1,721,119 research outputs found
Construction and analysis of Es2 efficient supersaturated designs
No full textIn this paper, we construct supersaturated designs for large numbers of two-level factors and 10?n?22 runs by augmenting k-circulant designs [Liu, Y., Dean, A.M., 2004. k-circulant supersaturated designs. Technometrics 46, 32–43] with interaction columns or by deleting columns from k-circulant designs. Most of the designs presented have Es2 efficiencies above 0.90 and they extend the range of efficient supersaturated designs available in the literature.Difficulties encountered in the use of supersaturated designs in detecting active factors are addressed. We show that, when only one factor is active, the regression technique of forward selection is guaranteed to select the correct factor as active under the idealized conditions that non-active factors have negligible effects and the errors are small. Under similar conditions, we derive bounds on the maximum allowable correlation between the columns of the model matrix that guarantee the correct selection of the “most active” factor when two or more factors are non-negligible. Further, we obtain conditions for the correct selection of the two most active factors using subset selection in regression. A number of designs that satisfy these conditions are identified
J. N. Srivastava and experimental design
No full textJ. N. Srivastava was a tremendously productive statistical researcher for five decades. He made significant contributions in many areas of statistics, including multivariate analysis and sampling theory. A constant throughout his career was the attention he gave to problems in discrete experimental design, where many of his best known publications are found. This paper focuses on his design work, tracing its progression, recounting his key contributions and ideas, and assessing its continuing impact. A synopsis of his design-related editorial and organizational roles is also included
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Two-stage sensitivity-based group screening in computer experiments
No full textSophisticated computer codes that implement mathematical models of physical processes can involve large numbers of inputs, and screening to determine the most active inputs is critical for understanding the input- output relationship. This article presents a new two-stage group screening methodology for identifying active inputs. In Stage 1, groups of inputs showing low activity are screened out; in Stage 2, individual inputs from the active groups are identified. Inputs are evaluated through their estimated total (effect) sensitivity indices (TSIs), which are compared with a benchmark null TSI distribution created from added low noise inputs. Examples show that, compared with other procedures, the proposed method provides more consistent and accurate results for high-dimensional screening. Additional details and computer code are provided in supplementary materials available online
Existence and construction of randomization defining contrast subspaces for factorial designs
No full textRegular factorial designs with randomization restrictions are widely used in practice. This paper provides a unified approach to the construction of such designs using randomization defining contrast subspaces for the representation of randomization restrictions. We use finite projective geometry to determine the existence of designs with the required structure and develop a systematic approach for their construction. An attractive feature is that commonly used factorial designs with randomization restrictions are special cases of this general representation. Issues related to the use of these designs for particular factorial experiments are also addressed
Non-collapsing space-filling designs for bounded nonrectangular regions
No full textMany researchers use computer simulators as experimental tools, especially when physical experiments are infeasible. When computer codes are computationally intensive, nonparametric predictors can be fitted to training data for detailed exploration of the input–output relationship. The accuracy of such flexible predictors is enhanced by taking training inputs to be “space-filling.” If there are inputs that have little or no effect on the response, it is desirable that the design be “noncollapsing” in the sense of having space-filling lower dimensional projections. This article describes an algorithm for constructing noncollapsing space-filling designs for bounded input regions that are of possibly high dimension. Online supplementary materials provide the code for the algorithm, examples of its use, and show its performance in multiple setting
Estimating sensitivity indices based on Gaussian process metamodels with compactly supported correlation functions
No full textSpecific formulae are derived for quadrature-based estimators of global sensitivity indices when the unknown function can be modeled by a regression plus stationary Gaussian process using the Gaussian, Bohman, or cubic correlation functions. Estimation formulae are derived for the computation of process-based Bayesian and empirical Bayesian estimates of global sensitivity indices when the observed data are the function values corrupted by noise. It is shown how to restrict the parameter space for the compactly supported Bohman and cubic correlation functions so that (at least) a given proportion of the training data correlation entries are zero. This feature is important in the situation where the set of training data is large. The estimation methods are illustrated and compared via examples
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