scholarly journals A Similarity-Weighted Informative Prior Distribution for Bayesian Multiple Regression Models

2021 ◽  
Vol 12 ◽  
Author(s):  
Christoph König
Keyword(s):  
Multiple Regression ◽  
Similarity Measure ◽  
Prior Distribution ◽  
Regression Models ◽  
Prior Distributions ◽  
Informative Prior ◽  
Informative Prior Distribution ◽  
Multiple Regression Models ◽  
Power Prior ◽  
Informative Prior Distributions

Specifying accurate informative prior distributions is a question of carefully selecting studies that comprise the body of comparable background knowledge. Psychological research, however, consists of studies that are being conducted under different circumstances, with different samples and varying instruments. Thus, results of previous studies are heterogeneous, and not all available results can and should contribute equally to an informative prior distribution. This implies a necessary weighting of background information based on the similarity of the previous studies to the focal study at hand. Current approaches to account for heterogeneity by weighting informative prior distributions, such as the power prior and the meta-analytic predictive prior are either not easily accessible or incomplete. To complicate matters further, in the context of Bayesian multiple regression models there are no methods available for quantifying the similarity of a given body of background knowledge to the focal study at hand. Consequently, the purpose of this study is threefold. We first present a novel method to combine the aforementioned sources of heterogeneity in the similarity measure ω. This method is based on a combination of a propensity-score approach to assess the similarity of samples with random- and mixed-effects meta-analytic models to quantify the heterogeneity in outcomes and study characteristics. Second, we show how to use the similarity measure ωas a weight for informative prior distributions for the substantial parameters (regression coefficients) in Bayesian multiple regression models. Third, we investigate the performance and the behavior of the similarity-weighted informative prior distribution in a comprehensive simulation study, where it is compared to the normalized power prior and the meta-analytic predictive prior. The similarity measure ω and the similarity-weighted informative prior distribution as the primary results of this study provide applied researchers with means to specify accurate informative prior distributions.

scholarly journals Updating Knowledge in The Estimation of The Genetics Parameters Multi-trait and Multi-Environment Bayesian Analysis in Rice (Oryza Sativa L.)

2021 ◽  
Author(s):  
Camila Ferreira Azevedo ◽  
Cynthia Barreto ◽  
Matheus Suela ◽  
Moysés Nascimento ◽  
Antônio Carlos Júnior ◽  
...  
Keyword(s):  
Bayesian Approach ◽  
Prior Distribution ◽  
Variance Components ◽  
Genetic Correlations ◽  
Bayesian Framework ◽  
Prior Distributions ◽  
Informative Prior ◽  
Informative Prior Distribution ◽  
Informative Prior Distributions ◽  
The Bayesian Approach

Abstract Among the multi-trait models used to jointly study several traits and environments, the Bayesian framework has been a preferable tool for using a more complex and biologically realistic model. In most cases, the non-informative prior distributions are adopted in studies using the Bayesian approach. Still, the Bayesian approach tends to present more accurate estimates when it uses informative prior distributions. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models under a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data from rice. The study involved data pertained to rice genotypes in three environments and five agricultural years (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components and genetic and non-genetic parameters were estimated by the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. The use of more informative prior distributions makes it possible to detect genetic correlations between traits, which cannot be achieved with the use of non-informative prior distributions. Therefore, this mechanism presented for updating knowledge to the elicitation of an informative prior distribution can be efficiently applied in rice genetic selection.

scholarly journals 242 Effect of trucking distance on sale price of beef calf and feeder cattle lots sold through video auctions from 2010 through 2018

2020 ◽  
Vol 98 (Supplement_3) ◽  
pp. 10-11
Author(s):  
Esther D McCabe ◽  
Mike E King ◽  
Karol E Fike ◽  
Maggie J Smith ◽  
Glenn M Rogers ◽  
...  
Keyword(s):  
Multiple Regression ◽  
Regression Models ◽  
Average Weight ◽  
Sale Price ◽  
Feeder Cattle ◽  
A Value ◽  
Multiple Regression Models ◽  
Determine Effect

