Estimating Semiparametric Panel Data Models by Marginal Integration

We propose a new methodology for estimating semiparametric panel data models, with a primary focus on the nonparametric component. We eliminate individual effects using first differencing transformation and estimate the unknown function by marginal integration. We extend our methodology to treat panel data models with both individual and time effects. And we characterize the asymptotic behavior of our estimators. Monte Carlo simulations show that our estimator behaves well in finite samples in both random effects and fixed effects settings.Semiparametric Panel Data Model, Partially Linear, First Differencing, Marginal Integration

Brachiopods from the Middle Devonian Hamilton Group of Southwestern Ontario, Canada

The late Middle Devonian (Givetian) Hamilton Group of southwestern Ontario is well known worldwide for its diverse, abundant and well-preserved brachiopod fauna. Yet, despite their undoubted value as a paleontological resource, the Hamilton Group brachiopods have been largely ignored over the past few decades in terms of their systematics and potential biostratigraphic value. A detailed systematic study of brachiopods collected from exposed strata of the Hamilton Group in the Arkona-Thedford-Ipperwash area of southwestern Ontario has led to the recognition of 50 species assigned to 34 genera, among which one species, Devonochonetes arkonensis n. sp., has been newly defined. A quantitative study of 38 brachiopod associations from the Hamilton Group of southwestern Ontario and equivalent strata in the Appalachian and the Michigan basins, using Q-mode (taxa as variables) and R-mode (localities/ages as variables) cluster analyses, principal component analysis, and four faunal similarity analyses (i.e., Jaccard similarity, Dice similarity, Simpson’s coefficient of similarity, and Raup-Crick similarity indices), has revealed that the late Middle Devonian (Givetian) brachiopod faunas from time-equivalent strata of the Michigan and Appalachian are distinct, al lowing identification of two separate faunal subprovinces. In spite of its present-day position in the southeastern part of the Michigan Basin, southwestern Ontario had brachiopod associations which were more similar to those in contemporaneous strata of the Appalachian Basin during the deposition of the lower and middle Hamilton sediments. This may have been related to the absence of a 111 topographie high in the present-day Findlay Arch area that would have otherwise prevented immigration of Appalachian Basin fauna into the study area. During the deposition of the upper Hamilton strata, the southwestern Ontario brachiopod associations developed a closer affinity to those of the Michigan Basin, suggesting that the immigration of brachiopod faunas from the Appalachian Basin into the study area was impeded. This change in faunal affinity may have been related to the formation of a local arch at present-day Cleveland area of Ohio that blocked the immigration of Appalachian Basin taxa into the study area

Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy

In medical research, it is common to collect information of multiple continuous biomarkers to improve the accuracy of diagnostic tests. Combining the measurements of these biomarkers into one single score is a popular practice to integrate the collected information, where the accuracy of the resultant diagnostic test is usually improved. To measure the accuracy of a diagnostic test, the Youden index has been widely used in literature. Various parametric and nonparametric methods have been proposed to linearly combine biomarkers so that the corresponding Youden index can be optimized. Yet there seems to be little justification of enforcing such a linear combination. This paper proposes a flexible approach that allows both linear and nonlinear combinations of biomarkers. The proposed approach formulates the problem in a large margin classification framework, where the combination function is embedded in a flexible reproducing kernel Hilbert space. Advantages of the proposed approach are demonstrated in a variety of simulated experiments as well as a real application to a liver disorder study

Reflections on the Use of Blood Activation and Stasis Removal in the Treatment of Lung Cancer

According to Chinese medicine, lung cancer is a malignant tumour with cough, haemoptysis, chest pain, fever and shortness of breath as its main manifestations. Its causes are: deficiency of positive qi, external invasion of evil toxins, internal accumulation of phlegm, stagnation of qi and blood, obstruction in the lung and loss of suction and descent of the lung. Blood stasis is the main cause of lung cancer, therefore, activating blood circulation and removing blood stasis is an important principle in the treatment of lung cancer. In recent years, the anti-cancer mechanism of traditional Chinese medicine and its extracts has gradually received academic attention, and the herbs that invigorate blood circulation and remove blood stasis have been widely used in clinical practice. This article introduces the mechanism of the action of Chinese medicine to invigorate blood circulation and remove blood stasis in the treatment of lung cancer in recent years, which provides a reference for clinical treatment and new drug development