Hothorn, T., Hornik, K. & Zeileis, A. Unbiased recursive partitioning: a conditional inference framework.
Again we adopt the conditional inference approach and demonstrate the method through two educational economics datasets.
The results are discussed with respect to the differential cognitive demands of simple MMN and conditional inference, and the possible involvement of prefrontal cortex in implementing conditional inference in the MMN system.
Data of horses used for noncompetition purposes were analyzed with three statistical approaches: logistic regression, classification tree, and conditional inference tree methods.
Based on the test results, a conditional inference tree was estimated, which showed a higher level of RAP content or binder grade generally results in a higher |E∗|.
The hierarchical structure of the estimated conditional inference tree reveals the interplay between the two mix design variables (i.e., RAP content and binder grade) depending on the levels of loading frequency and temperature.
Analysis using conditional inference trees suggested that a combination of forest composition, topography, and dispersal predicted the distribution of MPB infestation on the landscape and that the importance of these variables changed as the outbreak developed.
The drivers of land use change were identified and apportioned using stochastic models based on unbiased recursive partitioning method embracing the conditional inference tree (CIT) and random forest (RF) with a focus on cropland and urban land.
This study was designed to explore "Conditional Inference" in the MMN system the capacity of the auditory system to use current input to switch between inference models in memory.
Our approach is conditional inference via selective inference with randomization, and fits into the general data analysis framework discussed in Chapter 2. We demonstrate the inference method through a development economics dataset and also a Mendelian randomization dataset with only summary statistics.
Conditional inference tree (ctree) models further quantified the significant factors contributing to the observed regional patterns: for distribution of multiflora rose, percentage of forest cover in the county (measuring human disturbance intensity) and stand density index; for distribution of common buckthorn, distance to major highways.
