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This study extends power formulas proposed by Schochet (2008) assuming that the cluster-level score variable follows quadratic functional form. Results reveal that we need not be concerned with treatment by linear term interaction, and polynomial degree up to second order for symmetric truncation intervals. In comparison, every slight change in the functional form alters sample size requirements for asymmetric truncation intervals. Finally, an empirical framework beyond quadratic functional form is provided when the asymptotic variance of the treatment effect is untraceable. In this case, the CRD design effect is either computed from moments of the sample or approximate population moments via simulation. Formulas for quadratic functional form and the extended empirical framework are implemented in the cosa R package and companion Shiny web application.
This paper presents tests to formally choose between regression models using different derivatives of a functional covariate in scalar-on-function regression. We demonstrate that for linear regression, models using different derivatives can be nested
One of the most popular methodologies for estimating the average treatment effect at the threshold in a regression discontinuity design is local linear regression (LLR), which places larger weight on units closer to the threshold. We propose a Gaussi
Space-filling designs are important in computer experiments, which are critical for building a cheap surrogate model that adequately approximates an expensive computer code. Many design construction techniques in the existing literature are only appl
Massive data bring the big challenges of memory and computation for analysis. These challenges can be tackled by taking subsamples from the full data as a surrogate. For functional data, it is common to collect multiple measurements over their domain
Quadratic regression goes beyond the linear model by simultaneously including main effects and interactions between the covariates. The problem of interaction estimation in high dimensional quadratic regression has received extensive attention in the