Two-Step Residual-Based Estimation of Error Variances for Generalized Least Squares in Split-Plot Experiments

In split-plot experiments, estimation of unknown parameters by generalized least squares (GLS), as opposed to ordinary least squares (OLS), is required, owing to the existence of whole- and subplot errors. However, estimating the error variances is often necessary for GLS. Restricted maximum likelihood (REML) is an established method for estimating the error variances, and its benefits have been highlighted in many previous studies. This article proposes a new two-step residual-based approach for estimating error variances. Results of numerical simulations indicate that the proposed method performs sufficiently well to be considered as a suitable alternative to REML.     

“传染病”视角下“××门”扩散的模型研究

近年来,由于英汉两种语言的接触而产生的词项借用“××门”不断在新闻媒体和网络上涌现,并以惊人的速度扩散.文章统计了2005-2010年“××门”在中国网络媒体上的使用频率,基于模因论理论,同时借鉴生物传染病的数学模型研究了“××门”的扩散规律,建立了一个简单的“××门”扩散的数学模型,以期对语言接触、扩散与变异等语言动态变化规律研究提供理论分析依据.     

计数公式与时钟夹角公式在中学解题中的应用

本文应用计数公式n(n-1)/2和时钟夹角公式|m×30°-5.5°n|解决了两个元素确定一个图形或组合(握手、比赛等)的计数问题和与时针和分针夹角的相关数学问题。     

Mixed two- and four-level fractional factorial split-plot designs with clear effects

Mixed-level designs have become widely used in the practical experiments. When the levels of some factors are difficult to be changed or controlled, fractional factorial split-plot (FFSP) designs are often used. It is highly to know when a mixed-level FFSP design with resolution III or IV has clear effects. This paper investigates the conditions of a resolution III or IV FFSP design with both two-level and four-level factors to have various clear factorial effects, including two types of main effects and three types of two-factor interaction components. The structures of such designs are shown and illustrated with examples.     

Modifying a central composite design to model the process mean and variance when there are hard-to-change factors

Summary.  An important question within industrial statistics is how to find operating conditions that achieve some goal for the mean of a characteristic of interest while simultaneously minimizing the characteristic's process variance. Often, people refer to this kind of situation as the robust parameter design problem. The robust parameter design literature is rich with ways to create separate models for the mean and variance from this type of experiment. Many times time and/or cost constraints force certain factors of interest to be much more difficult to change than others. An appropriate approach to such an experiment restricts the randomization, which leads to a split-plot structure. The paper modifies the central composite design to allow the estimation of separate models for the characteristic's mean and variances under a split-plot structure. The paper goes on to discuss an appropriate analysis of the experimental results. It illustrates the methodology with an industrial experiment involving a chemical vapour deposition process for the manufacture of silicon wafers. The methodology was used to achieve a silicon layer thickness value of 485 Å while minimizing the process variation.     

A candidate-set-free algorithm for generating D-optimal split-plot designs

Summary.  We introduce a new method for generating optimal split-plot designs. These designs are optimal in the sense that they are efficient for estimating the fixed effects of the statistical model that is appropriate given the split-plot design structure. One advantage of the method is that it does not require the prior specification of a candidate set. This makes the production of split-plot designs computationally feasible in situations where the candidate set is too large to be tractable. The method allows for flexible choice of the sample size and supports inclusion of both continuous and categorical factors. The model can be any linear regression model and may include arbitrary polynomial terms in the continuous factors and interaction terms of any order. We demonstrate the usefulness of this flexibility with a 100-run polypropylene experiment involving 11 factors where we found a design that is substantially more efficient than designs that are produced by using other approaches.     

Analytical Characterization of the Information Matrix for Split-Plot Designs

This article provides the analytical characterization of the inverse of the information matrix for second-order SPD. A particular feature of these explicit expressions is that they are functions of the design parameters enabling the development of analytical functions to efficiently compute exact design optimality criteria. The application of these analytical expressions is demonstrated using the generalized variance of the parameter estimates for second-order SPD. An example illustrating the use of these expressions is also presented.     

Whole-Plot Exchange Algorithms for Constructing D-Optimal Multistratum Designs

Multistratum experiments contain several different sizes of experimental units. Examples include split-plot, strip-plot designs, and randomized block designs. We propose a strategy for constructing a D-optimal multistratum design by improving a randomly generated design through a sequence of whole-plot exchanges. This approach preserves the design structure and simplifies updates to the information and is applicable to any multistratum design where the largest-sized experimental unit is either a whole plot or a block. Two whole-plot exchange algorithms inspired by the point-exchange strategies of Fedorov (1972 Fedorov , V. V. ( 1972 ). Theory of Optimal Experiments . New York : Academic Press . [Google Scholar]) and Wynn (1972 Wynn , H. P. ( 1972 ). Results in the theory and construction of D-optimum experimental designs . Journal of the Royal Statistical Society Series B Statistics Methodology 34 : 133 – 147 . [Google Scholar]) are described. The application of the algorithms to several design problems is discussed.     

Maximum and Minimum Prediction Variance for Spherical and Cuboidal Split Plot Designs

The impact of restricted randomization on the information matrix has created challenges for the computation of design optimality criteria. This article focuses on the computation of the maximum and minimum prediction variance for Central Composite (CCD) and Box–Behnken (BBD) split plot designs (SPD). The approach is to analytically determine the exact maximum and minimum prediction variance for both spherical and cuboidal second-order SPD. A particular feature of these analytical functions is that they are functions of the design parameters. Finally, the application of these analytical functions is demonstrated for a CCD SPD.     

Some Results on Fractional Factorial Split-Plot Designs with Multi-Level Factors

Fractional factorial split-plot (FFSP) designs have received much attention in recent years. In this article, the matrix representation for FFSP designs with multi-level factors is first developed, which is an extension of the one proposed by Bingham and Sitter (1999b Bingham , D. , Sitter , R. R. ( 1999b ). Some theoretical results for fractional factorial split-plot designs . Ann. Statist. 27 : 1240 – 1255 . [Google Scholar]) for the two-level case. Based on this representation, periodicity results of maximum resolution and minimum aberration for such designs are derived. Differences between FFSP designs with multi-level factors and those with two-level factors are highlighted.