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Inference in generalized additive mixed modelsby using smoothing splines

X. Lin, D. Zhang
1999 Journal of The Royal Statistical Society Series B-statistical Methodology  
This class of models allows¯exible functional dependence of an outcome variable on covariates by using nonparametric regression, while accounting for correlation between observations by using random effects  ...  We estimate nonparametric functions by using smoothing splines and jointly estimate smoothing parameters and variance components by using marginal quasi-likelihood.  ...  Acknowledgements This work was supported in part by a grant from the US National Cancer Institute and grants from the University of Michigan.  ... 
doi:10.1111/1467-9868.00183 fatcat:nqrqdohvlreybipme7s7zjkecy

Semiparametric Regression of Multidimensional Genetic Pathway Data: Least-Squares Kernel Machines and Linear Mixed Models

Dawei Liu, Xihong Lin, Debashis Ghosh
2007 Biometrics  
Both the regression coefficients of the covariate effects and the LSKM estimator of the genetic pathway effect can be obtained using the best linear unbiased predictor in the corresponding linear mixed  ...  This unified framework allows a flexible function for the joint effect of multiple genes within a pathway by specifying a kernel function and allows for the possibility that each gene expression effect  ...  DG's research was supported by a grant from the National Institute of Health (GM072007). We thank the associate editor and three reviewers for their helpful comments that have improved the article.  ... 
doi:10.1111/j.1541-0420.2007.00799.x pmid:18078480 pmcid:PMC2665800 fatcat:p3wjf34fazb4nn4p2e5pt7lcfu

Semiparametric Regression for Periodic Longitudinal Hormone Data from Multiple Menstrual Cycles

Daowen Zhang, Xihong Lin, MaryFran Sowers
2000 Biometrics  
We develop a scaled chi-squared test for the equality of two nonparametric time functions.  ...  Parametric fixed effects are used to model the covariate effects and a periodic nonparametric smooth function is used to model the time effect.  ...  ACKNOWLEDGEMENTS This work was supported in part by National Cancer Institute grant R29 CA76404 and National Institute of Health grant R01 NIAMS 40888.  ... 
doi:10.1111/j.0006-341x.2000.00031.x pmid:10783774 fatcat:cz32upewb5capir4niu3qy7whq

Bayesian surface regression versus spatial spectral nonparametric curve regression [article]

M.D. Ruiz-Medina, D. Miranda
2021 arXiv   pre-print
Cross-validation procedures are implemented to test the performance of the two functional regression approaches.  ...  Specifically, a bayesian framework is adopted in the estimation of the pure point spectrum of the temporal autocorrelation operator, characterizing the second-order structure of a surface sequence.  ...  Step 2 Bayesian componentwise estimation of the functional entries of the inverse C −1 of the covariance matrix operator C (see equations ( 16 )-( 17 )), in terms of the truncated empirical autocovariance  ... 
arXiv:2111.00302v1 fatcat:24crx4rvnrbqrcvpwhssjgwdcm

Fully Nonparametric Regression for Bounded Data Using Dependent Bernstein Polynomials

Andrés F. Barrientos, Alejandro Jara, Fernando A. Quintana
2017 Journal of the American Statistical Association  
A. (2015) "Bayesian Nonparametric Estimation of Test Equating Functions with Covariates". Computational Statistics & Data Analysis, 89, 222-244. 72. González, J. and Barrientos, A.  ...  A. (2015) "A Dependent Bayesian Nonparametric Model for Test Equating", in Quantitative Psychology Research. Presentations from the 78th Annual Psychometric Society Meeting.  ... 
doi:10.1080/01621459.2016.1180987 fatcat:dhyfwuyb5fgbddwcee5d7gngui

ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference With and Without Covariates

María,Xosé Rodríguez-Álvarez, Vanda Inácio
2021 The R Journal  
From the estimated ROC curve (pooled, covariate-specific, or covariate-adjusted), several summary measures of discriminatory accuracy, such as the (partial) area under the ROC curve and the Youden index  ...  and nonparametric perspectives and within Bayesian and frequentist paradigms.  ...  Acknowledgements We acknowledge the reviewer for their constructive comments that led to an improved version of the article.  ... 
doi:10.32614/rj-2021-066 fatcat:vpig3kqpxbeavjprdyqyia4egq

Frontiers in Nonparametric Statistics

Peter Bühlmann, Tony Cai, Axel Munk, Bin Yu
2012 Oberwolfach Reports  
A particular focus was on high dimensional statistics, semiparametrics, adaptation, nonparametric bayesian statistics, shape constraint estimation and statistical inverse problems.  ...  The goal of this workshop was to discuss recent developments of nonparametric statistical inference.  ...  estimation and inference, statistical inverse problems and nonparametric Bayesian theory.  ... 
doi:10.4171/owr/2012/14 fatcat:ndg63i4x4vaqnpjitstzxrsh6a

Bayesian Nonparametric Conditional Copula Estimation of Twin Data [article]

Luciana Dalla Valle, Fabrizio Leisen, Luca Rossini
2017 arXiv   pre-print
We propose a flexible Bayesian nonparametric approach for the estimation of conditional copulas, which can model any conditional copula density.  ...  Our methodology is based on conditional copulas, which allow us to model the effect of a covariate driving the strength of dependence between the main variables.  ...  Acknowledgements The authors are thankful to the Associate Editor and the anonymous reviewers for their useful comments which significantly improved the quality of the paper.  ... 
arXiv:1603.03484v4 fatcat:f6sem7f4vzhjlpdws4mu3fg4my

