The first model estimates the treatment effect using all covariates identified to adjust the selection bias.
Propensity score matching aims to adjust the selection bias in nonexperimental, nonrandomized, and retrospective observational studies.
We developed a novel pixel-based compositing technique that dynamically adjusts the selection process to the underlying land surface phenology (LSP) of each pixel.
In spite of the selection methods, diversity shown in [23], and the use of five information variables for the decision making, this algorithm does not adjust the selection according to a particular application, for instance, BE or RT.
Moreover, a competition mechanism is proposed to adaptively adjust the selection rates of the operators, and some knowledge-based local search operators are developed to enhance the exploitation ability of the CMA.
Our findings underscore the importance of finely adjusting the selection of empiric antimicrobial therapy to the local epidemiological conditions.
Our method is controlled by a single parameter that influences the number of computed EMs, by adjusting the selection probability during the execution of the algorithm.
After controlling the difference of hospitals' characteristics in designing and using PSM to partially adjust the selection bias of doctors' characteristics, we demonstrated that public reporting of medicine information could decrease the percentage of prescriptions requiring injections in township hospitals of China.
Here we introduce the projection operator π W : L 2 → W which, for any function g ∈ L 2, denotes the orthogonal projection onto W, i.e. (6) (g - π W (g ), q ) ≔ ∫ Ω [ g - π W (g ) ] qdV ≔ 0, ∀ q ∈ W. In this way, we may elect to represent g coarsely or finely by adjusting the selection of the space W (as we will discuss further in the following sections).
