear. Improper statistical handling of this situation will most certainly generate models of little or no practical use and misleading interpretations. By means of two example studies, we demonstrate how diagnostic tools for collinearity or near-collinearity may fail in guiding the analyst. Instead, the most appropriate way of handling collinearity should be driven by the research question at hand and, in particular, by the distinction between predictive or explanatory aims.

scholarly journals KAJIAN SIMULASI PERBANDINGAN METODE REGRESI KUADRAT TERKECIL PARSIAL, SUPPORT VECTOR MACHINE, DAN RANDOM FOREST

2020 ◽  
Vol 4 (1) ◽  
pp. 203-215
Author(s):  
Asep Andri Fauzi ◽  
Agus M. Soleh ◽  
Anik Djuraidah
Keyword(s):  
Random Forest ◽  
Small Sample Size ◽  
Small Sample ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Support Vector ◽  
Ordinary Least Square ◽  
Highly Correlated ◽  
Correlated Predictors

Highly correlated predictors and nonlinear relationships between response and predictors potentially affected the performance of predictive modeling, especially when using the ordinary least square (OLS) method. The simple technique to solve this problem is by using another method such as Partial Least Square Regression (PLSR), Support Vector Regression with kernel Radial Basis Function (SVR-RBF), and Random Forest Regression (RFR). The purpose of this study is to compare OLS, PLSR, SVR-RBF, and RFR using simulation data. The methods were evaluated by the root mean square error prediction (RMSEP). The result showed that in the linear model, SVR-RBF and RFR have large RMSEP; OLS and PLSR are better than SVR-RBF and RFR, and PLSR provides much more stable prediction than OLS in case of highly correlated predictors and small sample size. In nonlinear data, RFR produced the smallest RMSEP when data contains high correlated predictors.

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