Varimax rotation based on gradient projection needs between 10 and more than 500 random start loading matrices for optimal performance

Gradient projection rotation (GPR) is a promising method to rotate factor or component loadings by different criteria. Since the conditions for optimal performance of GPR-Varimax are widely unknown, this simulation study investigates GPR towards the Varimax criterion in principal component analysis. The conditions of the simulation study comprise two sample sizes (n = 100, n = 300), with orthogonal simple structure population models based on four numbers of components (3, 6, 9, 12), with- and without Kaiser-normalization, and six numbers of random start loading matrices for GPR-Varimax rotation (1, 10, 50, 100, 500, 1,000). GPR-Varimax rotation always performed better when at least 10 random matrices were used for start loadings instead of the identity matrix. GPR-Varimax worked better for a small number of components, larger (n = 300) as compared to smaller (n = 100) samples, and when loadings were Kaiser-normalized before rotation. To ensure optimal (stationary) performance of GPR-Varimax in recovering orthogonal simple structure, we recommend using at least 10 iterations of start loading matrices for the rotation of up to three components and 50 iterations for up to six components. For up to nine components, rotation should be based on a sample size of at least 300 cases, Kaiser-normalization, and more than 50 different start loading matrices. For more than nine components, GPR-Varimax rotation should be based on at least 300 cases, Kaiser-normalization, and at least 500 different start loading matrices.Comment: 19 pages, 8 figures, 2 tables, 4 figures in the Supplemen

Emerging technologies for physical weed control in row crops in Euro

Review of European physical and cultural weed control methods including new directions in research and technolog

Wake me up before you GO-GARCH

In this paper we present a new three-step approach to the estimation of Generalized Orthogonal GARCH (GO-GARCH) models, as proposed by van der Weide (2002). The approach only requires (non-linear) least-squares methods in combination with univariate GARCH estimation, and as such is computationally attractive, especially in largerdimensional systems, where a full likelihood optimization is often infeasible. The eï¬~@ectiveness of the method is investigated using Monte Carlo simulations as well as a number of empirical applications.

Carbon cage-like materials as potential low work function metallic compounds: Case of clathrates

We present an ab-initio calculation of the electronic affinity of the hypothetical C-46 clathrate by studying its bare and hydrogenated (100) surfaces. We show that such a system shares with the diamond phase a small electronic affinity. Further, contrary to the diamond phase, the possibility of doping endohedrally these cage-like systems allows to significantly raise the position of the Fermi level, resulting in a true metal with a small work function. This is illustrated in the case of the Li8@C-46 doped compound. Such a class of materials might be of much interest for the design of electron-emitting devices.Comment: 4 pages, 3 figures, RevTe