A Bayesian Hierarchical Regression Approach to Longitudinal Data in Empirical Legal Studies

2009 ◽  
Author(s):  
William Anderson ◽  
Martin T. Wells
Keyword(s):  
Longitudinal Data ◽  
Hierarchical Regression ◽  
Bayesian Hierarchical ◽  
Legal Studies ◽  
Regression Approach

A Bayesian adjusted plus-minus analysis for the esport Dota 2

2020 ◽  
Vol 16 (4) ◽  
pp. 325-341
Author(s):  
Nicholas Clark ◽  
Brian Macdonald ◽  
Ian Kloo
Keyword(s):  
Professional Sports ◽  
Hierarchical Regression ◽  
Sports Analytics ◽  
Bayesian Hierarchical ◽  
The Past ◽  
Score Statistics ◽  
Level Statistics ◽  
Traditional Sports ◽  
Level Information ◽  
Individual Player

AbstractAnalytics and professional sports have become linked over the past several years, but little attention has been paid to the growing field of esports within the sports analytics community. We seek to apply an Adjusted Plus Minus (APM) model, an accepted analytic approach used in traditional sports like hockey and basketball, to one particular esports game: Defense of the Ancients 2 (Dota 2). As with traditional sports, we show how APM metrics developed with Bayesian hierarchical regression can be used to quantify individual player contributions to their teams and, ultimately, use this player-level information to predict game outcomes. In particular, we first provide evidence that gold can be used as a continuous proxy for wins to evaluate a team’s performance, and then use a Bayesian APM model to estimate how players contribute to their team’s gold differential. We demonstrate that this APM model outperforms models based on common team-level statistics (often referred to as “box score statistics”). Beyond the specifics of our modeling approach, this paper serves as an example of the potential utility of applying analytical methodologies from traditional sports analytics to esports.

scholarly journals Risk governance and cybercrime: the hierarchical regression approach

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Olayinka Adedayo Erin ◽  
Adebola Daniel Kolawole ◽  
Abdurafiu Olaiya Noah
Keyword(s):  
Hierarchical Regression ◽  
Risk Governance ◽  
Regression Approach

Bayesian hierarchical regression models for detecting QTLs in plant experiments

2008 ◽  
Vol 35 (7) ◽  
pp. 799-808 ◽  
Author(s):  
Edward L. Boone ◽  
Susan J. Simmons ◽  
Haikun Bao ◽  
Ann E. Stapleton
Keyword(s):  
Regression Models ◽  
Hierarchical Regression ◽  
Bayesian Hierarchical

A Hierarchical Regression Approach for Unconstrained Face Analysis

2015 ◽  
Vol 29 (08) ◽  
pp. 1556011 ◽  
Author(s):  
Yuanyuan Liu ◽  
Jingying Chen ◽  
Cunjie Shan ◽  
Zhiming Su ◽  
Pei Cai
Keyword(s):  
Feature Detection ◽  
Facial Feature ◽  
Hierarchical Regression ◽  
Facial Features ◽  
Head Pose ◽  
Face Analysis ◽  
Facial Feature Detection ◽  
Face Area ◽  
Regression Approach ◽  
Deformation Models

Head pose and facial feature detection are important for face analysis. However, many studies reported good results in constrained environment, the performance could be decreased due to the high variations in facial appearance, poses, illumination, occlusion, expression and make-up. In this paper, we propose a hierarchical regression approach, Dirichlet-tree enhanced random forests (D-RF) for face analysis in unconstrained environment. D-RF introduces Dirichlet-tree probabilistic model into regression RF framework in the hierarchical way to achieve the efficiency and robustness. To eliminate noise influence of unconstrained environment, facial patches extracted from face area are classified as positive or negative facial patches, only positive facial patches are used for face analysis. The proposed hierarchical D-RF works in two iterative procedures. First, coarse head pose is estimated to constrain the facial features detection, then the head pose is updated based on the estimated facial features. Second, the facial feature localization is refined based on the updated head pose. In order to further improve the efficiency and robustness, multiple probabilitic models are learned in leaves of the D-RF, i.e. the patch’s classification, the head pose probabilities, the locations of facial points and face deformation models (FDM). Moreover, our algorithm takes a composite weight voting method, where each patch extracted from the image can directly cast a vote for the head pose or each of the facial features. Extensive experiments have been done with different publicly available databases. The experimental results demonstrate that the proposed approach is robust and efficient for head pose and facial feature detection.

