Works (3)

Updated: July 5th, 2023 15:43

2015 journal article

Multilevel quantile function modeling with application to birth outcomes

BIOMETRICS, 71(2), 508–519.

By: L. Smith n, B. Reich n, A. Herring*, P. Langlois* & M. Fuentes n

author keywords: Birth weight; Discrete; Extremes; Gestational Age; Graphics processing units; Ozone; Quantile
MeSH headings : Air Pollutants / adverse effects; Bayes Theorem; Biometry; Birth Weight; Computer Simulation; Female; Gestational Age; Humans; Infant; Infant Mortality; Infant, Newborn; Infant, Premature; Infant, Small for Gestational Age; Male; Models, Statistical; Ozone / adverse effects; Pregnancy; Regression Analysis; Texas / epidemiology
TL;DR: A semi‐parametric Bayesian quantile approach that models the full quantile function rather than just a few quantile levels and shows that pooling information across gestational age and quantile level substantially reduces MSE of predictor effects. (via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being (Web of Science; OpenAlex)
Sources: Web Of Science, ORCID
Added: August 6, 2018

2015 journal article

QUANTILE REGRESSION FOR MIXED MODELS WITH AN APPLICATION TO EXAMINE BLOOD PRESSURE TRENDS IN CHINA

ANNALS OF APPLIED STATISTICS, 9(3), 1226–1246.

By: L. Smith n, M. Fuentes n, P. Gordon-Larsen n & B. Reich n

author keywords: Quantile regression; longitudinal; multivariate; Bayesian; blood pressure
TL;DR: The association between high blood pressure and living in an urban area has evolved from positive to negative, with the strongest changes occurring in the upper tail, which is the first quantile function approach that simultaneously models multivariate conditional response. (via Semantic Scholar)
UN Sustainable Development Goal Categories
11. Sustainable Cities and Communities (OpenAlex)
Sources: Web Of Science, ORCID
Added: August 6, 2018

2013 journal article

Bayesian Quantile Regression for Censored Data

BIOMETRICS, 69(3), 651–660.

By: B. Reich n & L. Smith n

author keywords: Accelerated failure time model; Markov chain Monte Carlo; Quantile regression; Survival data
MeSH headings : Bayes Theorem; Biometry / methods; Computer Simulation; Humans; Markov Chains; Models, Statistical; Monte Carlo Method; Regression Analysis; Survival Analysis; Uncertainty
TL;DR: This paper takes a semiparametric approach to quantile regression, representing the quantile process as a linear combination of basis functions, and finds that the Bayesian model often gives smaller measures of uncertainty than its competitors, and thus identifies more significant effects. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: Web Of Science, ORCID
Added: August 6, 2018

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