Semi-Parametric Maximum Likelihood Estimates for ROC Curves of Continuous-Scale Tests

In this paper, we propose a semi-parametric maximum likelihood estimate of an ROC curve that satisfies the property of invariance of the ROC curve. In our simulation studies, we demonstrate that the proposed estimator has the best performance among all the existing semi-parametric estimators considered here. Finally, we illustrate the application of the proposed estimator using a real data set

Solar system tests for realistic f(T)f(T) models with nonminimal torsion-matter coupling

In the previous paper, we have constructed two $f(T)$ models with nonminimal torsion-matter coupling extension, which are successful in describing the evolution history of the Universe including the radiation-dominated era, the matter-dominated era, and the present accelerating expansion. Meantime, the significant advantage of these models is that they could avoid the cosmological constant problem of $\Lambda$CDM. However, the nonminimal coupling between matter and torsion will affect the tests of Solar system. In this paper, we study the effects of Solar system in these models, including the gravitation redshift, geodetic effect and perihelion preccesion. We find that Model I can pass all three of the Solar system tests. For Model II, the parameter is constrained by the measure of the perihelion precession of Mercury.Comment: 10 page

Semi-Parametric Maximum Likelihood Estimates for ROC Curves of Continuous-Scale Tests

In this paper, we propose a new semi-parametric maximum likelihood (ML) estimate of an ROC curve that satisfies the property of invariance of the ROC curve and is easy to compute. We show that our new estimator is [Formula: see text]-consistent and has an asymptotically normal distribution. Our extensive simulation studies show the proposed method is efficient, robust, and simple to compute. Finally, we illustrate the application of the proposed estimator in a real data set

A Semi-Parametric Two-Part Mixed-Effects Heteroscedastic Transformation Model for Correlated Right-Skewed Semi-Continuous Data

In longitudinal or hierarchical structure studies, we often encounter a semi-continuous variable that has a certain proportion of a single value and a continuous and skewed distribution among the rest of values. In the paper, we propose a new semi-parametric two-part mixed-effects transformation model to fit correlated skewed semi-continuous data. In our model, we allow the transformation to be non-parametric. Fitting the proposed model faces computational challenges due to intractable numerical integrations. We derive the estimates for the parameter and the transformation function based on an approximate likelihood, which has high order accuracy but less computational burden. We also propose an estimator for the expected value of the semi-continuous outcome on the original-scale. Finally, we apply the proposed methods to a clinical study on effectiveness of a collaborative care treatment on late life depression on health care costs

Semaphorin 4D Promotes Skeletal Metastasis in Breast Cancer.

Bone density is controlled by interactions between osteoclasts, which resorb bone, and osteoblasts, which deposit it. The semaphorins and their receptors, the plexins, originally shown to function in the immune system and to provide chemotactic cues for axon guidance, are now known to play a role in this process as well. Emerging data have identified Semaphorin 4D (Sema4D) as a product of osteoclasts acting through its receptor Plexin-B1 on osteoblasts to inhibit their function, tipping the balance of bone homeostasis in favor of resorption. Breast cancers and other epithelial malignancies overexpress Sema4D, so we theorized that tumor cells could be exploiting this pathway to establish lytic skeletal metastases. Here, we use measurements of osteoblast and osteoclast differentiation and function in vitro and a mouse model of skeletal metastasis to demonstrate that both soluble Sema4D and protein produced by the breast cancer cell line MDA-MB-231 inhibits differentiation of MC3T3 cells, an osteoblast cell line, and their ability to form mineralized tissues, while Sema4D-mediated induction of IL-8 and LIX/CXCL5, the murine homologue of IL-8, increases osteoclast numbers and activity. We also observe a decrease in the number of bone metastases in mice injected with MDA-MB-231 cells when Sema4D is silenced by RNA interference. These results are significant because treatments directed at suppression of skeletal metastases in bone-homing malignancies usually work by arresting bone remodeling, potentially leading to skeletal fragility, a significant problem in patient management. Targeting Sema4D in these cancers would not affect bone remodeling and therefore could elicit an improved therapeutic result without the debilitating side effects