SMOOTH DENSITY SPATIAL QUANTILE REGRESSION.
Ontology highlight
ABSTRACT:
SUBMITTER: Brantley H
PROVIDER: S-EPMC8725653 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:This article develops a novel spatial quantile function-on-scalar regression model, which studies the conditional spatial distribution of a high-dimensional functional response given scalar predictors. With the strength of both quantile regression and copula modeling, we are able to explicitly characterize the conditional distribution of the functional or image response on the whole spatial domain. Our method provides a comprehensive understanding of the effect of scalar covariates on functional responses across different quantile levels and also gives a practical way to generate new images for given covariate values. Theoretically, we establish the minimax rates of convergence for estimating coefficient functions under both fixed and random designs. We further develop an efficient primal-dual algorithm to handle high-dimensional image data. Simulations and real data analysis are conducted to examine the finite-sample performance.
| S-EPMC10065478 | biostudies-literature
Project description:Between-sample normalization is a critical step in genomic data analysis to remove systematic bias and unwanted technical variation in high-throughput data. Global normalization methods are based on the assumption that observed variability in global properties is due to technical reasons and are unrelated to the biology of interest. For example, some methods correct for differences in sequencing read counts by scaling features to have similar median values across samples, but these fail to reduce other forms of unwanted technical variation. Methods such as quantile normalization transform the statistical distributions across samples to be the same and assume global differences in the distribution are induced by only technical variation. However, it remains unclear how to proceed with normalization if these assumptions are violated, for example, if there are global differences in the statistical distributions between biological conditions or groups, and external information, such as negative or control features, is not available. Here, we introduce a generalization of quantile normalization, referred to as smooth quantile normalization (qsmooth), which is based on the assumption that the statistical distribution of each sample should be the same (or have the same distributional shape) within biological groups or conditions, but allowing that they may differ between groups. We illustrate the advantages of our method on several high-throughput datasets with global differences in distributions corresponding to different biological conditions. We also perform a Monte Carlo simulation study to illustrate the bias-variance tradeoff and root mean squared error of qsmooth compared to other global normalization methods. A software implementation is available from https://github.com/stephaniehicks/qsmooth.
| S-EPMC5862355 | biostudies-other
Project description:An important goal of censored quantile regression is to provide reliable predictions of survival quantiles, which are often reported in practice to offer robust and comprehensive biomedical summaries. However, formal methods for evaluating and comparing working quantile regression models in terms of their performance in predicting survival quantiles have been lacking, especially when the working models are subject to model mis-specification. In this article, we proposes a sensible and rigorous framework to fill in this gap. We introduce and justify a predictive performance measure defined based on the check loss function. We derive estimators of the proposed predictive performance measure and study their distributional properties and the corresponding inference procedures. More importantly, we develop model comparison procedures that enable thorough evaluations of model predictive performance among nested or non-nested models. Our proposals properly accommodate random censoring to the survival outcome and the realistic complication of model mis-specification, and thus are generally applicable. Extensive simulations and a real data example demonstrate satisfactory performances of the proposed methods in real life settings.
| S-EPMC5462897 | biostudies-literature
Project description:This paper considers the quantile regression model with individual fixed effects for spatial panel data. Efficient minimum distance quantile regression estimators based on instrumental variable (IV) method are proposed for parameter estimation. The proposed estimator is computational fast compared with the IV-FEQR estimator proposed by Dai et al. (2020). Asymptotic properties of the proposed estimators are also established. Simulations are conducted to study the performance of the proposed method. Finally, we illustrate our methodologies using a cigarettes demand data set.
| S-EPMC8670681 | biostudies-literature
Project description:BACKGROUND:Child low and high birth weight are important public health problems. Many studies have looked at factors of low and high birth weight using mean regression. This study aimed at using quantile regression to find out determinants of low and high birth weight. METHODS:Spatial quantile regression models at 0.05 and 0.95 percentiles of birth weight were fitted to 13,087 children birth weight in kilograms using Malawi demographic health survey data of 2010 study. Full Bayesian method by integrated nested Laplace approximations (INLA) was used to estimate the model. Second order random walk priors were assigned for mother age and antenatal visits for pregnancy while Gaussian markov random field prior was used for district of the child. RESULTS:Residual spatial patterns reveal areas in the southern region promoting high birth weight while areas in the central and northern region promote low birth weight. Most fixed effects findings are consistent with the literature. Richest family, normal mother body mass index (BMI), mother over weight (BMI > 25 kg/m2), birth order 2-3, mother secondary education and height (≥150 cm) negate low birth weight while weight 45-70 kg promote low birth weight. Birth order category 6+, mother height (≥150 cm) and poor wealth quintile, promote high birth weight, while richer and richest wealth quintiles and education categories: primary, secondary, and higher, and mother overweight (BMI > 25 kg/m2) reduce high birth weight. Antenatal visits for pregnancy reduce both low and high birth weight. CONCLUSION:Strategies to reduce low and high birth weight should simultaneously address mother education, weight gain during pregnancy and poverty while targeting areas increasing low and high birth weight.
