Bayesian Lasso Regression Modeling via Model Averaging

Background: Body Mass Index (BMI) and maternal age are related to various disorders of the female reproductive system. This study aimed to estimate the causal effects of BMI and maternal age on the rate of metaphase II oocytes (MII) using a new statistical method based on Bayesian LASSO and model averaging. Methods: This investigation was a historical cohort study and data were collected from women who underwent assisted reproductive treatments in Tehran, Iran during 2015 to 2018. Exclusion criteria were gestational surrogacy and donor oocyte. We used a new method based on Bayesian LASSO and model average to capture important confounders. Results: Overall, 536 cycles of 398 women were evaluated. BMI and Age had inverse relationships with the number of MII based on univariate analysis, but after adjusting the effects of other variables, there was just a significant association between age and the number of MII (adjusted incidence rate ratio (aIRR) of age =0.989, 95% CI: [0.979, 0.998], P=0.02). The results of causal inference based on the new presented method showed that the overall effects of age and BMI of all patients were significantly and inversely associated with the number of MII (both P<0.001). Therefore the expected number of MII decreased by 0.99 for an increase of 1 year (95% CI: [-1.00,-0.97]) and decreased by 0.99 for each 1-unit increase in BMI (95% CI: [-1.01,-0.98]). Conclusion: Maternal age and BMI have significant adverse casual effects on the rate of MII in patients undergoing ART when the effects of important confounders were adjusted.

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SPARSE REGRESSION MODELING VIA THE MAP BAYESIAN LASSO

Bulletin of informatics and cybernetics ◽

10.5109/1909523 ◽

2015 ◽

Vol 47 ◽

pp. 37-58

Author(s):

Ibuki Hoshina

Keyword(s):

Regression Modeling ◽

Sparse Regression ◽

Bayesian Lasso

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Triple the Gamma—A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models

Econometrics ◽

10.3390/econometrics8020020 ◽

2020 ◽

Vol 8 (2) ◽

pp. 20

Author(s):

Annalisa Cadonna ◽

Sylvia Frühwirth-Schnatter ◽

Peter Knaus

Keyword(s):

Bayesian Model Averaging ◽

Model Averaging ◽

Predictive Performance ◽

Outcome Variable ◽

Time Varying ◽

Bayesian Lasso ◽

Vector Autoregressive ◽

Explanatory Variables ◽

Time Varying Parameter ◽

Shrinkage Prior

Time-varying parameter (TVP) models are very flexible in capturing gradual changes in the effect of explanatory variables on the outcome variable. However, in particular when the number of explanatory variables is large, there is a known risk of overfitting and poor predictive performance, since the effect of some explanatory variables is constant over time. We propose a new prior for variance shrinkage in TVP models, called triple gamma. The triple gamma prior encompasses a number of priors that have been suggested previously, such as the Bayesian Lasso, the double gamma prior and the Horseshoe prior. We present the desirable properties of such a prior and its relationship to Bayesian Model Averaging for variance selection. The features of the triple gamma prior are then illustrated in the context of time varying parameter vector autoregressive models, both for simulated dataset and for a series of macroeconomics variables in the Euro Area.

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Comparison of Permeability Estimation Models Through Bayesian Model Averaging and LASSO Regression

10.2118/177556-ms ◽

2015 ◽

Author(s):

Watheq J. Al-Mudhafar

Keyword(s):

Bayesian Model ◽

Bayesian Model Averaging ◽

Model Averaging ◽

Lasso Regression ◽

Permeability Estimation ◽

Estimation Models

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Exploring Spatial Trends and Influencing Factors for Gastric Cancer Based on Bayesian Statistics: A Case Study of Shanxi, China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15091824 ◽

2018 ◽

Vol 15 (9) ◽

pp. 1824 ◽

Cited By ~ 3

Author(s):

Gehong Zhang ◽

Junming Li ◽

Sijin Li ◽

Yang Wang

Keyword(s):

Gastric Cancer ◽

Vegetation Cover ◽

High Probability ◽

Lasso Regression ◽

Univariable Analysis ◽

Bayesian Lasso ◽

Pork Production ◽

Geographic Factors ◽

Spatial Trends ◽

Per Capita

Gastric cancer (GC) is the fourth most common type of cancer and the second leading cause of cancer-related deaths worldwide. To detect the spatial trends of GC risk based on hospital-diagnosed patients, this study presented a selection probability model and integrated it into the Bayesian spatial statistical model. Then, the spatial pattern of GC risk in Shanxi Province in north central China was estimated. In addition, factors influencing GC were investigated mainly using the Bayesian Lasso model. The spatial variability of GC risk in Shanxi has the conspicuous feature of being ‘high in the south and low in the north’. The highest GC relative risk was 1.291 (95% highest posterior density: 0.789–4.002). The univariable analysis and Bayesian Lasso regression results showed that a diverse dietary structure and increased consumption of beef and cow milk were significantly (p ≤ 0.08) and in high probability (greater than 68%) negatively associated with GC risk. Pork production per capita has a positive correlation with GC risk. Moreover, four geographic factors, namely, temperature, terrain, vegetation cover, and precipitation, showed significant (i68%) negatively associated with GC risk. Pork production per capita has a positive correlation with GC risk. Moreover, four geographic factors, namely, temperature, terrain, vegetation cover, and precipitation, showed significant (p < 0.05) associations with GC risk based on univariable analysis, and associated with GC risks in high probability (greater than 60%) inferred from Bayesian Lasso regression model.

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