GP CaKe: Effective brain connectivity with causal kernels [article]

Luca Ambrogioni, Max Hinne, Marcel van Gerven, Eric Maris
2017 arXiv   pre-print
We construct a novel class of causal covariance functions that enforce the desired properties of the causal kernels, an approach which we call GP CaKe.  ...  The approach combines the tractability and flexibility of autoregressive modeling with the biophysical interpretability of dynamic causal modeling.  ...  The Bayesian model We can frame the estimation of the effective connectivity between neuronal populations as a nonparametric Bayesian regression problem.  ... 
arXiv:1705.05603v1 fatcat:6k77dlbecfhghackq4uqe4ucru

BFDA: A MATLAB Toolbox for Bayesian Functional Data Analysis

Jingjing Yang, Peng Ren
2019 Journal of Statistical Software  
The advantages of BFDA include: (1) simultaneously smooths multiple functional data samples and estimates the mean-covariance functions in a nonparametric way; (2) flexibly deals with sparse and high-dimensional  ...  An option of approximating the Bayesian inference process using cubic B-spline basis functions is integrated in BFDA, which allows for efficiently dealing with high-dimensional functional data.  ...  Examples are provided in the supplementary file about using the fdaM package with functional data smoothed by BFDA. Details about the inputs and outputs of BFDA are provided in Appendix A.  ... 
doi:10.18637/jss.v089.i02 fatcat:wjoctmyh35dhhnpfk2srp72gm4

BFDA: A Matlab Toolbox for Bayesian Functional Data Analysis [article]

Jingjing Yang, Peng Ren
2017 arXiv   pre-print
The advantages of BFDA include: (1) Simultaneously smooths multiple functional data and estimates the mean-covariance functions in a nonparametric way; (2) flexibly deals with sparse and high-dimensional  ...  An option of approximating the Bayesian inference process using cubic B-spline basis functions is integrated in BFDA, which allows for efficiently dealing with high-dimensional functional data.  ...  In addition, we show that the Bayesian nonparametric covariance estimates in Figure 2 (a, b) are clearly smoother than the sample covariance estimate by using the raw common-grid data in Figure 2 (c)  ... 
arXiv:1604.05224v2 fatcat:6gwwce35vvhfbevradu56wtkna

Bayesian Nonparametric Conditional Copula Estimation of Twin Data

Luciana Dalla Valle, Fabrizio Leisen, Luca Rossini
2017 Social Science Research Network  
We propose a flexible Bayesian nonparametric approach for the estimation of conditional copulas, which can model any conditional copula density.  ...  Our methodology is based on conditional copulas, which allow us to model the effect of a covariate driving the strength of dependence between the main variables.  ...  Acknowledgements The authors are thankful to the Associate Editor and the anonymous reviewers for their useful comments which significantly improved the quality of the paper.  ... 
doi:10.2139/ssrn.2752355 fatcat:cno4tn7xnzc5fftdobkneasnau

Statistical Evaluation of Medical Tests [article]

Vanda Inacio, Maria Xose Rodriguez-Alvarez, Pilar Gayoso-Diz
2020 arXiv   pre-print
Special focus is placed on the receiver operating characteristic (ROC) curve and its estimation, with the topic of covariate adjustment receiving a great deal of attention.  ...  Measures of diagnostic performance for binary tests, such as sensitivity, specificity, and predictive values, are introduced, and extensions to the case of continuous-outcome tests are detailed.  ...  To finish this section, we turn our attention to Bayesian approaches and start with the nonparametric method of Erkanli et al. (2006) , which models the distribution of test outcomes in each group via  ... 
arXiv:2007.07687v1 fatcat:plmlic6whjfh3fguywjok2cpja

Population pharmacokinetics of mycophenolic acid in pediatric renal transplant patients using parametric and nonparametric approaches

A. Prémaud, L.T. Weber, B. Tönshoff, V.W. Armstrong, M. Oellerich, S. Urien, P. Marquet, A. Rousseau
2011 Pharmacological Research  
Bayesian forecasting of mycophenolic acid exposure using the NPAG population pharmacokinetic parameters as priors yielded a better predictive performance, with a significantly smaller bias than with the  ...  The aim of this study was to compare population pharmacokinetic modeling of MPA in pediatric renal transplant recipients given mycophenolate mofetil, the ester prodrug of MPA, using parametric and nonparametric  ...  their advice and their valuable methodological assistance for nonparametric analyses.  ... 
doi:10.1016/j.phrs.2010.10.017 pmid:21056671 fatcat:htzyvr4cvnconh7c6ks3ikttpa

Locally Adaptive Bayes Nonparametric Regression via Nested Gaussian Processes

Bin Zhu, David B. Dunson
2013 Journal of the American Statistical Association  
normal distribution with mean vector 0 and covariance matrix .  ...  Keywords Bayesian nonparametric regression; Nested Gaussian processes; Nested smoothing spline; Penalized sum-of-square; Reproducing kernel Hilbert space; Stochastic differential equations multivariate  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Environmental Health Sciences, the National Institutes of Health  ... 
doi:10.1080/01621459.2013.838568 pmid:25328260 pmcid:PMC4196220 fatcat:pd5q32t7cbe7de2wp5xxacyg5y
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