An F-type test for detecting departure from monotonicity in a functional linear model

When studying associations between a functional covariate and scalar response using a functional linear model (FLM), scientific knowledge may indicate possible monotonicity of the unknown parameter curve. In this context, we propose an F-type test of monotonicity, based on a full versus reduced nested model structure, where the reduced model with monotonically constrained parameter curve is nested within an unconstrained FLM. For estimation under the unconstrained FLM, we consider two approaches: penalised least-squares and linear mixed model effects estimation. We use a smooth then monotonise approach to estimate the reduced model, within the null space of monotone parameter curves. A bootstrap procedure is used to simulate the null distribution of the test statistic. We present a simulation study of the power of the proposed test, and illustrate the test using data from a head and neck cancer study.     

Classical testing in functional linear models

We extend four tests common in classical regression – Wald, score, likelihood ratio and F tests – to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications.     

Testing an Integrative Contextual Career Development Model With Adolescents From High‐Poverty Urban Areas

Variations in the economic recovery rate across the United States have led to even greater chasms that separate the employed, unemployed, and underemployed (DeSilver, 2017 ). Therefore, understanding and supporting the career development of future generations is critical—especially for those who live outside the context of social privilege. The authors examined the applicability of the integrative contextual model of career development (ICM; Lapan, 2004 ) to a sample of 220 adolescents (129 boys, 91 girls) from a high‐poverty urban area. Results indicated that a canonical variate comprising foundational ICM skills (e.g., career exploration, goal setting) predicted a variate composed of ICM outcomes (e.g., self‐efficacy, vocational identity), thus supporting the usefulness of the ICM framework for this population. The skill of setting viable career goals was an especially strong predictor of outcomes. Implications for career counseling with adolescents living in high‐poverty urban areas and directions for future research are discussed.     

食品安全检测紧缺人才培养模式的实践与探索

以新时期高等教育理念为指导,围绕产、学、研、用四个环节,教学实践中探索出食品安全人才培养模式:紧密结合生产、生活实际,注重实践应用;建设完善食品安全相关特色课程体系,提高教学水平,培养实用人才;结合食品安全法从法律角度提升人才培养,使培养的人才同时具备科学性和法律的规范性;研究性教学、研究性实验,由培养实用人才迈向培养有研究能力、创新能力的人才。     

Two new estimators for the proportion of true null hypotheses in multiple test

In multiple hypothesis test, an important problem is estimating the proportion of true null hypotheses. Existing methods are mainly based on the p-values of the single tests. In this paper, we propose two new estimations for this proportion. One is a natural extension of the commonly used methods based on p-values and the other is based on a mixed distribution. Simulations show that the first method is comparable with existing methods and performs better under some cases. And the method based on a mixed distribution can get accurate estimators even if the variance of data is large or the difference between the null hypothesis and alternative hypothesis is very small.     

Gay and Bisexual Men’s Experiences Using Self-Testing Kits for HIV and Rectal and Urethral Bacterial Sexually Transmitted Infections: Lessons Learned from a Study With Home-Based Testing

Abstract

Objectives: This study investigated the experiences of gay, bisexual, and other men who have sex with men (GBM) conducting HIV and sexually transmitted infection (STI) self-testing procedures. Methods: We analyzed mixed-methods data from 11 GBM who self-tested HIV-positive and 1,070 HIV-negative GBM who completed the HIV self-testing and STI self-sampling procedures. Results: Nearly all (99%) reported ease in urine-based STI self-sampling and most (90%) found rectal swab self-collection easy. Most (94%) checked their rapid-HIV self-testing results during the correct window (20-40 minutes), and nearly all (99%) trusted their HIV results. Conclusions: Recommendations for future self-testing procedures are provided based on findings from free-response data.     

A statistical measure of internal consistency

A test for homogeneity of several populations against the simple tree alternative is proposed when the observations in various groups are subject to the same pattern of random right-censorship. The test is a generalization of the one proposed by Slivka (1970) which is useful when testing time is expensive so that an early termination of an experiment is desirable. The power of the test is examined in a simulation study.     

Specification and testing of multiplicative time-varying GARCH models with applications

In this article, we develop a specification technique for building multiplicative time-varying GARCH models of Amado and Teräsvirta (2008, 2013). The variance is decomposed into an unconditional and a conditional component such that the unconditional variance component is allowed to evolve smoothly over time. This nonstationary component is defined as a linear combination of logistic transition functions with time as the transition variable. The appropriate number of transition functions is determined by a sequence of specification tests. For that purpose, a coherent modelling strategy based on statistical inference is presented. It is heavily dependent on Lagrange multiplier type misspecification tests. The tests are easily implemented as they are entirely based on auxiliary regressions. Finite-sample properties of the strategy and tests are examined by simulation. The modelling strategy is illustrated in practice with two real examples: an empirical application to daily exchange rate returns and another one to daily coffee futures returns.