It is very powerful for constructing nearly saturated factorial designs to characterize fractional factorial (FF) designs through their consulting designs when the consulting designs are small. Mukerjee and Fang employed the projective geometry theory to find the secondary wordlength pattern of a regular symmetrical fractional factorial split-plot (FFSP) design in terms of its complementary subset, but not in a unified form. In this paper, based on the connection between factorial design theory and coding theory, we obtain some general and unified combinatorial identities that relate the secondary wordlength pattern of a regular symmetrical or mixed-level FFSP design to that of its consulting design. According to these identities, we further establish some general and unified rules for identifying minimum secondary aberration, symmetrical or mixed-level, FFSP designs through their consulting designs.
Fractional factorial split-plot (FFSP) designs have an important value of investigation for their special structures.There are two types of factors in an FFSP design: the whole-plot (WP) factors and sub-plot (SP) factors,which can form three types of two-factor interactions:WP2fi,WS2fi and SP2fi.This paper considers FFSP designs with resolution Ⅲ or Ⅳ under the clear effects criterion.It derives the upper and lower bounds on the maximum numbers of clear WP2fis and WS2fis for FFSP designs,and gives some methods for constructing the desired FFSP designs.It further examines the performance of the construction methods.
Quasi-regression, motivated by the problems arising in the computer experiments, focuses mainly on speeding up evaluation. However, its theoretical properties are unexplored systemically. This paper shows that quasi-regression is unbiased, strong convergent and asymptotic normal for parameter estimations but it is biased for the fitting of curve. Furthermore, a new method called unbiased quasi-regression is proposed. In addition to retaining the above asymptotic behaviors of parameter estimations, unbiased quasi-regression is unbiased for the fitting of curve.
