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Exposure to BTEX has been suggested to be a potential risk factor for asthma,[[1], [3]] a heterogeneous disease with unclear etiology, but its individual risk for adult asthma and its phenotypic clusters remains unclear. An hourly grid-scale model was utilized to estimate the residence-based ambient air BTEX levels and the time-lag effect (lag-0, lag-1, lag-2, lag-3, lag-4, and lag-5) on the daily asthma ERVs and outpatient visits. Abbreviations 2,4-DPMA N -acetyl- S -(2,4-dimethylbenzene)- l -cysteine 4-HNE 4-hydroxynonenal BMA N -acetyl- S -(benzyl)- l -cysteine BTEX benzene, toluene, ethylbenzene, and xylene C1P ceramide-1-phosphate ERV emergency room visit HEL N -(hexanoyl)-lysine LC-MS liquid chromatography-tandem mass spectrometry NHIRD National Health Insurance Research Database PGA phenylglyoxylic acid PMA N -acetyl- S -phenyl- l -cysteine RR relative risk S1P sphingosine-1-phosphate SL sphingolipid VOCs volatile organic compounds In a combined population-based and case-control study, exposure to benzene, toluene, ethylbenzene, and xylene (BTEX), particularly benzene, concomitant with increased oxidative stress and sphingolipid dysregulation, posed a significant, but differential, risk for current asthma, its severity, and phenotypic clusters. [Extracted from the article]

Motivated by two examples concerning global warming and monthly total import and export by China, we study time series that contain a nonparametric periodic component with an unknown period, a nonparametric trending behavior and also additive covariate effects. Further, as the amplitude function may change at some known or unknown change-point(s), we extend our model to take this dynamical periodicity into account and introduce two change-point estimators. To the best of knowledge, this is the first work to study such complex periodic structure. A two-step estimation procedure is proposed to estimate accurately the periodicity, trend and covariate effects. First, we estimate the period with the trend and covariate effects being approximated by B-splines rather than being ignored. To achieve robustness we employ a penalized M-estimation method which uses post model selection inference ideas. Next, given the period estimate, we estimate the amplitude, trend and covariate effects. Asymptotic properties of our estimators are derived, including consistency of the period estimator and asymptotic normality and oracle property of the estimated periodic sequence, trend and covariate effects. Simulation studies confirm superiority of our method and illustrate good performance of our change-point estimators. Applications to the two motivating examples demonstrate utilities of our methods. [ABSTRACT FROM AUTHOR]

Testing the equality of two means is a fundamental inference problem. For high-dimensional data, the Hotelling's T2-test either performs poorly or becomes inapplicable. Several modifications have been proposed to address this issue. However, most of them are based on asymptotic normality of the null distributions of their test statistics which inevitably requires strong assumptions on the covariance. We study this problem thoroughly and propose an L2-norm based test that works under mild conditions and even when there are fewer observations than the dimension. Specially, to cope with general nonnormality of the null distribution we employ the Welch–Satterthwaite χ2-approximation. We derive a sharp upper bound on the approximation error and use it to justify that χ2-approximation is preferred to normal approximation. Simple ratio-consistent estimators for the parameters in the χ2-approximation are given. Importantly, our test can cope with singularity or near singularity of the covariance which is commonly seen in high dimensions and is the main cause of nonnormality. The power of the proposed test is also investigated. Extensive simulation studies and an application show that our test is at least comparable to and often outperforms several competitors in terms of size control, and the powers are comparable when their sizes are. for this article are available online. [ABSTRACT FROM AUTHOR]

Summary: In the ultra‐high dimensional setting, two popular variable screening methods with the desirable sure screening property are sure independence screening (SIS) and forward regression (FR). Both are classical variable screening methods, and recently have attracted greater attention under high‐dimensional data analysis. We consider a new and simple screening method that incorporates multiple predictors at each step of forward regression, with decisions on which variables to incorporate based on the same criterion. If only one step is carried out, the new procedure reduces to SIS. Thus it can be regarded as a generalisation and unification of FR and SIS. More importantly, it preserves the sure screening property and has computational complexity similar to FR at each step, yet it can discover the relevant covariates in fewer steps. Thus it reduces the computational burden of FR drastically while retaining the advantages of the latter over SIS. Furthermore, we show that it can find all the true variables if the number of steps taken is the same as the correct model size, which is a new theoretical result even for the original FR. An extensive simulation study and application to two real data examples demonstrate excellent performance of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: We develop a variance reduction method for the seemingly unrelated (SUR) kernel estimator of . We show that the quadratic interpolation method introduced in works for the SUR kernel estimator. For a given point of estimation, define a variance reduced local linear estimate as a linear combination of classical estimates at three nearby points. We develop an analogous variance reduction method for SUR kernel estimators in clustered/longitudinal models and perform simulation studies which demonstrate the efficacy of our variance reduction method in finite sample settings. [Copyright &y& Elsevier]