scholarly journals Bayesian Hierarchical Modeling for Categorical Longitudinal Data from Sedation Measurements

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Erol Terzi ◽  
Mehmet Ali Cengiz
Keyword(s):  
Longitudinal Data ◽  
Hierarchical Model ◽  
Hierarchical Modeling ◽  
Bayesian Hierarchical Model ◽  
Bayesian Hierarchical Modeling ◽  
Multinomial Model ◽  
Resonance Imaging ◽  
Bayesian Hierarchical ◽  
Ct Data ◽  
Magnetic Resonance Imaging Mri

We investigate a Bayesian hierarchical model for the analysis of categorical longitudinal data from sedation measurement for Magnetic Resonance Imaging (MRI) and Computerized Tomography (CT). Data for each patient is observed at different time points within the time up to 60 min. A model for the sedation level of patients is developed by introducing, at the first stage of a hierarchical model, a multinomial model for the response, and then subsequent terms are introduced. To estimate the model, we use the Gibbs sampling given some appropriate prior distributions.

scholarly journals An examination of disparities in cancer incidence in Texas using Bayesian random coefficient models

2015 ◽  
Author(s):  
Corey Sparks
Keyword(s):  
Cancer Risk ◽  
Cancer Incidence ◽  
The United States ◽  
Population Based ◽  
Hierarchical Regression ◽  
Random Coefficient Models ◽  
Bayesian Hierarchical ◽  
Incidence Data ◽  
Spatio Temporal ◽  
Spatially Varying

Disparities in cancer risk exist between ethnic groups in the United States. These disparities often result from differential access to healthcare, differences in socioeconomic status and differential exposure to carcinogens. This study uses cancer incidence data from the population based Texas Cancer Registry to investigate the disparities in digestive and respiratory cancers from 2000 to 2008. A Bayesian hierarchical regression approach is used. Specifically, a spatially varying coefficient model of the disparity between Hispanic and Non-Hispanic incidence is used. Results suggest that a spatio-temporal heterogeneity model best accounts for the observed Hispanic disparity in cancer risk. Overall, there is a significant disadvantage for the Hispanic population of Texas with respect to both of these cancers, and this disparity varies significantly over space. The greatest disparities between Hispanics and Non-Hispanics in digestive and respiratory cancers occur in eastern Texas, with patterns emerging as early as 2000 and continuing until 2008.

scholarly journals Using a coupled-oscillator model of speech rhythm to estimate rhythmic variability in two Brazilian Portuguese varieties (CE and SP)

2021 ◽  
Vol 2 (4) ◽  
pp. e577
Author(s):  
Pablo Arantes ◽  
Ronaldo Mangueira Lima Júnior
Keyword(s):  
Brazilian Portuguese ◽  
The State ◽  
Temporal Organization ◽  
Hierarchical Regression ◽  
Entrainment Rate ◽  
Speech Rhythm ◽  
Bayesian Hierarchical ◽  
Syllable Duration ◽  
Natural Duration ◽  
Relative Change

This paper presents preliminary results of a semi-automatic methodology to extract three parameters of a dynamic model of speech rhythm. The model attempts to analyze the production of rhythm as a system of coupled oscillators which represent syllabicity and phrase stress as levels of temporal organization. The estimated parameters are the syllabic oscillator entrainment rate (alpha), the syllabic oscillator decay rate (beta), and the coupling strength between the oscillators (w0). The methodology involves finding the <alpha, beta, w0> combination that minimizes the distance between natural duration contours and simulated contours generated using several combinations of the parameters. The distance between natural and model-generated contours was measured in two ways by comparing: (1) plain or overt syllable to syllable duration and (2) relative change along both contours.We applied this methodology to read speech produced by five speakers of the state of Ceará (CE) and eight speakers of the state of São Paulo (SP). Mean w0 and alpha values are compatible with the view that Brazilian Portuguese is a mixed-rhythm language. Results from two bayesian hierarchical regression models do not suggest a difference between SP and CE speakers, but indicate a difference between the two methods, with the relative change method generating lower alpha values and higher w0 values, and the reverse for the plain duration method.

Sign in / Sign up

Export Citation Format

Share Document