| S-EPMC6884851 | biostudies-literature
Project description:A time-specific log-linear regression method on quantile residual lifetime is proposed. Under the proposed regression model, any quantile of a time-to-event distribution among survivors beyond a certain time point is associated with selected covariates under right censoring. Consistency and asymptotic normality of the regression estimator are established. An asymptotic test statistic is proposed to evaluate the covariate effects on the quantile residual lifetimes at a specific time point. Evaluation of the test statistic does not require estimation of the variance-covariance matrix of the regression estimators, which involves the probability density function of the survival distribution with censoring. Simulation studies are performed to assess finite sample properties of the regression parameter estimator and test statistic. The new regression method is applied to a breast cancer data set with long-term follow-up to estimate the patients' median residual lifetimes, adjusting for important prognostic factors.
| S-EPMC3050018 | biostudies-literature
Project description:Censored quantile regression (CQR) has emerged as a useful regression tool for survival analysis. Some commonly used CQR methods can be characterized by stochastic integral-based estimating equations in a sequential manner across quantile levels. In this paper, we analyze CQR in a high dimensional setting where the regression functions over a continuum of quantile levels are of interest. We propose a two-step penalization procedure, which accommodates stochastic integral based estimating equations and address the challenges due to the recursive nature of the procedure. We establish the uniform convergence rates for the proposed estimators, and investigate the properties on weak convergence and variable selection. We conduct numerical studies to confirm our theoretical findings and illustrate the practical utility of our proposals.
| S-EPMC6193274 | biostudies-literature
Project description:We develop scalar-on-image regression models when images are registered multidimensional manifolds. We propose a fast and scalable Bayes inferential procedure to estimate the image coefficient. The central idea is the combination of an Ising prior distribution, which controls a latent binary indicator map, and an intrinsic Gaussian Markov random field, which controls the smoothness of the nonzero coefficients. The model is fit using a single-site Gibbs sampler, which allows fitting within minutes for hundreds of subjects with predictor images containing thousands of locations. The code is simple and is provided in less than one page in the Appendix. We apply this method to a neuroimaging study where cognitive outcomes are regressed on measures of white matter microstructure at every voxel of the corpus callosum for hundreds of subjects.
| S-EPMC3979628 | biostudies-literature
Project description:Double censoring often occurs in registry studies when left censoring is present in addition to right censoring. In this work, we propose a new analysis strategy for such doubly censored data by adopting a quantile regression model. We develop computationally simple estimation and inference procedures by appropriately using the embedded martingale structure. Asymptotic properties, including the uniform consistency and weak convergence, are established for the resulting estimators. Moreover, we propose conditional inference to address the special identifiability issues attached to the double censoring setting. We further show that the proposed method can be readily adapted to handle left truncation. Simulation studies demonstrate good finite-sample performance of the new inferential procedures. The practical utility of our method is illustrated by an analysis of the onset of the most commonly investigated respiratory infection, Pseudomonas aeruginosa, in children with cystic fibrosis through the use of the U.S. Cystic Fibrosis Registry.
| S-EPMC3312995 | biostudies-literature
Project description:Per- and poly-fluoroalkyl substances (PFAS) are chemicals, detected in 95% of Americans, that induce osteotoxicity and modulate hormones, thereby influencing bone health. Previous studies found associations between individual PFAS and bone mineral density in adults but did not analyze their combined effects.ObjectiveTo extend weighted quantile sum (WQS) regression to a Bayesian framework (Bayesian extension of the WQS regression [BWQS]) and determine the association between a mixture of serum PFAS and mineral density in lumbar spine, total, and neck femur in 499 adults from the 2013 to 2014 National Health and Nutrition Examination Survey (NHANES).MethodsWe used BWQS to assess the combined association of eight PFAS, as a mixture, with bone mineral density in adults. As secondary analyses, we focused on vulnerable populations (men over 50 years and postmenopausal women). Analyses were adjusted for sociodemographic factors. Sensitivity analyses included bone mineral density associations with individual compounds and results from WQS regressions.ResultsThe mean age was 55 years old (SD = 1) with average spine, total, and neck femur mineral densities of 1.01 (SD = 0.01), 0.95 (SD = 0.01), and 0.78 (SD = 0.01) gm/cm2, respectively. PFAS mixture levels showed no evidence of association with mineral density (spine: β = -0.004; 95% credible interval [CrI] = -0.04, 0.04; total femur: β = 0.002; 95% CrI = -0.04, 0.05; femur neck: β = 0.005; 95%CrI = -0.03, 0.04) in the overall population. Results were also null in vulnerable populations. Findings were consistent across sensitivity analyses.ConclusionsWe introduced a Bayesian extension of WQS and found no evidence of the association between PFAS mixture and bone mineral density.
| S-EPMC7289141 | biostudies-literature