Tests for High Dimensional Generalized Linear Models

We consider testing regression coefficients in high dimensional generalized linear models. An investigation of the test of Goeman et al. (2011) is conducted, which reveals that if the inverse of the link function is unbounded, the high dimensionality in the covariates can impose adverse impacts on the power of the test. We propose a test formation which can avoid the adverse impact of the high dimensionality. When the inverse of the link function is bounded such as the logistic or probit regression, the proposed test is as good as Goeman et al. (2011)'s test. The proposed tests provide p-values for testing significance for gene-sets as demonstrated in a case study on an acute lymphoblastic leukemia dataset.Comment: The research paper was stole by someone last November and illegally submitted to arXiv by a person named gong zi jiang nan. We have asked arXiv to withdraw the unfinished paper [arXiv:1311.4043] and it was removed last December. We have collected enough evidences to identify the person and Peking University has begun to investigate the plagiarize

Schwinger boson mean field theory of the Heisenberg Ferrimagnetic Spin Chain

The Schwinger boson mean field theory is applied to the quantum ferrimagnetic Heisenberg chain. There is a ferrimagnetic long range order in the ground state. We observe two branches of the low lying excitation and calculate the spin reduction, the gap of the antiferromagnetic branch, and the spin fluctuation at $T=0K$. These results agree with the established numerical results quite well. At finite temperatures, the long range order is destroyed because of the disappearance of the Bose condensation. The thermodynamic observables, such as the free energy, magnetic susceptibility, specific heat, and the spin correlation at $T>0K$, are calculated. The $T\chi_{uni}$ has a minimum at intermediate temperatures and the spin correlation length behaves as $T^{-1}$ at low temperatures. These qualitatively agree with the numerical results and the difference is small at low temperatures.Comment: 15 pages, 5 figures. Accepted by Phys. Rev.

Do Consumers Really Care about Genetically Modified (GM) Food Label? What Do We Know? What Else Should We Know?

This paper employs household survey data to examine whether GM food labeling has an impact on consumers’ vegetable oil purchasing decision. Direct variables indicating consumers’ response to label regulation are employed to test labeling effect. We find that supermarket customers who concern GM label or GM material have respectively 4.1-7.5 percent and 9.8-12.3 percent lower probability of buying GM oil. Meanwhile, their probability of switching from GM oil to non-GM oil after labeling enforcement is higher by 10.5 percent and 12.7 percent respectively. The empirical results support our previous finding that in the short run the market share of GM oil decreased significantly by a small amount as a result of label enforcement. To capture a comprehensive picture of GM food labeling and the market trend in the long run, major concerns and needs for the future are discussed, including understanding the discrepancy between stated preferences and revealed preferences, influencing factors for aggregate market share incorporating other market channels, real decision body for food consumption, and other major marketing strategies.GM food labeling, household survey data, aggregate market share, China.