scholarly journals Understanding the Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of 7 Randomised Experiments

2015 ◽  
Author(s):  
Rachael Meager
Keyword(s):  
Hierarchical Analysis ◽  
Bayesian Hierarchical ◽  
The Impact

scholarly journals Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments

2019 ◽  
Vol 11 (1) ◽  
pp. 57-91 ◽  
Author(s):  
Rachael Meager
Keyword(s):  
External Validity ◽  
Hierarchical Models ◽  
Hierarchical Analysis ◽  
Randomized Experiments ◽  
Average Effect ◽  
Bayesian Hierarchical ◽  
Heterogeneous Effects ◽  
Average Impact ◽  
The Impact ◽  
Better Than

Despite evidence from multiple randomized evaluations of microcredit, questions about external validity have impeded consensus on the results. I jointly estimate the average effect and the heterogeneity in effects across seven studies using Bayesian hierarchical models. I  find the impact on household business and consumption variables is unlikely to be transformative and may be negligible. I find reasonable external validity: true heterogeneity in effects is moderate, and approximately 60 percent of observed heterogeneity is sampling variation. Households with previous business experience have larger but more heterogeneous effects. Economic features of microcredit interventions predict variation in effects better than studies’ evaluation protocols. (JEL D14, G21, I38, O12, O16, P34, P36)

scholarly journals Measuring spatial allocative efficiency in basketball

2020 ◽  
Vol 16 (4) ◽  
pp. 271-289
Author(s):  
Nathan Sandholtz ◽  
Jacob Mortensen ◽  
Luke Bornn
Keyword(s):  
Spatial Distribution ◽  
Hierarchical Model ◽  
Opportunity Cost ◽  
Allocative Efficiency ◽  
Bayesian Hierarchical Model ◽  
National Basketball Association ◽  
Spatial Context ◽  
Bayesian Hierarchical ◽  
The Impact

AbstractEvery shot in basketball has an opportunity cost; one player’s shot eliminates all potential opportunities from their teammates for that play. For this reason, player-shot efficiency should ultimately be considered relative to the lineup. This aspect of efficiency—the optimal way to allocate shots within a lineup—is the focus of our paper. Allocative efficiency should be considered in a spatial context since the distribution of shot attempts within a lineup is highly dependent on court location. We propose a new metric for spatial allocative efficiency by comparing a player’s field goal percentage (FG%) to their field goal attempt (FGA) rate in context of both their four teammates on the court and the spatial distribution of their shots. Leveraging publicly available data provided by the National Basketball Association (NBA), we estimate player FG% at every location in the offensive half court using a Bayesian hierarchical model. Then, by ordering a lineup’s estimated FG%s and pairing these rankings with the lineup’s empirical FGA rate rankings, we detect areas where the lineup exhibits inefficient shot allocation. Lastly, we analyze the impact that sub-optimal shot allocation has on a team’s overall offensive potential, demonstrating that inefficient shot allocation correlates with reduced scoring.

scholarly journals Modelling the impact of the tier system on SARS-CoV-2 transmission in the UK between the first and second national lockdowns

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e050346
Author(s):  
Daniel J Laydon ◽  
Swapnil Mishra ◽  
Wes R Hinsley ◽  
Pantelis Samartsidis ◽  
Seth Flaxman ◽  
...  
Keyword(s):  
Real Time ◽  
Reproduction Number ◽  
Secondary Outcome ◽  
Bayesian Hierarchical ◽  
Time Reproduction ◽  
Tier System ◽  
The Uk ◽  
National Trends ◽  
Tier 3 ◽  
The Impact

ObjectiveTo measure the effects of the tier system on the COVID-19 pandemic in the UK between the first and second national lockdowns, before the emergence of the B.1.1.7 variant of concern.DesignThis is a modelling study combining estimates of real-time reproduction number Rt (derived from UK case, death and serological survey data) with publicly available data on regional non-pharmaceutical interventions. We fit a Bayesian hierarchical model with latent factors using these quantities to account for broader national trends in addition to subnational effects from tiers.SettingThe UK at lower tier local authority (LTLA) level. 310 LTLAs were included in the analysis.Primary and secondary outcome measuresReduction in real-time reproduction number Rt.ResultsNationally, transmission increased between July and late September, regional differences notwithstanding. Immediately prior to the introduction of the tier system, Rt averaged 1.3 (0.9–1.6) across LTLAs, but declined to an average of 1.1 (0.86–1.42) 2 weeks later. Decline in transmission was not solely attributable to tiers. Tier 1 had negligible effects. Tiers 2 and 3, respectively, reduced transmission by 6% (5%–7%) and 23% (21%–25%). 288 LTLAs (93%) would have begun to suppress their epidemics if every LTLA had gone into tier 3 by the second national lockdown, whereas only 90 (29%) did so in reality.ConclusionsThe relatively small effect sizes found in this analysis demonstrate that interventions at least as stringent as tier 3 are required to suppress transmission, especially considering more transmissible variants, at least until effective vaccination is widespread or much greater population immunity has amassed.