Abstract The objective was to determine effect of trucking distance on sale price of beef calf and feeder cattle lots sold through Superior Livestock Video Auctions from 2010 through 2018. Data analyzed were collected from 211 livestock video auctions. There were 42,043 beef calf lots and 19,680 feeder cattle lots used in these analyses. Six states (Colorado, Iowa, Kansas, Nebraska, Oklahoma, and Texas) of delivery comprised 70% of calf lots and 83% of feeder cattle lots and were used in these analyses. All lot characteristics that could be accurately quantified or categorized were used to develop multiple regression models that evaluated effects of independent factors using backwards selection. A value of P < 0.05 was used to maintain a factor in the final models. Based upon reported state of origin and state of delivery, lots were categorized into one of the following trucking distance categories: 1) Within-State, 2) Short-Haul, 3) Medium-Haul, and 4) Long-Haul. Average weight and number of calves in lots analyzed was 259.2 ± 38.4 kg BW and 100.6 ± 74.3 head, respectively. Average weight and number of feeder cattle in lots analyzed was 358.4 ± 34.3 kg BW and 110.6 ± 104.1 head, respectively. Beef calf lots hauled Within-State sold for more ($169.24/45.36 kg; P < 0.0001) than other trucking distance categories (Table 1). Long-Haul calf lots sold for the lowest (P < 0.0001) price ($166.70/45.36 kg). Within-State and Short-Haul feeder cattle lots sold for the greatest (P < 0.0001) price ($149.96 and $149.81/45.36 kg, respectively; Table 2). Long-Haul feeder cattle lots sold for the lowest (P < 0.0001) price, $148.43/45.36 kg. These results indicate there is a price advantage for lots expected to be hauled shorter distances, likely because of cost and risk associated with transportation.

Multiple regression models for predicting total daily pollen concentration in Cartagena

Grana ◽  
2005 ◽  
Vol 44 (2) ◽  
pp. 108-114 ◽  
Author(s):  
José Manuel Angosto ◽  
Stella Moreno‐Grau ◽  
Javier Bayo ◽  
Belén Elvira‐Rendueles
Keyword(s):  
Multiple Regression ◽  
Regression Models ◽  
Pollen Concentration ◽  
Multiple Regression Models ◽  
Daily Pollen Concentration

A Characterization of the Exponential Distribution

1994 ◽  
Vol 44 (1-2) ◽  
pp. 123-126
Author(s):  
E. S. Jebvanand ◽  
N. Unnikrishnan Nair
Keyword(s):  
Exponential Distribution ◽  
Prior Distribution ◽  
Future Observation ◽  
Informative Prior ◽  
Informative Prior Distribution ◽  
Continuous Population

In this note we prove that the exponential distribution is characterized by the property [Formula: see text] where Y is a future observation and x1, x2,…, x n are identical and independently distributed observations from a continuous population with density f( x; a), where a is assumed to have a non-informative prior distribution

Abstract WP238: Atrial Fibrillation is Associated With Severe Basal Ganglia Perivascular Spaces

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Alvin S Das ◽  
Elif Gokcal ◽  
Robert W Regenhardt ◽  
Andrew Warren ◽  
Kristin Schwab ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Basal Ganglia ◽  
Multiple Regression ◽  
Regression Models ◽  
Small Vessel Disease ◽  
Perivascular Spaces ◽  
Nonvalvular Atrial Fibrillation ◽  
Related Factors ◽  
Multiple Regression Models ◽  
Independent Effects

Introduction: High burdens of basal ganglia-perivascular spaces (BG-PVS) are often attributed to underlying hypertensive cerebral small vessel disease (HTN-CSVD). Although PVS are thought to arise from decreased perivascular drainage related to changes in arterial pulsatility, the contribution of pulsatility changes from nonvalvular atrial fibrillation (NVAF) has not been studied. Hypothesis: We hypothesized that NVAF patients have a higher burden of BG-PVS than HTN-CSVD patients, possibly through hemodynamic factors related to NVAF. Methods: Through an observational single-center study of consecutive stroke patients, we compared BG-EPVS severity between 136 patients with NVAF-related ischemic stroke (NVAF-IS) and 107 patients with HTN-CSVD-related intracerebral hemorrhage (HTN-ICH) without NVAF. Within the NVAF cohort, we also built multiple regression models to evaluate independent effects of NVAF-related factors on BG-PVS. All multiple regression models were adjusted for age, hypertension, sex, and neuroimaging markers of CSVD (extent of white matter hyperintensities (WMH), presence of lacunes, and cerebral microbleeds). Results: Patients with NVAF-IS were older than patients with HTN-ICH (75 + 12 vs. 64 + 13, p < 0.0001); however, there was no difference in sex between groups ( p = 0.6). Severe BG-PVS (defined as > 20 PVS in the BG) were found in 42.6% of NVAF-IS patients vs. 8.4% of HTN-ICH ( p < 0.0001). Even after multivariate adjustment, the presence of NVAF remained significantly related to BG-PVS ( p = 0.001). Within the NVAF cohort, CHA2DS2-VASc was associated with the presence of severe BG-PVS ( p = 0.003) despite controlling for other covariates. When CHA2DS2-VASc was replaced with its individual components in the same regression model, congestive heart failure (CHF, p = 0.017), WMH burden ( p = 0.009), and age ( p = 0.02) were found to be predictors of severe BG-PVS. Conclusions: Severe BG-PVS were significantly more common in NVAF patients compared to HTN-CSVD patients. NVAF-related features (CHA2DS2-VASc score) and CHF were associated with higher burdens of BG-PVS. These findings suggest that NVAF might play a role in the development of BG-PVS, conceivably through hemodynamic factors.

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