63 research outputs found
On Stein's Identity and Near-Optimal Estimation in High-dimensional Index Models
We consider estimating the parametric components of semi-parametric multiple
index models in a high-dimensional and non-Gaussian setting. Such models form a
rich class of non-linear models with applications to signal processing, machine
learning and statistics. Our estimators leverage the score function based first
and second-order Stein's identities and do not require the covariates to
satisfy Gaussian or elliptical symmetry assumptions common in the literature.
Moreover, to handle score functions and responses that are heavy-tailed, our
estimators are constructed via carefully thresholding their empirical
counterparts. We show that our estimator achieves near-optimal statistical rate
of convergence in several settings. We supplement our theoretical results via
simulation experiments that confirm the theory
Baseline participant characteristics by hs-CRP category.
<p>Baseline participant characteristics by hs-CRP category.</p
High-normal levels of hs-CRP predict the development of non-alcoholic fatty liver in healthy men
<div><p>We performed a follow-up study to address whether high sensitivity C-reactive protein (hs-CRP) levels within the normal range can predict the development of non-alcoholic fatty liver disease (NAFLD) in healthy male subjects. Among15347 male workers between 30 and 59 years old who received annual health check-ups in 2002, a NAFLD-free cohort of 4,138 was followed through December 2009. Alcohol consumption was assessed with a questionnaire. At each visit, abdominal ultrasonography was performed to identify fatty liver disease. The COX proportional hazard model was used to evaluate the relationship between hs-CRP and incident NAFLD. During the follow-up period, 28.8% (1191 of 4138) of participants developed NAFLD. The hazard ratios of NAFLD were increased by hs-CRP categories within the normal range in the non-adjusted model and age-adjusted model. After adjusting for age, exercise, smoking, BMI, systolic BP, triglyceride, and fasting glucose, these incidences were only increased between the lowest and the highest hs-CRP categories. The risk for NAFLD increased as the hs-CRP level increased (p< 0.001). As the hs-CRP level increased within the healthy cohort, the risk of developing NAFLD increased. This trend remained true even if the hs-CRP level remained within the normal range. hs-CRP can be used as a predictor of NAFLD, as well as other obesity-associated diseases. Therefore, individuals with higher hs-CRP levels (even within the normal range) may require appropriate follow-up and management to prevent NAFLD development.</p></div
Distribution of hs-CRP between NAFLD and no-NAFLD.
<p>Distribution of hs-CRP between NAFLD and no-NAFLD.</p
Baseline participant characteristics between NAFLD and no NAFLD.
<p>Baseline participant characteristics between NAFLD and no NAFLD.</p
Hazard ratios (95% CI) for the incidental rate of non-alcoholic fatty liver by the baseline hs-CRP level (Cox proportional hazard models).
<p>Hazard ratios (95% CI) for the incidental rate of non-alcoholic fatty liver by the baseline hs-CRP level (Cox proportional hazard models).</p
Association between Personality Traits and Sleep Quality in Young Korean Women
<div><p>Personality is a trait that affects behavior and lifestyle, and sleep quality is an important component of a healthy life. We analyzed the association between personality traits and sleep quality in a cross-section of 1,406 young women (from 18 to 40 years of age) who were not reporting clinically meaningful depression symptoms. Surveys were carried out from December 2011 to February 2012, using the Revised NEO Personality Inventory and the Pittsburgh Sleep Quality Index (PSQI). All analyses were adjusted for demographic and behavioral variables. We considered beta weights, structure coefficients, unique effects, and common effects when evaluating the importance of sleep quality predictors in multiple linear regression models. Neuroticism was the most important contributor to PSQI global scores in the multiple regression models. By contrast, despite being strongly correlated with sleep quality, conscientiousness had a near-zero beta weight in linear regression models, because most variance was shared with other personality traits. However, conscientiousness was the most noteworthy predictor of poor sleep quality status (PSQI≥6) in logistic regression models and individuals high in conscientiousness were least likely to have poor sleep quality, which is consistent with an OR of 0.813, with conscientiousness being protective against poor sleep quality. Personality may be a factor in poor sleep quality and should be considered in sleep interventions targeting young women.</p></div
Hazard ratios<sup>a</sup> (95% CI) of incident diabetes according to relative muscle mass category in clinically relevant subgroups.
<p>Hazard ratios<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0188650#t004fn002" target="_blank"><sup>a</sup></a> (95% CI) of incident diabetes according to relative muscle mass category in clinically relevant subgroups.</p
Logistic regression models predicting poor sleep quality based on personality characteristics.
<p>N = 1,406</p><p><i>Note</i>. CI: Wald Confidence Interval</p><p><sup>a</sup> Odds Ratios (ORs) per 10 <i>T</i>-score increase in a given personality trait, controlling for age, marital status, working status, education, caffeine intake, alcohol use, smoking status, and physical activity.</p><p><sup>b</sup> Model I: logistic regression model including a single domain or facet of personality as an independent variable.</p><p><sup>c</sup> Model II: multiple logistic regression model including all five domains of personality as independent variables. At a facet level, six facets of each domain were included in the model.</p><p><sup>d</sup> Logistic regression analyses of the facet level were performed in neuroticism and conscientiousness.</p><p>*<i>p</i><.05</p><p>**<i>p</i><.01</p><p>***<i>p</i><.001</p><p>Logistic regression models predicting poor sleep quality based on personality characteristics.</p
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