7,932 research outputs found
Combining multiple observational data sources to estimate causal effects
The era of big data has witnessed an increasing availability of multiple data
sources for statistical analyses. We consider estimation of causal effects
combining big main data with unmeasured confounders and smaller validation data
with supplementary information on these confounders. Under the unconfoundedness
assumption with completely observed confounders, the smaller validation data
allow for constructing consistent estimators for causal effects, but the big
main data can only give error-prone estimators in general. However, by
leveraging the information in the big main data in a principled way, we can
improve the estimation efficiencies yet preserve the consistencies of the
initial estimators based solely on the validation data. Our framework applies
to asymptotically normal estimators, including the commonly-used regression
imputation, weighting, and matching estimators, and does not require a correct
specification of the model relating the unmeasured confounders to the observed
variables. We also propose appropriate bootstrap procedures, which makes our
method straightforward to implement using software routines for existing
estimators
Asymptotic causal inference with observational studies trimmed by the estimated propensity scores
Causal inference with observational studies often relies on the assumptions
of unconfoundedness and overlap of covariate distributions in different
treatment groups. The overlap assumption is violated when some units have
propensity scores close to 0 or 1, and therefore both practical and theoretical
researchers suggest dropping units with extreme estimated propensity scores.
However, existing trimming methods ignore the uncertainty in this design stage
and restrict inference only to the trimmed sample, due to the non-smoothness of
the trimming. We propose a smooth weighting, which approximates the existing
sample trimming but has better asymptotic properties. An advantage of the new
smoothly weighted estimator is its asymptotic linearity, which ensures that the
bootstrap can be used to make inference for the target population,
incorporating uncertainty arising from both the design and analysis stages. We
also extend the theory to the average treatment effect on the treated,
suggesting trimming samples with estimated propensity scores close to 1.Comment: 21 pages, 1 figures and 3 table
Body image distortions and muscle dysmorphia symptoms among Asian men : do exercise status and type matter?
Theoretical Framework: Body image distortions and muscle dysmorphia symptoms were assessed among 78 Asian men who engaged in regular resistance training, aerobic training or did not engage in either. Method: Body fat and muscularity were measured and participants also completed the Muscle Dysmorphia Disorder Inventory. Results: Resistance trained men selected a body shape ideal that was higher in muscularity and lower in body fat. Aerobically trained men also reported higher perceived current Body Fat even though their actual levels were close to their ideal. Conclusion: The results suggest that specificity in body image distortion (e.g., perceived current-ideal versus perceived current-actual) when examining body image distortions might reduce conflicting findings in extant research
Detecting interactions between dark matter and photons at high energy colliders
We investigate the sensitivity to the effective operators describing
interactions between dark matter particles and photons at future high energy
colliders via the \gamma+ \slashed{E} channel. Such operators could
be useful to interpret the potential gamma-ray line signature observed by the
Fermi-LAT. We find that these operators can be further tested at
colliders by using either unpolarized or polarized beams. We also derive a
general unitarity condition for processes and apply it to the dark
matter production process .Comment: 13 pages, 8 figure
Global Water Vapor Estimates from Measurements from Active GPS RO Sensors and Passive Infrared and Microwave Sounders
Water vapor plays an important role in both climate change processes and atmospheric chemistry and photochemistry. Global water vapor vertical profile can be derived from satellite infrared and microwave sounders. However, no single remote sensing technique is capable of completely fulfilling the needs for numerical weather prediction, chemistry, and climate studies in terms of vertical resolution, spatial and temporal coverage, and accuracy. In addition to the passive infrared and microwave sounder observations, the active global positioning system (GPS) radio occultation (RO) technique can also provide all-weather temperature and moisture profiles. In this chapter, we describe the current developments of global water vapor vertical profile and total precipitable water derived from active GPS RO measurements. In addition, we also demonstrate the potential improvement of global water vapor estimates using combined active GPS RO and passive IR/MW particularly from Atmospheric InfraRed Sounder (AIRS) and Advanced Technology Microwave Sounder (ATMS) measurements. Results show that because RO data are very sensitive to water vapor variation in the moisture rich troposphere, the RO data are able to provide extra water vapor information for the combined AIRS/ATMS and RO retrievals in the lower troposphere
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