Add to ORKGThis manuscript investigates two different approaches, namely the Neymanian randomization based (Neyman, 1923) method and the Bayesian model based (Rubin, 1978) method, towards the causal inference for 2-by-2 split-plot designs (Jones and Nachtsheim, 2009), both under the potential outcomes framework (Neyman, 1923; Rubin, 1974, 1978, 2005). Chapter 1 -- Chapter 5. Given two 2-level factors of interest, a 2-by-2 split-plot design (a) takes each of the 2-by-2 = 4 possible factorial combinations as a treatment, (b) identifies one factor as 'whole-plot,' (c) divides the experimental units into blocks, and (d) assigns the treatments in such a way that all units within the same block receive the same level of the whole-plot factor. Assuming the ...
Over the past decade, there have been rapid advances in the development of methods for the design an...
textabstractIndustrial experiments often involve factors that are hard to change or costly to manipu...
A split-plot data structure is usually modelled by a linear classificatory model with a 0,1 model ma...
Under the potential outcomes framework, we propose a randomization based estimation procedure for ca...
In many experimental settings, some experimental factors are very hard to change or very expensive t...
Split-plot design may be refer to a common experimental setting where a particular type of restricte...
Many industrial response surface experiments are deliberately not conducted in a completely randomiz...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73673/1/j.1467-9876.2003.05029.x.pd
Within a general linear model framework, optimality of OLS estimators may be obtained if and only if...
The fundamental principles of experiment design are factorization, replication, randomization, and l...
We introduce a new method for generating optimal split-plot designs. These designs are optimal in th...
In an increasingly common class of studies, the goal is to evaluate causal effects of treatments tha...
The paper shows, how incomplete split-plot designs can be constructed from -designs and how they can...
This manuscript includes three topics in causal inference, all of which are under the randomization ...
Binomial data are often generated in split-plot experimental designs in agricultural, biological, an...
Over the past decade, there have been rapid advances in the development of methods for the design an...
textabstractIndustrial experiments often involve factors that are hard to change or costly to manipu...
A split-plot data structure is usually modelled by a linear classificatory model with a 0,1 model ma...
Under the potential outcomes framework, we propose a randomization based estimation procedure for ca...
In many experimental settings, some experimental factors are very hard to change or very expensive t...
Split-plot design may be refer to a common experimental setting where a particular type of restricte...
Many industrial response surface experiments are deliberately not conducted in a completely randomiz...
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73673/1/j.1467-9876.2003.05029.x.pd
Within a general linear model framework, optimality of OLS estimators may be obtained if and only if...
The fundamental principles of experiment design are factorization, replication, randomization, and l...
We introduce a new method for generating optimal split-plot designs. These designs are optimal in th...
In an increasingly common class of studies, the goal is to evaluate causal effects of treatments tha...
The paper shows, how incomplete split-plot designs can be constructed from -designs and how they can...
This manuscript includes three topics in causal inference, all of which are under the randomization ...
Binomial data are often generated in split-plot experimental designs in agricultural, biological, an...
Over the past decade, there have been rapid advances in the development of methods for the design an...
textabstractIndustrial experiments often involve factors that are hard to change or costly to manipu...
A split-plot data structure is usually modelled by a linear classificatory model with a 0,1 model ma...