scholarly journals Statistical Modeling in Nonlinear Systems

2005 ◽  
Vol 18 (16) ◽  
pp. 3388-3399 ◽  
Author(s):  
Edward P. Campbell
Keyword(s):  
Nonlinear Dynamics ◽  
Statistical Models ◽  
Lorenz System ◽  
Prediction Models ◽  
Climate Data ◽  
Bayesian Hierarchical ◽  
Alternative Approaches ◽  
The Lorenz System ◽  
Bayesian Hierarchical Methods ◽  
The Impact

Abstract The use of linear statistical methods in building climate prediction models is examined, particularly the use of anomalies. The author’s perspective is that the climate system is a nonlinear interacting system, so the impact of modeling using anomalies rather than observed data directly is considered. With reference to the Lorenz system and a simple model for regime dependence, it is shown that anomalies impair our ability to reconstruct nonlinear dynamics. Some alternative approaches in the literature that offer an attractive way forward are explored, focusing on Bayesian hierarchical methods to construct so-called physical–statistical models. The author’s view is that anomalies should be reserved in most cases as a tool for enhancing graphical representations of climate data. The exceptions are when the implicit assumptions underlying the use of anomalies are met or when an anomaly representation is physically motivated.

Bayesian Hierarchical Analysis for Multiple Health Endpoints in a Toxicity Study

2010 ◽  
Vol 15 (3) ◽  
pp. 290-307 ◽  
Author(s):  
Taeryon Choi ◽  
Mark J. Schervish ◽  
Ketra A. Schmitt ◽  
Mitchell J. Small
Keyword(s):  
Toxicity Study ◽  
Hierarchical Analysis ◽  
Bayesian Hierarchical ◽  
Multiple Health

scholarly journals What’s in a Name: A Bayesian Hierarchical Analysis of the Name-Letter Effect

2012 ◽  
Vol 3 ◽  
Author(s):  
Oliver Dyjas ◽  
Raoul P. P. P. Grasman ◽  
Ruud Wetzels ◽  
Han L. J. van der Maas ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Hierarchical Analysis ◽  
Bayesian Hierarchical

scholarly journals P.051 Do clinical confounders to the neurological examination modify the diagnostic accuracy of CT-angiography for death by neurological criteria/brain death?

2021 ◽  
Vol 48 (s3) ◽  
pp. S33-S33
Author(s):  
J Neves Briard ◽  
R Nitulescu ◽  
É Lemoine ◽  
S English ◽  
L McIntyre ◽  
...  
Keyword(s):  
Systematic Review ◽  
Diagnostic Accuracy ◽  
Ct Angiography ◽  
Neurological Examination ◽  
Meta Analysis ◽  
Bayesian Hierarchical Model ◽  
Statistical Uncertainty ◽  
Bayesian Hierarchical ◽  
Ancillary Test ◽  
The Impact

Background: CT-angiography is an ancillary test used to diagnose death by neurological criteria (DNC), notably in cases of unreliable neurological examinations due to clinical confounders. We studied whether clinical confounders to the neurological examination modified CT-angiography diagnostic accuracy. Methods: Systematic review and meta-analysis of studies including deeply comatose patients undergoing DNC ancillary testing. We estimated pooled sensitivities and specificities using a Bayesian hierarchical model, including data on CT-angiography (4-point, 7-point, 10-point scales, and no intracranial flow), and performing a subgroup analysis on clinical confounders to the reference neurological examination. Results: Of 40 studies included in the meta-analysis, 7 involve CT-angiography (n=586). There was no difference between subgroups (Table). The degree of uncertainty involving sensitivity estimates was high in both subgroups. Conclusions: Statistical uncertainty in diagnostic accuracy estimates preclude any conclusion regarding the impact of clinical confounders on CT-angiography diagnostic accuracy. Further research is required to validate CT-angiography as an accurate ancillary test for DNC. Table. Pooled sensitivities and specificities of CT-angiography for death by neurological criteria

scholarly journals State-level tracking of COVID-19 in the United States

2020 ◽  
Author(s):  
H. Juliette T. Unwin ◽  
Swapnil Mishra ◽  
Valerie C. Bradley ◽  
Axel Gandy ◽  
Thomas A. Mellan ◽  
...  
Keyword(s):  
State Level ◽  
The United States ◽  
Bayesian Hierarchical ◽  
Secondary Infections ◽  
The Us ◽  
Control And Prevention ◽  
Credible Intervals ◽  
Behaviour Changes ◽  
The Impact ◽  
Number Of Individuals

AbstractAs of 1st June 2020, the US Centers for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly modelled the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We used changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. On 1st June, we estimated that Rt was only below one in 23 states. We also estimated that 3.7% [3.4%-4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals.

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