THE IMPLICATIONS OF CHOOSING "NO RACE" ON THE SALIENCE OF HISPANIC IDENTITY: How Racial and Ethnic Backgrounds Intersect among Hispanic Adolescents

Using data from the National Longitudinal Study of Adolescent Health (Add Health), a nationally representative sample of youth from 7th to 12th grades, we examined how racial and ethnic identification overlap among Hispanic adolescents. We evaluated the relative proximity between race and ethnic identifiers among Hispanics. Empirical analyses suggest that the racial identification of other students at school has a significant impact on the odds of choosing particular racial identifiers. Both Hispanic and non-Hispanic schoolmates' racial identification is related to the racial identification of Hispanic adolescents. We also find evidence that both ethnicity and race are distinct stratifiers among Hispanics. Overall, our findings support the notion that Hispanic may be a more meaningful "racial" identity than black, white, or other, but we also find that racial identification and ethnic background are still important and meaningful dividers among Hispanic youth.     

Analysis of supersaturated designs via the Dantzig selector

A supersaturated design is a design whose run size is not enough for estimating all the main effects. It is commonly used in screening experiments, where the goals are to identify sparse and dominant active factors with low cost. In this paper, we study a variable selection method via the Dantzig selector, proposed by Candes and Tao [2007. The Dantzig selector: statistical estimation when p$p$ is much larger than n$n$. Annals of Statistics 35, 2313–2351], to screen important effects. A graphical procedure and an automated procedure are suggested to accompany with the method. Simulation shows that this method performs well compared to existing methods in the literature and is more efficient at estimating the model size.     

论高校体育课程目标

运用问卷调查、专家访问、文献资料研究和分析归纳等方法,探讨21世纪我国高校体育课程目标的定位以及实现课程目标的具体措施.     

Helping family members manage medication administration hassles

Managing the medication regimens of dependent older adults in the home is a complex issue for family caregivers. The tasks involved can include helping the older adults manage their own medications or may require involvement in the purchasing, scheduling, and administering of multiple medications for serious conditions. The hassles associated with this responsibility are now known to be associated with increased caregiver strain. In this article, we provide an overview of the body of research on medication administration hassles and offer a tool to assess family caregivers who are involved in this important aspect of care.     

The Dynamic Model for Cancer Relapse Based on Two-Stage Model of Carcinogenesis

In this article, the time from the start of chemotherapy randomization until cancer relapse is of primary interest. Here, cancer relapse refers to the appearance of the first observable malignant clone after therapy. A dynamic model for cancer relapse after chemotherapy is developed. The model differs from the traditional cure rate models in that it takes into consideration the growth kinetics of malignant tumors using a two-stage carcinogenesis model. The survival and hazard functions for cancer relapse time are derived, and a simulation study is performed to validate the underlying model.     

Wavelet‐Based Testing for Serial Correlation of Unknown Form in Panel Models

Wavelet analysis is a new mathematical method developed as a unified field of science over the last decade or so. As a spatially adaptive analytic tool, wavelets are useful for capturing serial correlation where the spectrum has peaks or kinks, as can arise from persistent dependence, seasonality, and other kinds of periodicity. This paper proposes a new class of generally applicable wavelet‐based tests for serial correlation of unknown form in the estimated residuals of a panel regression model, where error components can be one‐way or two‐way, individual and time effects can be fixed or random, and regressors may contain lagged dependent variables or deterministic/stochastic trending variables. Our tests are applicable to unbalanced heterogenous panel data. They have a convenient null limit N(0,1) distribution. No formulation of an alternative model is required, and our tests are consistent against serial correlation of unknown form even in the presence of substantial inhomogeneity in serial correlation across individuals. This is in contrast to existing serial correlation tests for panel models, which ignore inhomogeneity in serial correlation across individuals by assuming a common alternative, and thus have no power against the alternatives where the average of serial correlations among individuals is close to zero. We propose and justify a data‐driven method to choose the smoothing parameter—the finest scale in wavelet spectral estimation, making the tests completely operational in practice. The data‐driven finest scale automatically converges to zero under the null hypothesis of no serial correlation and diverges to infinity as the sample size increases under the alternative, ensuring the consistency of our tests. Simulation shows that our tests perform well in small and finite samples relative to some existing tests.     

Predicting project approvals: A case of grants from the National Science Council of Taiwan

Project selection is an important task for organizations in achieving their missions using limited budgets and resources. Whether or not a project will be approved is also of primary concern to the applicants. This paper predicts whether a project will be approved for cases where the criteria for evaluating it are known while the scoring system is not. The idea is to construct a frontier function for the approved projects from past performance on the criteria. The relative distance between a proposed project and the frontier serves as an indicator of the possibility that the project will be approved. Data from the Management II Division of the National Science Council of Taiwan in the Topic Research Project are collected to illustrate this approach. From the percentile of the distance measure, an applicant is able to predict the possibility that their project will be approved. Since professors with different levels of experience and different research areas have different research performance, these factors are taken into account in the prediction. A Malmquist productivity index analysis is also conducted to investigate the performance improvement of the applicants in research between two periods.