Abstract: First, we propose a new method for estimating the conditional variance in heteroscedasticity regression models. For heavy tailed innovations, this method is in general more efficient than either of the local linear and local likelihood estimators. Secondly, we apply a variance reduction technique to improve the inference for the conditional variance. The proposed methods are investigated through their asymptotic distributions and numerical performances. [Copyright &y& Elsevier]

Multivariate functional data are prevalent in various fields such as biology, climatology, and finance. Motivated by the World Health Data applications, in this study, we propose and examine a global test for assessing the equality of multiple mean functions in multivariate functional data. This test addresses the one-way Functional Multivariate Analysis of Variance (FMANOVA) problem, which is a fundamental issue in the analysis of multivariate functional data. While numerous analysis of variance tests have been proposed and studied for univariate functional data, only a limited number of methods have been developed for the one-way FMANOVA problem. Furthermore, our global test has the ability to handle heteroscedasticity in the unknown covariance function matrices that underlie the multivariate functional data, which is not possible with existing methods. We establish the asymptotic null distribution of the test statistic as a chi-squared-type mixture, which depends on the eigenvalues of the covariance function matrices. To approximate the null distribution, we introduce a Welch–Satterthwaite type chi-squared-approximation with consistent parameter estimation. The proposed test exhibits root- n consistency, meaning it possesses nontrivial power against a local alternative. Additionally, it offers superior computational efficiency compared to several permutation-based tests. Through simulation studies and applications to the World Health Data, we highlight the advantages of our global test. • One-way Functional Multivariate Analysis of Variance (FMANOVA) problem is considered. • A global test statistic is proposed which can handle heteroscedasticity in the unknown covariance function matrices. • The asymptotic null distribution of the test statistic is derived. • The test demonstrates enhanced computational efficiency when compared to various permutation-based tests. [ABSTRACT FROM AUTHOR]

Background: Adult asthma is phenotypically heterogeneous with unclear aetiology. We aimed to evaluate the potential contribution of environmental exposure and its ensuing response to asthma and its heterogeneity., Methods: Environmental risk was evaluated by assessing the records of National Health Insurance Research Database (NHIRD) and residence-based air pollution (particulate matter with diameter less than 2.5 micrometers (PM 2.5 ) and PM 2.5 -bound polycyclic aromatic hydrocarbons (PAHs)), integrating biomonitoring analysis of environmental pollutants, inflammatory markers and sphingolipid metabolites in case-control populations with mass spectrometry and ELISA. Phenotypic clustering was evaluated by t-distributed stochastic neighbor embedding (t-SNE) integrating 18 clinical and demographic variables., Findings: In the NHIRD dataset, modest increase in the relative risk with time-lag effect for emergency (N=209 837) and outpatient visits (N=638 538) was observed with increasing levels of PM 2.5 and PAHs. Biomonitoring analysis revealed a panel of metals and organic pollutants, particularly metal Ni and PAH, posing a significant risk for current asthma (ORs=1.28-3.48) and its severity, correlating with the level of oxidative stress markers, notably Nε-(hexanoyl)-lysine (r=0.108-0.311, p<0.05), but not with the accumulated levels of PM 2.5 exposure. Further, levels of circulating sphingosine-1-phosphate and ceramide-1-phosphate were found to discriminate asthma (p<0.001 and p<0.05, respectively), correlating with the levels of PAH (r=0.196, p<0.01) and metal exposure (r=0.202-0.323, p<0.05), respectively, and both correlating with circulating inflammatory markers (r=0.186-0.427, p<0.01). Analysis of six phenotypic clusters and those cases with comorbid type 2 diabetes mellitus (T2DM) revealed cluster-selective environmental risks and biosignatures., Interpretation: These results suggest the potential contribution of environmental factors from multiple sources, their ensuing oxidative stress and sphingolipid remodeling to adult asthma and its phenotypic heterogeneity., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)